2047 Data Engineering Jobs - Page 39

Must have skills : - Bachelor's/master's in engineering, Computer Science, or equivalent experience - 5-6 years of experience in the IT industry, experience in Data space is preferred. - Working experience in GCP-BQ. - Good knowledge in Teradata or Oracle. - Experience in Data modelling. - Advanced scripting experience - Python, Shell, etc. - Strong analytical skills including the ability to define problems, collect data, establish facts, and draw valid conclusions - Knowledge of Scheduling Tools (preferably Airflow, UC4) is a plus - Working knowledge on any ETL tool (i.e, Informatica) is a plus. - Excellent written and oral communication skills - Familiarity with data movement techniques and best practices to handle large volumes of data - Strong communication skills and willingness to take initiative to contribute beyond core-responsibilities Responsibilities : In this role, the individual will be part of the Credit data engineering team within Credit Platform organization and have the following responsibilities : - Design and implement an integrated credit data platform that is extremely high-volume, fault-tolerant, scalable backend systems that process and manage petabytes of customer data. - Should adopt long term/strategic thought process during the entire project life cycle. - Participating and collaborating with cross functional teams in the organization to understand the business requirements and to deliver solutions that can scale.

PositionSenior Data Engineer - Airflow, PLSQL Experience5+ Years LocationBangalore/Hyderabad/Pune Seeking a Senior Data Engineer with strong expertise in Apache Airflow and Oracle PL/SQL, along with working experience in Snowflake and Agile methodologies. The ideal candidate will also take up Scrum Master responsibilities and lead a data engineering scrum team to deliver robust, scalable data solutions. Key Responsibilities: Design, develop, and maintain scalable data pipelines using Apache Airflow. Write and optimize complex PL/SQL queries, procedures, and packages on Oracle databases. Collaborate with cross-functional teams to design efficient data models and integration workflows. Work with Snowflake for data warehousing and analytics use cases. Own the delivery of sprint goals, backlog grooming, and facilitation of agile ceremonies as the Scrum Master. Monitor pipeline health and troubleshoot production data issues proactively. Ensure code quality, documentation, and best practices across the team. Mentor junior data engineers and promote a culture of continuous improvement. Required Skills and Qualifications: 5+ years of experience as a Data Engineer in enterprise environments. Strong expertise in Apache Airflow for orchestrating workflows. Expert in Oracle PL/SQL - stored procedures, performance tuning, debugging. Hands-on experience with Snowflake - data modeling, SQL, optimization. Working knowledge of version control (Git) and CI/CD practices. Prior experience or certification as a Scrum Master is highly desirable. Strong analytical and problem-solving skills with attention to detail. Excellent communication and leadership skills.

We are looking for a highly skilled API & Pixel Tracking Integration Engineer to lead the development and deployment of server-side tracking and attribution solutions across multiple platforms. The ideal candidate brings deep expertise in CAPI integrations (Meta, Google, and other platforms), secure data handling using cryptographic techniques, and experience working within privacy-first environments like Azure Clean Rooms . This role requires strong hands-on experience in C# development, Azure cloud services, OCI (Oracle Cloud Infrastructure) , and marketing technology stacks including Adobe Tag Management and Pixel Management . You will work closely with engineering, analytics, and marketing teams to deliver scalable, compliant, and secure data tracking solutions that drive business insights and performance. Key Responsibilities: Design, implement, and maintain CAPI integrations across Meta, Google, and all major platforms , ensuring real-time and accurate server-side event tracking. Utilize Fabric and OCI environments as needed for data integration and marketing intelligence workflows. Develop and manage custom tracking solutions leveraging Azure Clean Rooms , ensuring user NFAs are respected and privacy-compliant logic is implemented. Implement cryptographic hashing (e.g., SHA-256) Use Azure Data Lake Gen1 & Gen2 (ADLS) , Cosmos DB , and Azure Functions to build and host scalable backend systems. Integrate with Azure Key Vaults to securely manage secrets and sensitive credentials. Design and execute data pipelines in Azure Data Factory (ADF) for processing and transforming tracking data. Lead pixel and tag management initiatives using Adobe Tag Manager , including pixel governance and QA across properties. Collaborate with security teams to ensure all data-sharing and processing complies with Azures data security standards and enterprise privacy frameworks. Monitor, troubleshoot, and optimize existing integrations using logs, diagnostics, and analytics tools. Required Skills: Strong hands-on experience with Fabric and building scalable APIs. Experience in implementing Meta CAPI , Google Enhanced Conversions , and other platform-specific server-side tracking APIs. Knowledge of Azure Clean Rooms , with experience developing custom logic and code for clean data collaborations . Proficiency with Azure Cloud technologies , especially Cosmos DB, Azure Functions, ADF, Key Vault, ADLS , and Azure security best practices . Familiarity with OCI for hybrid-cloud integration scenarios. Understanding of cryptography and secure data handling (e.g., hashing email addresses with SHA-256). Experience with Adobe Tag Management , specifically in pixel governance and lifecycle. Proven ability to collaborate across functions, especially with marketing and analytics teams. Soft Skills: Strong communication skills to explain technical concepts to non-technical stakeholders. Proven ability to collaborate across teams, especially with marketing, product, and data analytics. Adaptable and proactive in learning and applying evolving technologies and regulatory changes.

Job Description : Position: Sr.Data Engineer Experience: Minimum 7 years Location: Hyderabad Job Summary: What Youll Do Design and build efficient, reusable, and reliable data architecture leveraging technologies like Apache Flink, Spark, Beam and Redis to support large-scale, real-time, and batch data processing. Participate in architecture and system design discussions, ensuring alignment with business objectives and technology strategy, and advocating for best practices in distributed data systems. Independently perform hands-on development and coding of data applications and pipelines using Java, Scala, and Python, including unit testing and code reviews. Monitor key product and data pipeline metrics, identify root causes of anomalies, and provide actionable insights to senior management on data and business health. Maintain and optimize existing datalake infrastructure, lead migrations to lakehouse architectures, and automate deployment of data pipelines and machine learning feature engineering requests. Acquire and integrate data from primary and secondary sources, maintaining robust databases and data systems to support operational and exploratory analytics. Engage with internal stakeholders (business teams, product owners, data scientists) to define priorities, refine processes, and act as a point of contact for resolving stakeholder issues. Drive continuous improvement by establishing and promoting technical standards, enhancing productivity, monitoring, tooling, and adopting industry best practices. What Youll Bring Bachelors degree or higher in Computer Science, Engineering, or a quantitative discipline, or equivalent professional experience demonstrating exceptional ability. 7+ years of work experience in data engineering and platform engineering, with a proven track record in designing and building scalable data architectures. Extensive hands-on experience with modern data stacks, including datalake, lakehouse, streaming data (Flink, Spark), and AWS or equivalent cloud platforms. Cloud - AWS Apache Flink/Spark , Redis Database platform- Databricks. Proficiency in programming languages such as Java, Scala, and Python(Good to have) for data engineering and pipeline development. Expertise in distributed data processing and caching technologies, including Apache Flink, Spark, and Redis. Experience with workflow orchestration, automation, and DevOps tools (Kubernetes,git,Terraform, CI/CD). Ability to perform under pressure, managing competing demands and tight deadlines while maintaining high-quality deliverables. Strong passion and curiosity for data, with a commitment to data-driven decision making and continuous learning. Exceptional attention to detail and professionalism in report and dashboard creation. Excellent team player, able to collaborate across diverse functional groups and communicate complex technical concepts clearly. Outstanding verbal and written communication skills to effectively manage and articulate the health and integrity of data and systems to stakeholders. Please feel free to contact us: 9440806850 Email ID : careers@jayamsolutions.com

Responsibilities: - Contribute and build an internal product library that is focused on solving business problems related to prediction & recommendation. Research unfamiliar methodologies, techniques to fine tune existing models in the product suite and, recommend better solutions and/or technologies. Improve features of the product to include newer machine learning algorithms in the likes of product recommendation, real time predictions, fraud detection, offer personalization etc Collaborate with client teams to on-board data, build models and score predictions. Participate in building automations and standalone applications around machine learning algorithms to enable a One Click solution to getting predictions and recommendations. Analyze large datasets, perform data wrangling operations, apply statistical treatments to filter and fine tune input data, engineer new features and eventually aid the process of building machine learning models. Run test cases to tune existing models for performance, check criteria and define thresholds for success by scaling the input data to multifold. Demonstrate a basic understanding of different machine learning concepts such as Regression, Classification, Matrix Factorization, K-fold Validations and different algorithms such as Decision Trees, Random Forrest, K-means clustering. Demonstrate working knowledge and contribute to building models using deep learning techniques, ensuring robust, scalable and high-performance solutions Minimum Qualifications: Education: Master's or PhD in a quantitative discipline (Statistics, Economics, Mathematics, Computer Science) is highly preferred. Deep Learning Mastery: Extensive experience with deep learning frameworks (TensorFlow, PyTorch, or Keras) and advanced deep learning projects across various domains, with a focus on multimodal data applications. Generative AI Expertise: Proven experience with generative AI models and techniques, such as RAG, VAEs, Transformers, and applications at scale in content creation or data augmentation. Programming and Big Data: Expert-level proficiency in Python and big data/cloud technologies (Databricks and Spark) with a minimum of 4-5 years of experience . Recommender Systems and Real-time Predictions: Expertise in developing sophisticated recommender systems, including the application of real-time prediction frameworks. Machine Learning Algorithms: In-depth experience with complex algorithms such as logistic regression, random forest, XGBoost, advanced neural networks, and ensemble methods. Experienced with machine learning algorithms such as logistic regression, random forest, XG boost, KNN, SVM, neural network, linear regression, lasso regression and k-means. Desirable Qualifications: Generative AI Tools Knowledge: Proficiency with tools and platforms for generative AI (such as OpenAI, Hugging Face Transformers). Databricks and Unity Catalog: Experience leveraging Databricks and Unity Catalog for robust data management, model deployment, and tracking. Working experience in CI/CD tools such as GIT & BitBucket

job requisition idJR1027452 Overall Responsibilities: Data Pipeline Development: Design, develop, and maintain highly scalable and optimized ETL pipelines using PySpark on the Cloudera Data Platform, ensuring data integrity and accuracy. Data Ingestion: Implement and manage data ingestion processes from a variety of sources (e.g., relational databases, APIs, file systems) to the data lake or data warehouse on CDP. Data Transformation and Processing: Use PySpark to process, cleanse, and transform large datasets into meaningful formats that support analytical needs and business requirements. Performance Optimization: Conduct performance tuning of PySpark code and Cloudera components, optimizing resource utilization and reducing runtime of ETL processes. Data Quality and Validation: Implement data quality checks, monitoring, and validation routines to ensure data accuracy and reliability throughout the pipeline. Automation and Orchestration: Automate data workflows using tools like Apache Oozie, Airflow, or similar orchestration tools within the Cloudera ecosystem. Monitoring and Maintenance: Monitor pipeline performance, troubleshoot issues, and perform routine maintenance on the Cloudera Data Platform and associated data processes. Collaboration: Work closely with other data engineers, analysts, product managers, and other stakeholders to understand data requirements and support various data-driven initiatives. Documentation: Maintain thorough documentation of data engineering processes, code, and pipeline configurations. Software : Advanced proficiency in PySpark, including working with RDDs, DataFrames, and optimization techniques. Strong experience with Cloudera Data Platform (CDP) components, including Cloudera Manager, Hive, Impala, HDFS, and HBase. Knowledge of data warehousing concepts, ETL best practices, and experience with SQL-based tools (e.g., Hive, Impala). Familiarity with Hadoop, Kafka, and other distributed computing tools. Experience with Apache Oozie, Airflow, or similar orchestration frameworks. Strong scripting skills in Linux. Category-wise Technical Skills: PySpark: Advanced proficiency in PySpark, including working with RDDs, DataFrames, and optimization techniques. Cloudera Data Platform: Strong experience with Cloudera Data Platform (CDP) components, including Cloudera Manager, Hive, Impala, HDFS, and HBase. Data Warehousing: Knowledge of data warehousing concepts, ETL best practices, and experience with SQL-based tools (e.g., Hive, Impala). Big Data Technologies: Familiarity with Hadoop, Kafka, and other distributed computing tools. Orchestration and Scheduling: Experience with Apache Oozie, Airflow, or similar orchestration frameworks. Scripting and Automation: Strong scripting skills in Linux. Experience: 5-12 years of experience as a Data Engineer, with a strong focus on PySpark and the Cloudera Data Platform. Proven track record of implementing data engineering best practices. Experience in data ingestion, transformation, and optimization on the Cloudera Data Platform. Day-to-Day Activities: Design, develop, and maintain ETL pipelines using PySpark on CDP. Implement and manage data ingestion processes from various sources. Process, cleanse, and transform large datasets using PySpark. Conduct performance tuning and optimization of ETL processes. Implement data quality checks and validation routines. Automate data workflows using orchestration tools. Monitor pipeline performance and troubleshoot issues. Collaborate with team members to understand data requirements. Maintain documentation of data engineering processes and configurations. Qualifications: Bachelors or Masters degree in Computer Science, Data Engineering, Information Systems, or a related field. Relevant certifications in PySpark and Cloudera technologies are a plus. Soft Skills: Strong analytical and problem-solving skills. Excellent verbal and written communication abilities. Ability to work independently and collaboratively in a team environment. Attention to detail and commitment to data quality.

About this role: Wells Fargo is seeking a Senior Analytics Consultant with a proven track record of success preferably in the banking industry. In this role, you will: Consult, review and research moderately complex business, operational, and technical challenges that require an in-depth evaluation of variable data factors Perform moderately complex data analysis to support and drive strategic initiatives and business needs Develop a deep understanding of technical systems and business processes to extract data driven insights while identifying opportunities for engineering enhancements Lead or participate on large cross group projects Mentor less experienced staff Collaborate and consult with peers, colleagues, external contractors, and mid-level managers to resolve issues and achieve goals Leverage a solid understanding of compliance and risk management requirements for supported area. Required Qualifications: 4+ years of Analytics experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education Desired Qualifications: Strong analytical skills, proficiency in data manipulation tools like SQL, and the ability to translate complex data into actionable insights to inform strategic business decisions. Ability to collaborate closely with various stakeholders to develop and implement data strategies across the organization. Good to have certifications in Data Engineering. Domain understanding of home lending and its complete product lifecycle (Ops, Sales, Servicing, Risk) will be an added advantage. Strong understanding of business drivers and industry trends of lending products & home lending in specific. Detail oriented, results driven, and can navigate in a quickly changing and high demand environment while balancing multiple priorities. Expected to learn the business aspects quickly, multitask and prioritize between projects. Dedicated, enthusiastic, driven and performance-oriented; possesses a strong work ethic and good team player.

Exprence 5-8 Years Location - Bangalore Mode C2H Hands on data engineering experience. Hands on experience with Python programming Hands-on Experience with AWS & EKS Working knowledge of Unix, Databases, SQL Working Knowledge on Databricks Working Knowledge on Airflow and DBT

Position Summary This is the Requisition for Employee Referrals Campaign and JD is Generic. We are looking for Associates with 5+ years of experience in delivering solutions around Data Engineering, Big data analytics and data lakes, MDM, BI, and data visualization. Experienced to Integrate and standardize structured and unstructured data to enable faster insights using cloud technology. Enabling data-driven insights across the enterprise. Job Responsibilities He/she should be able to design implement and deliver complex Data Warehousing/Data Lake, Cloud Data Management, and Data Integration project assignments. Technical Design and Development – Expertise in any of the following skills. Any ETL tools (Informatica, Talend, Matillion, Data Stage), andhosting technologies like the AWS stack (Redshift, EC2) is mandatory. Any BI toolsamong Tablau, Qlik & Power BI and MSTR. Informatica MDM, Customer Data Management. Expert knowledge of SQL with the capability to performance tune complex SQL queries in tradition and distributed RDDMS systems is must. Experience across Python, PySpark and Unix/Linux Shell Scripting. Project Managementis must to have. Should be able create simple to complex project plans in Microsoft Project Plan and think in advance about potential risks and mitigation plans as per project plan. Task Management – Should be able to onboard team on the project plan and delegate tasks to accomplish milestones as per plan. Should be comfortable in discussing and prioritizing work items with team members in an onshore-offshore model. Handle Client Relationship – Manage client communication and client expectations independently or with support of reporting manager. Should be able to deliver results back to the Client as per plan. Should have excellent communication skills. Education Bachelor of Technology Master's Equivalent - Engineering Work Experience Overall, 5- 7years of relevant experience inData Warehousing, Data management projects with some experience in the Pharma domain. We are hiring for following roles across Data management tech stacks - ETL toolsamong Informatica, IICS/Snowflake,Python& Matillion and other Cloud ETL. BI toolsamong Power BI and Tableau. MDM - Informatica/ Raltio, Customer Data Management. Azure cloud Developer using Data Factory and Databricks Data Modeler-Modelling of data - understanding source data, creating data models for landing, integration. Python/PySpark -Spark/ PySpark Design, Development, and Deployment

Must have -Azure Data Factory (Mandatory). Azure Databricks, Pyspark and Python and advance SQL Azure eco-system. 1) Advanced SQL Skills. 2)Data Analysis. 3) Data Models. 4) Python (Desired). 5) Automation - Experience required : 8 to 10 years.

ABOUT THE ROLE Role Description: We are seeking a seasoned Engineering Manager (Data Engineering) to lead the end-to-end management of enterprise data assets and operational data workflows. This role is critical in ensuring the availability, quality, consistency, and timeliness of data across platforms and functions, supporting analytics, reporting, compliance, and digital transformation initiatives. You will be responsible for the day-to-day data operations, manage a team of data professionals, and drive process excellence in data intake, transformation, validation, and delivery. You will work closely with cross-functional teams including data engineering, analytics, IT, governance, and business stakeholders to align operational data capabilities with enterprise needs. Roles & Responsibilities: Lead and manage the enterprise data operations team, responsible for data ingestion, processing, validation, quality control, and publishing to various downstream systems. Define and implement standard operating procedures for data lifecycle management, ensuring accuracy, completeness, and integrity of critical data assets. Oversee and continuously improve daily operational workflows, including scheduling, monitoring, and troubleshooting data jobs across cloud and on-premise environments. Establish and track key data operations metrics (SLAs, throughput, latency, data quality, incident resolution) and drive continuous improvements. Partner with data engineering and platform teams to optimize pipelines, support new data integrations, and ensure scalability and resilience of operational data flows. Collaborate with data governance, compliance, and security teams to maintain regulatory compliance, data privacy, and access controls. Serve as the primary escalation point for data incidents and outages, ensuring rapid response and root cause analysis. Build strong relationships with business and analytics teams to understand data consumption patterns, prioritize operational needs, and align with business objectives. Drive adoption of best practices for documentation, metadata, lineage, and change management across data operations processes. Mentor and develop a high-performing team of data operations analysts and leads. Functional Skills: Must-Have Skills: Experience managing a team of data engineers in biotech/pharma domain companies. Experience in designing and maintaining data pipelines and analytics solutions that extract, transform, and load data from multiple source systems. Demonstrated hands-on experience with cloud platforms (AWS) and the ability to architect cost-effective and scalable data solutions. Experience managing data workflows in cloud environments such as AWS, Azure, or GCP. Strong problem-solving skills with the ability to analyze complex data flow issues and implement sustainable solutions. Working knowledge of SQL, Python, or scripting languages for process monitoring and automation. Experience collaborating with data engineering, analytics, IT operations, and business teams in a matrixed organization. Familiarity with data governance, metadata management, access control, and regulatory requirements (e.g., GDPR, HIPAA, SOX). Excellent leadership, communication, and stakeholder engagement skills. Well versed with full stack development & DataOps automation, logging frameworks, and pipeline orchestration tools. Strong analytical and problem-solving skills to address complex data challenges. Effective communication and interpersonal skills to collaborate with cross-functional teams. Good-to-Have Skills: Data Engineering Management experience in Biotech/Life Sciences/Pharma Experience using graph databases such as Stardog or Marklogic or Neo4J or Allegrograph, etc. Education and Professional Certifications Doctorate Degree with 3-5 + years of experience in Computer Science, IT or related field OR Masters degree with 6 - 8 + years of experience in Computer Science, IT or related field OR Bachelors degree with 10 - 12 + years of experience in Computer Science, IT or related field AWS Certified Data Engineer preferred Databricks Certificate preferred Scaled Agile SAFe certification preferred Soft Skills: Excellent analytical and troubleshooting skills Strong verbal and written communication skills Ability to work effectively with global, virtual teams High degree of initiative and self-motivation Ability to manage multiple priorities successfully Team-oriented, with a focus on achieving team goals Strong presentation and public speaking skills

Role: Data Engineer Exp: 5 to 8 Years Location: Bangalore, Noida, and Hyderabad (Hybrid, weekly 2 Days office must) NP: Immediate to 15 Days (Try to find only immediate joiners) Note: Candidate must have experience in Python, Kafka Streams, Pyspark, and Azure Databricks. Not looking for candidates who have only Exp in Pyspark and not in Python. Job Title: SSE Kafka, Python, and Azure Databricks (Healthcare Data Project) Experience: 5 to 8 years Role Overview: We are looking for a highly skilled with expertise in Kafka, Python, and Azure Databricks (preferred) to drive our healthcare data engineering projects. The ideal candidate will have deep experience in real-time data streaming, cloud-based data platforms, and large-scale data processing . This role requires strong technical leadership, problem-solving abilities, and the ability to collaborate with cross-functional teams. Key Responsibilities: Lead the design, development, and implementation of real-time data pipelines using Kafka, Python, and Azure Databricks . Architect scalable data streaming and processing solutions to support healthcare data workflows. Develop, optimize, and maintain ETL/ELT pipelines for structured and unstructured healthcare data. Ensure data integrity, security, and compliance with healthcare regulations (HIPAA, HITRUST, etc.). Collaborate with data engineers, analysts, and business stakeholders to understand requirements and translate them into technical solutions. Troubleshoot and optimize Kafka streaming applications, Python scripts, and Databricks workflows . Mentor junior engineers, conduct code reviews, and ensure best practices in data engineering . Stay updated with the latest cloud technologies, big data frameworks, and industry trends . Required Skills & Qualifications: 4+ years of experience in data engineering, with strong proficiency in Kafka and Python . Expertise in Kafka Streams, Kafka Connect, and Schema Registry for real-time data processing. Experience with Azure Databricks (or willingness to learn and adopt it quickly). Hands-on experience with cloud platforms (Azure preferred, AWS or GCP is a plus) . Proficiency in SQL, NoSQL databases, and data modeling for big data processing. Knowledge of containerization (Docker, Kubernetes) and CI/CD pipelines for data applications. Experience working with healthcare data (EHR, claims, HL7, FHIR, etc.) is a plus. Strong analytical skills, problem-solving mindset, and ability to lead complex data projects. Excellent communication and stakeholder management skills. Email: Sam@hiresquad.in

Job Overview We are looking for a Data Engineer who will be part of our Analytics Practice and will be expected to actively work in a multi-disciplinary fast paced environment. This role requires a broad range of skills and the ability to step into different roles depending on the size and scope of the project; its primary responsibility is the acquisition, transformation, loading and processing of data from a multitude of disparate data sources, including structured and unstructured data for advanced analytics and machine learning in a big data environment. Responsibilities: Engineer a modern data pipeline to collect, organize, and process data from disparate sources. Performs data management tasks, such as conduct data profiling, assess data quality, and write SQL queries to extract and integrate data Develop efficient data collection systems and sound strategies for getting quality data from different sources Consume and analyze data from the data pool to support inference, prediction and recommendation of actionable insights to support business growth. Design and develop ETL processes using tools and scripting. Troubleshoot and debug ETL processes. Performance tuning and opitimization of the ETL processes. Provide support to new of existing applications while recommending best practices and leading projects to implement new functionality. Collaborate in design reviews and code reviews to ensure standards are met. Recommend new standards for visualizations. Learn and develop new ETL techniques as required to keep up with the contemporary technologies. Reviews the solution requirements and architecture to ensure selection of appropriate technology, efficient use of resources and integration of multiple systems and technology. Support presentations to Customers and Partners Advising on new technology trends and possible adoption to maintain competitive advantage Experience Needed: 8+ years of related experience is required. A BS or Masters degree in Computer Science or related technical discipline is required ETL experience with data integration to support data marts, extracts and reporting Experience connecting to varied data sources Excellent SQL coding experience with performance optimization for data queries. Understands different data models like normalized, de-normalied, stars, and snowflake models. Worked with transactional, temporarl, time series, and structured and unstructured data. Experience on Azure Data Factory and Azure Synapse Analytics Worked in big data environments, cloud data stores, different RDBMS and OLAP solutions. Experience in cloud-based ETL development processes. Experience in deployment and maintenance of ETL Jobs. Is familiar with the principles and practices involved in development and maintenance of software solutions and architectures and in service delivery. Has strong technical background and remains evergreen with technology and industry developments. At least 3 years of demonstrated success in software engineering, release engineering, and/or configuration management. Highly skilled in scripting languages like PowerShell. Substantial experience in the implementation and exectuion fo CI/CD processes. Additional Demonstrated ability to have successfully completed multiple, complex technical projects Prior experience with application delivery using an Onshore/Offshore model Experience with business processes across multiple Master data domains in a services based company Demonstrates a rational and organized approach to the tasks undertaken and an awareness of the need to achieve quality. Demonstrates high standards of professional behavior in dealings with clients, colleagues and staff. Is able to make sound and far reaching decisions alone on major issues and to take full responsibility for them on a technical basis. Strong written communication skills. Is effective and persuasive in both written and oral communication. Experience with gathering end user requirements and writing technical documentation Time management and multitasking skills to effectively meet deadlines under time-to-market pressure

Job Area: Information Technology Group, Information Technology Group > IT Data Engineer General Summary: Developer will play an integral role in the PTEIT Machine Learning Data Engineering team. Design, develop and support data pipelines in a hybrid cloud environment to enable advanced analytics. Design, develop and support CI/CD of data pipelines and services. - 5+ years of experience with Python or equivalent programming using OOPS, Data Structures and Algorithms - Develop new services in AWS using server-less and container-based services. - 3+ years of hands-on experience with AWS Suite of services (EC2, IAM, S3, CDK, Glue, Athena, Lambda, RedShift, Snowflake, RDS) - 3+ years of expertise in scheduling data flows using Apache Airflow - 3+ years of strong data modelling (Functional, Logical and Physical) and data architecture experience in Data Lake and/or Data Warehouse - 3+ years of experience with SQL databases - 3+ years of experience with CI/CD and DevOps using Jenkins - 3+ years of experience with Event driven architecture specially on Change Data Capture - 3+ years of Experience in Apache Spark, SQL, Redshift (or) Big Query (or) Snowflake, Databricks - Deep understanding building the efficient data pipelines with data observability, data quality, schema drift, alerting and monitoring. - Good understanding of the Data Catalogs, Data Governance, Compliance, Security, Data sharing - Experience in building the reusable services across the data processing systems. - Should have the ability to work and contribute beyond defined responsibilities - Excellent communication and inter-personal skills with deep problem-solving skills. Minimum Qualifications: 3+ years of IT-related work experience with a Bachelor's degree in Computer Engineering, Computer Science, Information Systems or a related field. OR 5+ years of IT-related work experience without a Bachelors degree. 2+ years of any combination of academic or work experience with programming (e.g., Java, Python). 1+ year of any combination of academic or work experience with SQL or NoSQL Databases. 1+ year of any combination of academic or work experience with Data Structures and algorithms. 5 years of Industry experience and minimum 3 years experience in Data Engineering development with highly reputed organizations- Proficiency in Python and AWS- Excellent problem-solving skills- Deep understanding of data structures and algorithms- Proven experience in building cloud native software preferably with AWS suit of services- Proven experience in design and develop data models using RDBMS (Oracle, MySQL, etc.) Desirable - Exposure or experience in other cloud platforms (Azure and GCP) - Experience working on internals of large-scale distributed systems and databases such as Hadoop, Spark - Working experience on Data Lakehouse platforms (One House, Databricks Lakehouse) - Working experience on Data Lakehouse File Formats (Delta Lake, Iceberg, Hudi) Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.

Job Area: Engineering Group, Engineering Group > Software Engineering General Summary: As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation experiences and drives digital transformation to help create a smarter, connected future for all. As a Qualcomm Software Engineer, you will design, develop, create, modify, and validate embedded and cloud edge software, applications, and/or specialized utility programs that launch cutting-edge, world class products that meet and exceed customer needs. Qualcomm Software Engineers collaborate with systems, hardware, architecture, test engineers, and other teams to design system-level software solutions and obtain information on performance requirements and interfaces. Minimum Qualifications: Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience. OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Software Engineering or related work experience. OR PhD in Engineering, Information Systems, Computer Science, or related field. 2+ years of academic or work experience with Programming Language such as C, C++, Java, Python, etc. General Summary Preferred Qualifications 3+ years of experience as a Data Engineer or in a similar role Experience with data modeling, data warehousing, and building ETL pipelines Solid working experience with Python, AWS analytical technologies and related resources (Glue, Athena, QuickSight, SageMaker, etc.,) Experience with Big Data tools , platforms and architecture with solid working experience with SQL Experience working in a very large data warehousing environment, Distributed System. Solid understanding on various data exchange formats and complexities Industry experience in software development, data engineering, business intelligence, data science, or related field with a track record of manipulating, processing, and extracting value from large datasets Strong data visualization skills Basic understanding of Machine Learning; Prior experience in ML Engineering a plus Ability to manage on-premises data and make it inter-operate with AWS based pipelines Ability to interface with Wireless Systems/SW engineers and understand the Wireless ML domain; Prior experience in Wireless (5G) domain a plus Education Bachelor's degree in computer science, engineering, mathematics, or a related technical discipline Preferred QualificationsMasters in CS/ECE with a Data Science / ML Specialization Minimum Qualifications: Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Engineering or related work experience. OR Master's degree in Engineering, Information Systems, Computer Science, or related field OR PhD in Engineering, Information Systems, Computer Science, or related field. 3+ years of experience with Programming Language such as C, C++, Java, Python, etc. Develops, creates, and modifies general computer applications software or specialized utility programs. Analyzes user needs and develops software solutions. Designs software or customizes software for client use with the aim of optimizing operational efficiency. May analyze and design databases within an application area, working individually or coordinating database development as part of a team. Modifies existing software to correct errors, allow it to adapt to new hardware, or to improve its performance. Analyzes user needs and software requirements to determine feasibility of design within time and cost constraints. Confers with systems analysts, engineers, programmers and others to design system and to obtain information on project limitations and capabilities, performance requirements and interfaces. Stores, retrieves, and manipulates data for analysis of system capabilities and requirements. Designs, develops, and modifies software systems, using scientific analysis and mathematical models to predict and measure outcome and consequences of design. Principal Duties and Responsibilities: Completes assigned coding tasks to specifications on time without significant errors or bugs. Adapts to changes and setbacks in order to manage pressure and meet deadlines. Collaborates with others inside project team to accomplish project objectives. Communicates with project lead to provide status and information about impending obstacles. Quickly resolves complex software issues and bugs. Gathers, integrates, and interprets information specific to a module or sub-block of code from a variety of sources in order to troubleshoot issues and find solutions. Seeks others' opinions and shares own opinions with others about ways in which a problem can be addressed differently. Participates in technical conversations with tech leads/managers. Anticipates and communicates issues with project team to maintain open communication. Makes decisions based on incomplete or changing specifications and obtains adequate resources needed to complete assigned tasks. Prioritizes project deadlines and deliverables with minimal supervision. Resolves straightforward technical issues and escalates more complex technical issues to an appropriate party (e.g., project lead, colleagues). Writes readable code for large features or significant bug fixes to support collaboration with other engineers. Determines which work tasks are most important for self and junior engineers, stays focused, and deals with setbacks in a timely manner. Unit tests own code to verify the stability and functionality of a feature.

Role Purpose The purpose of this role is to design, test and maintain software programs for operating systems or applications which needs to be deployed at a client end and ensure its meet 100% quality assurance parameters Responsibilities: Design and implement the data modeling, data ingestion and data processing for various datasets Design, develop and maintain ETL Framework for various new data source Develop data ingestion using AWS Glue/ EMR, data pipeline using PySpark, Python and Databricks. Build orchestration workflow using Airflow & databricks Job workflow Develop and execute adhoc data ingestion to support business analytics. Proactively interact with vendors for any questions and report the status accordingly Explore and evaluate the tools/service to support business requirement Ability to learn to create a data-driven culture and impactful data strategies. Aptitude towards learning new technologies and solving complex problem. Qualifications: Minimum of bachelors degree. Preferably in Computer Science, Information system, Information technology. Minimum 5 years of experience on cloud platforms such as AWS, Azure, GCP. Minimum 5 year of experience in Amazon Web Services like VPC, S3, EC2, Redshift, RDS, EMR, Athena, IAM, Glue, DMS, Data pipeline & API, Lambda, etc. Minimum of 5 years of experience in ETL and data engineering using Python, AWS Glue, AWS EMR /PySpark and Airflow for orchestration. Minimum 2 years of experience in Databricks including unity catalog, data engineering Job workflow orchestration and dashboard generation based on business requirements Minimum 5 years of experience in SQL, Python, and source control such as Bitbucket, CICD for code deployment. Experience in PostgreSQL, SQL Server, MySQL & Oracle databases. Experience in MPP such as AWS Redshift, AWS EMR, Databricks SQL warehouse & compute cluster. Experience in distributed programming with Python, Unix Scripting, MPP, RDBMS databases for data integration Experience building distributed high-performance systems using Spark/PySpark, AWS Glue and developing applications for loading/streaming data into Databricks SQL warehouse & Redshift. Experience in Agile methodology Proven skills to write technical specifications for data extraction and good quality code. Experience with big data processing techniques using Sqoop, Spark, hive is additional plus Experience in data visualization tools including PowerBI, Tableau. Nice to have experience in UI using Python Flask framework anglular Mandatory Skills: Python for Insights. Experience: 5-8 Years.

Role Purpose The purpose of this role is to design, test and maintain software programs for operating systems or applications which needs to be deployed at a client end and ensure its meet 100% quality assurance parameters Do 1. Instrumental in understanding the requirements and design of the product/ software Develop software solutions by studying information needs, studying systems flow, data usage and work processes Investigating problem areas followed by the software development life cycle Facilitate root cause analysis of the system issues and problem statement Identify ideas to improve system performance and impact availability Analyze client requirements and convert requirements to feasible design Collaborate with functional teams or systems analysts who carry out the detailed investigation into software requirements Conferring with project managers to obtain information on software capabilities 2. Perform coding and ensure optimal software/ module development Determine operational feasibility by evaluating analysis, problem definition, requirements, software development and proposed software Develop and automate processes for software validation by setting up and designing test cases/scenarios/usage cases, and executing these cases Modifying software to fix errors, adapt it to new hardware, improve its performance, or upgrade interfaces. Analyzing information to recommend and plan the installation of new systems or modifications of an existing system Ensuring that code is error free or has no bugs and test failure Preparing reports on programming project specifications, activities and status Ensure all the codes are raised as per the norm defined for project / program / account with clear description and replication patterns Compile timely, comprehensive and accurate documentation and reports as requested Coordinating with the team on daily project status and progress and documenting it Providing feedback on usability and serviceability, trace the result to quality risk and report it to concerned stakeholders 3. Status Reporting and Customer Focus on an ongoing basis with respect to project and its execution Capturing all the requirements and clarifications from the client for better quality work Taking feedback on the regular basis to ensure smooth and on time delivery Participating in continuing education and training to remain current on best practices, learn new programming languages, and better assist other team members. Consulting with engineering staff to evaluate software-hardware interfaces and develop specifications and performance requirements Document and demonstrate solutions by developing documentation, flowcharts, layouts, diagrams, charts, code comments and clear code Documenting very necessary details and reports in a formal way for proper understanding of software from client proposal to implementation Ensure good quality of interaction with customer w.r.t. e-mail content, fault report tracking, voice calls, business etiquette etc Timely Response to customer requests and no instances of complaints either internally or externally Deliver No. Performance Parameter Measure 1. Continuous Integration, Deployment & Monitoring of Software 100% error free on boarding & implementation, throughput %, Adherence to the schedule/ release plan 2. Quality & CSAT On-Time Delivery, Manage software, Troubleshoot queries,Customer experience, completion of assigned certifications for skill upgradation 3. MIS & Reporting 100% on time MIS & report generation Mandatory Skills: DataBricks - Data Engineering. Experience: 3-5 Years.

ABOUT THE ROLE Role Description: As part of the cybersecurity organization, the Data Engineer is responsible for designing, building, and maintaining data infrastructure to support data-driven decision-making. This role involves working with large datasets, developing reports, executing data governance initiatives, and ensuring data is accessible, reliable, and efficiently managed. The ideal candidate has strong technical skills, experience with big data technologies, and a deep understanding of data architecture, ETL processes, and cybersecurity data frameworks. Roles & Responsibilities: Design, develop, and maintain data solutions for data generation, collection, and processing. Be a key team member that assists in design and development of the data pipeline. Create data pipelines and ensure data quality by implementing ETL processes to migrate and deploy data across systems. Schedule and manage workflows the ensure pipelines run on schedule and are monitored for failures. Collaborate with cross-functional teams to understand data requirements and design solutions that meet business needs. Develop and maintain data models, data dictionaries, and other documentation to ensure data accuracy and consistency. Implement data security and privacy measures to protect sensitive data. Leverage cloud platforms (AWS preferred) to build scalable and efficient data solutions. Collaborate and communicate effectively with product teams. Collaborate with data scientists to develop pipelines that meet dynamic business needs. Share and discuss findings with team members practicing SAFe Agile delivery model. Functional Skills: Basic Qualifications: Masters degree and 1 to 3 years of Computer Science, IT or related field experience OR Bachelors degree and 3 to 5 years of Computer Science, IT or related field experience OR Diploma and 7 to 9 years of Computer Science, IT or related field experience Preferred Qualifications: Hands on experience with data practices, technologies, and platforms, such as Databricks, Python, Gitlab, LucidChart,etc. Proficiency in data analysis tools (e.g. SQL) and experience with data sourcing tools Excellent problem-solving skills and the ability to work with large, complex datasets Understanding of data governance frameworks, tools, and best practices Knowledge of and experience with data standards (FAIR) and protection regulations and compliance requirements (e.g., GDPR, CCPA) Good-to-Have Skills: Experience with ETL tools and various Python packages related to data processing, machine learning model development Strong understanding of data modeling, data warehousing, and data integration concepts Knowledge of Python/R, Databricks, cloud data platforms Experience working in Product team's environment Experience working in an Agile environment Professional Certifications: AWS Certified Data Engineer preferred Databricks Certificate preferred Soft Skills: Initiative to explore alternate technology and approaches to solving problems Skilled in breaking down problems, documenting problem statements, and estimating efforts Excellent analytical and troubleshooting skills Strong verbal and written communication skills Ability to work effectively with global, virtual teams High degree of initiative and self-motivation Ability to manage multiple priorities successfully Team-oriented, with a focus on achieving team goals

Join our innovative team and architect the future of data solutions on Azure, Synapse, and Databricks! Senior Data Engineer (Data Architect) Additional Details: Notice Period: 30 days (maximum) Location: Remote About the Role Design and implement scalable data pipelines, data warehouses, and data lakes that drive business growth. Collaborate with stakeholders to deliver data-driven insights and shape the data landscape. Requirements 8+ years of experience in data engineering and data architecture Strong expertise in Azure services (Synapse Analytics, Databricks, Storage, Active Directory) Proven experience in designing and implementing data pipelines, data warehouses, and data lakes Strong understanding of data governance, data quality, and data security Experience with infrastructure design and implementation, including DevOps practices and tools

We are seeking an experienced Senior Data Engineer to join our team. The ideal candidate will have a strong background in data engineering and AWS infrastructure, with hands-on experience in building and maintaining data pipelines and the necessary infrastructure components. The role will involve using a mix of data engineering tools and AWS services to design, build, and optimize data architecture. Key Responsibilities: Design, develop, and maintain data pipelines using Airflow and AWS services. Implement and manage data warehousing solutions with Databricks and PostgreSQL. Automate tasks using GIT / Jenkins. Develop and optimize ETL processes, leveraging AWS services like S3, Lambda, AppFlow, and DMS. Create and maintain visual dashboards and reports using Looker. Collaborate with cross-functional teams to ensure smooth integration of infrastructure components. Ensure the scalability, reliability, and performance of data platforms. Work with Jenkins for infrastructure automation. Technical and functional areas of expertise: Working as a senior individual contributor on a data intensive project Strong experience in building high performance, resilient & secure data processing pipelines preferably using Python based stack. Extensive experience in building data intensive applications with a deep understanding of querying and modeling with relational databases preferably on time-series data. Intermediate proficiency in AWS services (S3, Airflow) Proficiency in Python and PySpark Proficiency with ThoughtSpot or Databricks. Intermediate proficiency in database scripting (SQL) Basic experience with Jenkins for task automation Nice to Have : Intermediate proficiency in data analytics tools (Power BI / Tableau / Looker / ThoughSpot) Experience working with AWS Lambda, Glue, AppFlow, and other AWS transfer services. Exposure to PySpark and data automation tools like Jenkins or CircleCI. Familiarity with Terraform for infrastructure-as-code. Experience in data quality testing to ensure the accuracy and reliability of data pipelines. Proven experience working directly with U.S. client stakeholders. Ability to work independently and take the lead on tasks. Education and experience: Bachelors or masters in computer science or related fields. 5+ years of experience Stack/Skills needed: Databricks PostgreSQL Python & Pyspark AWS Stack Power BI / Tableau / Looker / ThoughSpot Familiarity with GIT and/or CI/CD tools

Hiring for Denodo Admin with 3+ years experience with below skills: Must Have: - Denodo admin logical data models, views & caching - ETL pipelines (Informatica/Talend) for EDW/data lakes, performance issues - SQL, Informatica, Talend, Big Data, Hive Required Candidate profile - Design, develop & maintain ETL pipelines using Informatica PowerCenter or Talend to extract, Hive - Optimize & troubleshoot complex SQL queries - Immediate Joiner is plus - Work from office is must

What you will do In this vital role you will be responsible to design, develop, and optimize data pipelines, data integration frameworks, and metadata-driven architectures that enable seamless data access and analytics. This role prefers deep expertise in big data processing, distributed computing, data modeling, and governance frameworks to support self-service analytics, AI-driven insights, and enterprise-wide data management. Design, develop, and maintain complex ETL/ELT data pipelines in Databricks using PySpark, Scala, and SQL to process large-scale datasets Understand the biotech/pharma or related domains & build highly efficient data pipelines to migrate and deploy complex data across systems Design and Implement solutions to enable unified data access, governance, and interoperability across hybrid cloud environments Ingest and transform structured and unstructured data from databases (PostgreSQL, MySQL, SQL Server, MongoDB etc.), APIs, logs, event streams, images, pdf, and third-party platforms Ensuring data integrity, accuracy, and consistency through rigorous quality checks and monitoring Expert in data quality, data validation and verification frameworks Innovate, explore and implement new tools and technologies to enhance efficient data processing Proactively identify and implement opportunities to automate tasks and develop reusable frameworks Work in an Agile and Scaled Agile (SAFe) environment, collaborating with cross-functional teams, product owners, and Scrum Masters to deliver incremental value Use JIRA, Confluence, and Agile DevOps tools to manage sprints, backlogs, and user stories Support continuous improvement, test automation, and DevOps practices in the data engineering lifecycle Collaborate and communicate effectively with the product teams, with cross-functional teams to understand business requirements and translate them into technical solutions What we expect of you We are all different, yet we all use our unique contributions to serve patients. We are looking for highly motivated expert Data Engineer who can own the design & development of complex data pipelines, solutions and frameworks. Basic Qualifications: Masters degree and 1 to 3 years of Computer Science, IT or related field experience OR Bachelors degree and 3 to 5 years of Computer Science, IT or related field experience OR Diploma and 7 to 9 years of Computer Science, IT or related field experience Hands-on experience in data engineering technologies such as Databricks, PySpark, SparkSQL Apache Spark, AWS, Python, SQL, and Scaled Agile methodologies Proficiency in workflow orchestration, performance tuning on big data processing Strong understanding of AWS services Ability to quickly learn, adapt and apply new technologies Strong problem-solving and analytical skills Excellent communication and teamwork skills Experience with Scaled Agile Framework (SAFe), Agile delivery practices, and DevOps practices Preferred Qualifications: AWS Certified Data Engineer preferred Databricks Certificate preferred Scaled Agile SAFe certification preferred Data Engineering experience in Biotechnology or pharma industry Experience in writing APIs to make the data available to the consumers Experienced with SQL/NOSQL database, vector database for large language models Experienced with data modeling and performance tuning for both OLAP and OLTP databases Experienced with software engineering best-practices, including but not limited to version control (Git, Subversion, etc.), CI/CD (Jenkins, Maven etc.), automated unit testing, and DevOps Soft Skills: Excellent analytical and troubleshooting skills. Strong verbal and written communication skills Ability to work effectively with global, virtual teams High degree of initiative and self-motivation. Ability to manage multiple priorities successfully. Team-oriented, with a focus on achieving team goals. Ability to learn quickly, be organized and detail oriented. Strong presentation and public speaking skills.

ABOUT THE ROLE Role Description: We are seeking a Senior Data Engineering Manager with a strong background in Regulatory or Integrated Product Teams within the Biotech or Pharmaceutical domain. This role will lead the end-to-end data strategy and execution for regulatory product submissions, lifecycle management, and compliance reporting, ensuring timely and accurate delivery of regulatory data assets across global markets. You will be embedded in a cross-functional Regulatory Integrated Product Team (IPT) and serve as the data and technology lead, driving integration between scientific, regulatory, and engineering functions to support submission-ready data and regulatory intelligence solutions. Roles & Responsibilities: Functional Skills: Lead the engineering strategy and implementation for end-to-end regulatory operations, including data ingestion, transformation, integration, and delivery across regulatory systems. Serve as the data engineering SME in the Integrated Product Team (IPT) to support regulatory submissions, agency interactions, and lifecycle updates. Collaborate with global regulatory affairs, clinical, CMC, quality, safety, and IT teams to gather submission data requirements and translate them into data engineering solutions. Manage and oversee the development of data pipelines, data models, and metadata frameworks that support submission data standards (e.g., eCTD, IDMP, SPL, xEVMPD). Enable integration and reporting across regulatory information management systems (RIMS), EDMS, clinical trial systems, and lab data platforms. Implement data governance, lineage, validation, and audit trails for regulatory data workflows, ensuring GxP and regulatory compliance. Guide the development of automation solutions, dashboards, and analytics that improve visibility into submission timelines, data quality, and regulatory KPIs. Ensure interoperability between regulatory data platforms and enterprise data lakes or lakehouses for cross-functional reporting and insights. Collaborate with IT, data governance, and enterprise architecture teams to ensure alignment with overall data strategy and compliance frameworks. Drive innovation by evaluating emerging technologies in data engineering, graph data, knowledge management, and AI for regulatory intelligence. Lead, mentor, and coach a small team of data engineers and analysts, fostering a culture of excellence, innovation, and delivery. Drive Agile and Scaled Agile (SAFe) methodologies, managing sprint backlogs, prioritization, and iterative improvements to enhance team velocity and project delivery. Stay up-to-date with emerging data technologies, industry trends, and best practices, ensuring the organization leverages the latest innovations in data engineering and architecture. Must-Have Skills: 812 years of experience in data engineering or data architecture, with 3+ years in a senior or managerial capacity, preferably within the biotech or pharmaceutical industry. Proven experience supporting regulatory functions, including submissions, tracking, and reporting for FDA, EMA, and other global authorities. Experience with ETL/ELT tools, data pipelines, and cloud-based data platforms (e.g., Databricks, AWS, Azure, or GCP). Familiarity with regulatory standards and data models such as eCTD, IDMP, HL7, CDISC, and xEVMPD. Deep understanding of GxP data compliance, audit requirements, and regulatory submission processes. Experience with tools like Power BI, Tableau, or Qlik for regulatory dashboarding and visualization is a plus. Strong project management, stakeholder communication, and leadership skills, especially in matrixed, cross-functional environments. Ability to translate technical capabilities into regulatory and business outcomes.Prepare team members for stakeholder discussions by helping assess data costs, access requirements, dependencies, and availability for business scenarios. Good-to-Have Skills: Prior experience working on integrated product teams or regulatory transformation programs. Knowledge of Regulatory Information Management Systems (RIMS), Veeva Vault RIM, or Master Data Management (MDM) in regulated environments. Familiarity with Agile/SAFe methodologies and DevOps/DataOps best practices. Education and Professional Certifications 12 to 15 years of experience in Computer Science, IT or related field Scaled Agile SAFe certification preferred Project Management certifications preferred Soft Skills: Excellent analytical and troubleshooting skills Strong verbal and written communication skills Ability to work effectively with global, virtual teams High degree of initiative and self-motivation Ability to manage multiple priorities successfully Team-oriented, with a focus on achieving team goals Strong presentation and public speaking skills

What you will do The R&D Precision Medicine team is responsible for Data Standardization, Data Searching, Cohort Building, and Knowledge Management tools that provide the Amgen scientific community with access to Amgens wealth of human datasets, projects and study histories, and knowledge over various scientific findings. These data include clinical data, omics, and images. These solutions are pivotal tools in Amgens goal to accelerate the speed of discovery, and speed to market of advanced precision medications. The Data Engineer will be responsible for full stack development of enterprise analytics and data mastering solutions leveraging Databricks and Power BI. This role requires expertise in both data architecture and analytics, with the ability to create scalable, reliable, and high-performing enterprise solutions that support research cohort-building and advanced AI pipelines. The ideal candidate will have experience creating and surfacing large unified repositories of human data, based on integrations from multiple repositories and solutions, and be exceptionally skilled with data analysis and profiling. You will collaborate closely with partners, product team members, and related IT teams, to design and implement data models, integrate data from various sources, and ensure best practices for data governance and security. The ideal candidate will have a solid background in data warehousing, ETL, Databricks, Power BI, and enterprise data mastering. Roles & Responsibilities Design and build scalable enterprise analytics solutions using Databricks, Power BI, and other modern data management tools. Leverage data virtualization, ETL, and semantic layers to balance need for unification, performance, and data transformation with goal to reduce data proliferation Break down features into work that aligns with the architectural direction runway Participate hands-on in pilots and proofs-of-concept for new patterns Create robust documentation from data analysis and profiling, and proposed designs and data logic Develop advanced sql queries to profile, and unify data Develop data processing code in sql, along with semantic views to prepare data for reporting Develop PowerBI Models and reporting packages Design robust data models, and processing layers, that support both analytical processing and operational reporting needs. Design and develop solutions based on best practices for data governance, security, and compliance within Databricks and Power BI environments. Ensure the integration of data systems with other enterprise applications, creating seamless data flows across platforms. Develop and maintain Power BI solutions, ensuring data models and reports are optimized for performance and scalability. Collaborate with partners to define data requirements, functional specifications, and project goals. Continuously evaluate and adopt new technologies and methodologies to enhance the architecture and performance of data solutions. What we expect of you We are all different, yet we all use our unique contributions to serve patients. The professional we seek is someone with these qualifications. Basic Qualifications: Masters degree with 1 to 3 years of experience in Data Engineering OR Bachelors degree with 1 to 3 years of experience in Data Engineering Must-Have Skills: Minimum of 1 year of hands-on experience with BI solutions (Preferrable Power BI or Business Objects) including report development, dashboard creation, and optimization. Minimum of 1 year of hands-on experience building Change-data-capture (CDC) ETL pipelines, data warehouse design and build, and enterprise-level data management. Hands-on experience with Databricks, including data engineering, optimization, and analytics workloads. Experience using cloud platforms (AWS), data lakes, and data warehouses. Working knowledge of ETL processes, data pipelines, and integration technologies. Good communication and collaboration skills to work with cross-functional teams and senior leadership. Ability to assess business needs and design solutions that align with organizational goals. Exceptional hands-on capabilities with data profiling and data anlysis Good-to-Have Skills: Experience with human data, ideally human healthcare data Familiarity with laboratory testing, patient data from clinical care, HL7, FHIR, and/or clinical trial data management Professional Certifications: ITIL Foundation or other relevant certifications (preferred) SAFe Agile Practitioner (6.0) Microsoft CertifiedData Analyst Associate (Power BI) or related certification. Databricks Certified Professional or similar certification. Soft Skills: Excellent analytical and troubleshooting skills Deep intellectual curiosity Highest degree of initiative and self-motivation Strong verbal and written communication skills, including presentation to varied audiences of complex technical/business topics Confidence technical leader Ability to work effectively with global, virtual teams, specifically including using of tools and artifacts to assure clear and efficient collaboration across time zones Ability to manage multiple priorities successfully Team-oriented, with a focus on achieving team goals Strong problem solving, analytical skills; Ability to learn quickly and retain and synthesize complex information from diverse sources

Role Description: We are looking for highly motivated expert Senior Data Engineer who can own the design & development of complex data pipelines, solutions and frameworks. The ideal candidate will be responsible to design, develop, and optimize data pipelines, data integration frameworks, and metadata-driven architectures that enable seamless data access and analytics. This role prefers deep expertise in big data processing, distributed computing, data modeling, and governance frameworks to support self-service analytics, AI-driven insights, and enterprise-wide data management. Roles & Responsibilities: Design, develop, and maintain scalable ETL/ELT pipelines to support structured, semi-structured, and unstructured data processing across the Enterprise Data Fabric. Implement real-time and batch data processing solutions, integrating data from multiple sources into a unified, governed data fabric architecture. Optimize big data processing frameworks using Apache Spark, Hadoop, or similar distributed computing technologies to ensure high availability and cost efficiency. Work with metadata management and data lineage tracking tools to enable enterprise-wide data discovery and governance. Ensure data security, compliance, and role-based access control (RBAC) across data environments. Optimize query performance, indexing strategies, partitioning, and caching for large-scale data sets. Develop CI/CD pipelines for automated data pipeline deployments, version control, and monitoring. Implement data virtualization techniques to provide seamless access to data across multiple storage systems. Collaborate with cross-functional teams, including data architects, business analysts, and DevOps teams, to align data engineering strategies with enterprise goals. Stay up to date with emerging data technologies and best practices, ensuring continuous improvement of Enterprise Data Fabric architectures. Must-Have Skills: Hands-on experience in data engineering technologies such as Databricks, PySpark, SparkSQL Apache Spark, AWS, Python, SQL, and Scaled Agile methodologies. Proficiency in workflow orchestration, performance tuning on big data processing. Strong understanding of AWS services Experience with Data Fabric, Data Mesh, or similar enterprise-wide data architectures. Ability to quickly learn, adapt and apply new technologies Strong problem-solving and analytical skills Excellent communication and teamwork skills Experience with Scaled Agile Framework (SAFe), Agile delivery practices, and DevOps practices. Good-to-Have Skills: Good to have deep expertise in Biotech & Pharma industries Experience in writing APIs to make the data available to the consumers Experienced with SQL/NOSQL database, vector database for large language models Experienced with data modeling and performance tuning for both OLAP and OLTP databases Experienced with software engineering best-practices, including but not limited to version control (Git, Subversion, etc.), CI/CD (Jenkins, Maven etc.), automated unit testing, and Dev Ops Education and Professional Certifications 9 to 12 years of Computer Science, IT or related field experience AWS Certified Data Engineer preferred Databricks Certificate preferred Scaled Agile SAFe certification preferred Soft Skills: Excellent analytical and troubleshooting skills. Strong verbal and written communication skills Ability to work effectively with global, virtual teams High degree of initiative and self-motivation. Ability to manage multiple priorities successfully. Team-oriented, with a focus on achieving team goals. Ability to learn quickly, be organized and detail oriented. Strong presentation and public speaking skills.