5 Stardog Jobs

Essential Skills/Experience: Hands-on experience with Neo4j and Cypher query development. Solid grounding in RDF, OWL, SHACL, SPARQL, and semantic modeling standard methodologies. Strong proficiency in Python (or an equivalent language) for automation, data transformation, and pipeline integration. Demonstrated ability to define use cases, structure delivery backlogs, and manage technical execution. Strong problem-solving and communication skills, with a delivery-focused mindset. Bachelor s degree in Computer Science, Data Science, Information Systems, or a related field (Master s preferred). Desirable Skills/Experience: Experience with additional graph platforms such as GraphDB, Stardog, or Amazon Neptune. Familiarity with Cognite Data Fusion, IoT/industrial data integration, or other large-scale operational data platforms. Understanding of knowledge representation techniques and reasoning systems. Exposure to AI/ML approaches using graphs or semantic features. Knowledge of tools such as Prot g , TopBraid Composer, or VocBench. Familiarity with metadata standards, data governance, and FAIR principles.

ABOUT THE ROLE Role Description: We are seeking a seasoned Senior Engineering Manager ( Data Engineer ing) 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. As a senior leader in the data organization, you will oversee 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 availability, 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/pharmadomain companies. Experience in designing and maintainingdata 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 on Databricks 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, PySparkor 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 & observability 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 12 to 15 years of experience in Computer Science, IT or related field Databricks Certificate preferred Scaled Agile SAFe certification preferred Experience in life sciences, healthcare, or other regulated industries with large-scale operational data environments. Familiarity with incident and change management processes (e.g., ITIL). 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 Role Description: We are seeking a Senior Data Engineer with expertise in Graph Data technologies to join our data engineering team and contribute to the development of scalable, high-performance data pipelines and advanced data models that power next-generation applications and analytics. This role combines core data engineering skills with specialized knowledge in graph data structures, graph databases, and relationship-centric data modeling, enabling the organization to leverage connected data for deep insights, pattern detection, and advanced analytics use cases. The ideal candidate will have a strong background in data architecture, big data processing, and Graph technologies and will work closely with data scientists, analysts, architects, and business stakeholders to design and deliver graph-based data engineering solutions. Roles & Responsibilities: Design, build, and maintain robust data pipelines using Databricks (Spark, Delta Lake, PySpark) for complex graph data processing workflows. Own the implementation of graph-based data models, capturing complex relationships and hierarchies across domains. Build and optimize Graph Databases such as Stardog, Neo4j, Marklogic or similar to support query performance, scalability, and reliability. Implement graph query logic using SPARQL, Cypher, Gremlin, or GSQL, depending on platform requirements. Collaborate with data architects to integrate graph data with existing data lakes, warehouses, and lakehouse architectures. Work closely with data scientists and analysts to enable graph analytics, link analysis, recommendation systems, and fraud detection use cases. Develop metadata-driven pipelines and lineage tracking for graph and relational data processing. Ensure data quality, governance, and security standards are met across all graph data initiatives. Mentor junior engineers and contribute to data engineering best practices, especially around graph-centric patterns and technologies. Stay up to date with the latest developments in graph technology, graph ML, and network analytics. What we expect of you Must-Have Skills: Hands-on experience in Databricks, including PySpark, Delta Lake, and notebook-based development. Hands-on experience with graph database platforms such as Stardog, Neo4j, Marklogic etc. Strong understanding of graph theory, graph modeling, and traversal algorithms Proficiency in workflow orchestration, performance tuning on big data processing Strong understanding of AWS services Ability to quickly learn, adapt and apply new technologies with strong problem-solving and analytical skills Excellent collaboration and communication skills, with experience working 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 Masters degree and 3 to 4 + years of Computer Science, IT or related field experience Bachelors degree and 5 to 8 + 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.

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

We are seeking a skilled and creative RShiny Developer with hands-on experience in MarkLogic and graph databases. You will be responsible for designing and developing interactive web applications using RShiny, integrating complex datasets stored in MarkLogic, and leveraging graph capabilities for advanced analytics and knowledge representation. Roles & Responsibilities: Develop interactive dashboards and web applications using RShiny. Connect and query data from MarkLogic, especially leveraging its graph and semantic features (e.g., RDF triples, SPARQL). Design and maintain backend data workflows and APIs. Collaborate with data scientists, analysts, and backend engineers to deliver integrated solutions. Optimize performance and usability of RShiny applications. Functional Skills: Must-Have Skills: Proven experience with R and RShiny in a production or research setting. Proficiency with MarkLogic , including use of its graph database features (triples, SPARQL queries, semantics). Familiarity with XQuery , XPath , or REST APIs for interfacing with MarkLogic. Strong understanding of data visualization principles and UI/UX best practices. Experience with data integration and wrangling. Good-to-Have Skills: Experience with additional graph databases (e.g., Neo4j, Stardog) is a plus. Background in knowledge graphs, linked data, or ontologies (e.g., OWL, RDF, SKOS). Familiarity with front-end frameworks (HTML/CSS/JavaScript) to enhance RShiny applications. Experience in regulated industries (e.g., pharma, finance) or with complex domain ontologies. Professional Certifications (preferred): SAFe Methodology Courses in R, RShiny, and data visualization from reputable institutions (e.g., Johns Hopkins Data Science Specialization on Coursera) Other Graph Certifications (optional but beneficial) Neo4j Certified Professional (to demonstrate transferable graph database skills) Linked Data and Semantic Web Training (via organizations like W3C or OReilly) Soft Skills: Excellent written and verbal communications skills (English) in translating technology content into business-language at various levels 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 problem-solving and analytical skills. Strong time and task management skills to estimate and successfully meet project timeline with ability to bring consistency and quality assurance across various projects.