Data Engineer, Clinical Operations

At Bristol Myers Squibb, our employees often ask, “Who are you working for?”—a question that fuels collaboration, accountability, and urgency in our work. Our purpose-driven culture inspires us to discover, develop, and deliver innovative medicines to prevail over serious diseases. We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits. This is work that transforms the lives of patients, and the careers of those who do it.

Position Summary:

As a Data Engineer, you will play a vital role in supporting the broader Data Engineering community to deliver cutting-edge data and analytics platforms for our Global Drug Development (GDD) IT group — specifically within the Cross Study Operations and Specimen Management domain.

We seek a candidate who excels at creating innovative, reliable, secure, and easy-to-use data ecosystems that support the full data product lifecycle — including ingesting, storing, processing, governing, and interacting with data. You will be a hands-on technical expert and individual contributor, applying expertise in data engineering, cloud platforms, and Generative AI to solve complex clinical data challenges across cross-study operations and biospecimen workflows, delivering high-quality, scalable data solutions.

You will collaborate closely with BI&T partners, business analysts, and data engineers, as well as Clinical Operations specialists, Specimen Management professionals, Cross-Study Operations leads, and other domain experts to support various data-driven initiatives and enhance the overall BMS data ecosystem. You will be expected to leverage Generative AI (GenAI), Databricks, and semantic technologies to drive innovation and efficiency within our data platforms.

Key Responsibilities:

• Collaborate with BI&T partners, cross-study operations leads, specimen management specialists, clinical study teams, clinical trial analysts, trial managers, domain experts, and cross-functional leaders in data engineering, data product teams, and data operations to support effective adoption of our Data Platform.
• Design, build, and maintain scalable, production-grade data pipelines and platform components supporting Cross Study Operations and Specimen Management product lines, including cross-trial data aggregation, specimen tracking, and biobanking workflows.
• Develop and enhance data solutions to accelerate data usage across cross-study clinical R&D programs, ensuring robustness, interoperability with consumer applications, and scalability.
• Optimize data platform components for performance, scalability, interoperability, availability, and cost-effectiveness using techniques such as cloud-native parallel processing, Databricks Delta Lake, caching, and partitioning.
• Help design and build scalable ETL/ELT pipelines and data models using Databricks, Delta Lake, cloud-native tools, semantic modeling, and interoperability standards for large, complex cross-study and specimen datasets in life sciences.

• Partner with business and data product owners to deliver hands-on technical solutions for data product development, standardization, testing, lineage, meeting latency requirements, and ensuring access governance across cross-study and specimen management data assets.
• Utilize Databricks Unity Catalog to enforce data governance, manage metadata, and ensure end-to-end data lineage across cross-study and specimen management data products.
• Contribute to the development of self-service data discovery solutions, fostering findability, accessibility, and reusability of cross-study operational and specimen data assets.
• Maintain thorough documentation of processes, data structures, and technical solutions; deliver clear technical recommendations and execute solutions effectively across the enterprise.

• Help build and deploy GenAI-powered and NLP-driven applications that deliver measurable outcomes across cross-study operations and specimen management, including efficiency gains, specimen traceability improvements, cross-study insight generation, risk mitigation, and compliance automation.
• Develop and operationalize cloud-based GenAI and LLM-powered applications in collaboration with data product owners, engineers, and data scientists, utilizing techniques such as RAG, fine-tuning, and vector embeddings to deliver high-quality data discovery and consumption features for cross-study and specimen management workflows.
• Leverage Databricks Mosaic AI and MLflow to develop, track, deploy, and manage machine learning and GenAI models at scale within cross-study and specimen management contexts.
• Stay current on technology trends in GenAI, RAG, semantic search, Databricks, cloud orchestration, and containerization; apply emerging best practices to optimize platform performance, scalability, and cost-effectiveness.

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