3 Databricks Notebooks Jobs

We are looking for energetic, self-motivated and exceptional Data engineer to work on extraordinary enterprise products based on AI and Big Data engineering leveraging AWS/Databricks tech stack. He/she will work with star team of Architects, Data Scientists/AI Specialists, Data Engineers and Integration. Skills and Qualifications: 5+ years of experience in DWH/ETL Domain; Databricks/AWS tech stack 2+ years of experience in building data pipelines with Databricks/ PySpark /SQL Experience in writing and interpreting SQL queries, designing data models and data standards. Experience in SQL Server databases, Oracle and/or cloud databases. Experience in data warehousing and data mart, Star and Snowflake model. Experience in loading data into database from databases and files. Experience in analyzing and drawing design conclusions from data profiling results. Understanding business process and relationship of systems and applications. Must be comfortable conversing with the end-users. Must have ability to manage multiple projects/clients simultaneously. Excellent analytical, verbal and communication skills. Role and Responsibilities: Work with business stakeholders and build data solutions to address analytical & reporting requirements. Work with application developers and business analysts to implement and optimise Databricks/AWS-based implementations meeting data requirements. Design, develop, and optimize data pipelines using Databricks (Delta Lake, Spark SQL, PySpark), AWS Glue, and Apache Airflow Implement and manage ETL workflows using Databricks notebooks, PySpark and AWS Glue for efficient data transformation Develop/ optimize SQL scripts, queries, views, and stored procedures to enhance data models and improve query performance on managed databases. Conduct root cause analysis and resolve production problems and data issues. Create and maintain up to date documentation of the data model, data flow and field level mappings. Provide support for production problems and daily batch processing. Provide ongoing maintenance and optimization of database schemas, data lake structures (Delta Tables, Parquet), and views to ensure data integrity and performance

Design, build, and maintain scalable and efficient data pipelines using Azure services such as Azure Data Factory (ADF), Azure Databricks, and Azure Synapse Analytics. Develop and optimize ETL/ELT workflows for ingestion, cleansing, transformation, Required Candidate profile 5-10 years of overall experience in Data Engineering with a minimum of 5 years in Azure-based data projects. Strong understanding of data warehouse architecture, data lakes, and big data frameworks

Responsibilities As a Databricks Data Engineer, you will: Design, construct, install, test, and maintain highly scalable data management systems using Azure Databricks. Develop data processing pipelines using Databricks Notebooks, Spark SQL, and other Databricks utilities. Integrate Azure Databricks with other Azure services such as Azure Data Lake Storage, Azure SQL Data Warehouse, and Azure Cosmos DB. Ensure optimal performance of data processing and extraction, transformation, and loading (ETL) processes. Collaborate with data architects to implement effective data models and schemas within the Databricks environment. Monitor data reliability, efficiency, and quality, and implement improvements as needed. Work with stakeholders to assist with data-related technical issues and support their data infrastructure needs. Develop and maintain documentation related to data pipeline architecture, development processes, and data governance. Stay current with industry trends and developments in cloud computing, big data, and Azure Databricks. Skills Azure certifications such as Azure Data Engineer Associate or Azure Data Scientist Associate. Experience with machine learning frameworks and libraries. Knowledge of additional big data tools and platforms. Familiarity with data visualization tools and techniques. Qualification Bachelor's degree in Engineering or a related field. Proven experience as a Data Engineer with a focus on Azure Databricks and related Azure services. Strong programming skills in languages such as Python, Scala, or Java. Experience with Apache Spark and its integration with Databricks. Proficiency in SQL and experience with data modeling and data warehousing concepts. Familiarity with DevOps practices and CI/CD pipelines. Excellent analytical and problem-solving abilities. Effective communication skills and the ability to work collaboratively in a team environment. Career Level - IC2 Responsibilities As a Databricks Data Engineer, you will: Design, construct, install, test, and maintain highly scalable data management systems using Azure Databricks. Develop data processing pipelines using Databricks Notebooks, Spark SQL, and other Databricks utilities. Integrate Azure Databricks with other Azure services such as Azure Data Lake Storage, Azure SQL Data Warehouse, and Azure Cosmos DB. Ensure optimal performance of data processing and extraction, transformation, and loading (ETL) processes. Collaborate with data architects to implement effective data models and schemas within the Databricks environment. Monitor data reliability, efficiency, and quality, and implement improvements as needed. Work with stakeholders to assist with data-related technical issues and support their data infrastructure needs. Develop and maintain documentation related to data pipeline architecture, development processes, and data governance. Stay current with industry trends and developments in cloud computing, big data, and Azure Databricks. Skills Azure certifications such as Azure Data Engineer Associate or Azure Data Scientist Associate. Experience with machine learning frameworks and libraries. Knowledge of additional big data tools and platforms. Familiarity with data visualization tools and techniques. Qualification Bachelor's degree in Engineering or a related field. Proven experience as a Data Engineer with a focus on Azure Databricks and related Azure services. Strong programming skills in languages such as Python, Scala, or Java. Experience with Apache Spark and its integration with Databricks. Proficiency in SQL and experience with data modeling and data warehousing concepts. Familiarity with DevOps practices and CI/CD pipelines. Excellent analytical and problem-solving abilities. Effective communication skills and the ability to work collaboratively in a team environment.