Data Engineer, Clinical Operations

Bristol Myers Squibb • Princeton, NJ

2d • $65,540 - $79,413

About The Position

The Data Engineer is a hands-on technical individual contributor supporting the Data Engineering community and the Global Drug Development IT group, specifically the Cross Study Operations and Specimen Management domain. The role builds innovative, reliable, secure, and user-friendly data ecosystems across the full data product lifecycle, including data ingestion, storage, processing, governance, and interaction. It applies data engineering, cloud platform, and Generative AI expertise to clinical data challenges and delivers scalable solutions for cross-study operations and biospecimen workflows.

The position works closely with BI&T partners, business analysts, data engineers, Clinical Operations and Specimen Management professionals, Cross-Study Operations leads, and other domain experts to advance data-driven initiatives and adoption of the BMS Data Platform. The engineer develops production-grade data pipelines and platform components, optimizes cloud data solutions, supports governance and self-service discovery, and develops GenAI- and NLP-powered applications for clinical operations and specimen workflows. The role also shares technical knowledge and mentors junior analysts, interns, and vendor resources.

The position is listed for Princeton, New Jersey, with a starting pay range of $65,540–$79,413. The posting describes BMS benefit programs, which may include health and insurance offerings, retirement and financial protection benefits, paid time off, holidays, volunteer time, and other leave benefits, subject to eligibility and applicable plan terms. The assigned work model is not specified in the posting.

Requirements

• 0–2 years of hands-on experience in Data Engineering, Analytics, and AI/ML, including implementing and operating data capabilities and solutions in a cloud environment.
• Hands-on expertise with Databricks, including Delta Lake, Unity Catalog, Databricks Workflows, Mosaic AI, and MLflow.
• Expertise in cloud-native data platforms, ETL/ELT pipeline design, data modeling, and semantic analytics for large, complex datasets, with hands-on DevOps experience.
• Strong proficiency in Python, SQL, Spark, including PySpark on Databricks, and GenAI frameworks.
• Hands-on experience with LLM architectures, RAG, prompt engineering, and agentic frameworks.
• Experience creating and maintaining data pipeline architecture for large, complex datasets, including domain semantic modeling.
• Experience delivering production-grade GenAI applications, predictive models, and self-service analytics tools for critical clinical or research business functions.
• Working knowledge of GenAI and LLM approaches including RAG, Chain-of-Thought, fine-tuning, vectorization, agentic frameworks, and prompt engineering for improving response accuracy.
• Strong stakeholder engagement and communication skills, including the ability to explain technical concepts to non-technical audiences and drive adoption across functional teams.
• Commitment to engineering excellence, documentation, and process improvement.

Nice To Haves

• Databricks certification, such as Databricks Certified Data Engineer Associate or Professional.
• Experience with life sciences, clinical trial operations, or specimen/biobanking workflows.
• Functional knowledge of Life Sciences R&D and clinical trial operations; highly preferred.

Responsibilities

• Collaborate with BI&T partners, cross-study operations leads, specimen management specialists, clinical study teams, clinical trial analysts, trial managers, domain experts, and data engineering, data product, and data operations teams to support adoption of the Data Platform.
• Design, build, and maintain scalable, production-grade data pipelines and platform components for Cross Study Operations and Specimen Management, including cross-trial aggregation, specimen tracking, and biobanking workflows.
• Develop robust, interoperable, and scalable data solutions that accelerate data use across cross-study clinical R&D programs.
• Optimize data platform performance, scalability, interoperability, availability, and cost-effectiveness using cloud-native parallel processing, Databricks Delta Lake, caching, partitioning, and related techniques.
• Design and build ETL/ELT pipelines and data models using Databricks, Delta Lake, cloud-native tools, semantic modeling, and interoperability standards for large cross-study and specimen datasets.
• Partner with business and data product owners on data product development, standardization, testing, lineage, latency requirements, and access governance.
• Use Databricks Unity Catalog to enforce governance, manage metadata, and maintain end-to-end data lineage.
• Contribute to self-service data discovery solutions that improve findability, accessibility, and reusability of cross-study operational and specimen data assets.
• Document processes, data structures, and technical solutions; provide technical recommendations and implement solutions across the enterprise.
• Build and deploy GenAI-powered and NLP-driven applications for cross-study operations and specimen management, supporting outcomes such as efficiency, specimen traceability, insight generation, risk mitigation, and compliance automation.
• Develop and operationalize cloud-based GenAI and LLM applications using methods such as RAG, fine-tuning, and vector embeddings for data discovery and consumption features.
• Use Databricks Mosaic AI and MLflow to develop, track, deploy, and manage machine learning and GenAI models at scale.
• Monitor technology trends in GenAI, RAG, semantic search, Databricks, cloud orchestration, and containerization, and apply best practices to improve platform performance, scalability, and cost-effectiveness.
• Provide technical guidance and mentorship to junior analysts, interns, and vendor resources on Databricks, engineering practices, and business alignment.
• Promote continuous learning, engineering excellence, and knowledge sharing across the data engineering community.

Benefits

• Wellbeing support.
• Retirement and financial protection benefits.
• Medical, dental, vision, life, and disability insurance offerings.
• Flexible Time Off without a set accrual limit for eligible U.S.-based exempt employees, subject to manager approval.
• 11 paid company holidays each year for eligible employees.
• For specified non-exempt, RayzeBio, and Puerto Rico employees: 160 hours of paid vacation annually for new hires, 11 paid company holidays, and 3 optional holidays, subject to manager approval and applicable terms.
• Depending on eligibility, paid sick leave, up to two paid volunteer days per year, summer hours flexibility, and leaves of absence for medical, personal, parental, caregiver, bereavement, or military needs.
• Eligible employees may receive an annual Global Shutdown between Christmas Day and New Year's Day.
• Potential discretionary cash incentives and stock opportunities.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: No Education Listed