Data Engineer, Clinical Operations

About the role

Bristol Myers Squibb seeks a Data Engineer to support the company's Global Drug Development IT group. The ideal candidate will have 5+ years of hands-on experience in data engineering, analytics, and AI/ML, with expertise in cloud environments. The position involves designing, building, and maintaining scalable data pipelines and platforms, developing GenAI-powered applications, and fostering a culture of continuous learning.

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.
• Design, build, and maintain scalable, production-grade data pipelines and platform components supporting Cross Study Operations and Specimen Management product lines.
• 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.
• 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.
• Utilize Databricks Unity Catalog to enforce data governance, manage metadata, and ensure end-to-end data lineage.
• 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.
• 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.
• Serve as a go-to technical expert within the Cross Study Operations and Specimen Management domain, providing guidance and mentorship to junior analysts, interns, and vendor resources on Databricks, technical best practices, and business alignment.
• Foster a culture of continuous learning, engineering excellence, and knowledge sharing across the data engineering community.

Qualifications & Experience

• 5+ years of hands-on experience in Data Engineering, Analytics, and AI/ML, with demonstrated expertise 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; Databricks certification (e.g., Databricks Certified Data Engineer Associate/Professional) is a strong plus.
• Demonstrated expertise in cloud-native data platforms, ETL/ELT pipeline design, data modeling, and semantic analytics for large-scale, 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.
• Proficiency in creating and maintaining optimal data pipeline architecture for large, complex datasets, including semantic modeling within a domain — preferably life sciences, clinical trial operations, or specimen/biobanking workflows.
• Demonstrated experience delivering production-grade GenAI applications, predictive models, and self-service analytics tools supporting critical clinical or research business functions.
• Working knowledge of LLM and GenAI-driven approaches, including RAG, Chain-of-Thought, fine-tuning, vectorization, agentic frameworks, and prompt engineering techniques for improving the accuracy of LLM-based responses.
• Strong stakeholder engagement and communication skills; ability to clearly articulate technical concepts to non-technical audiences and drive adoption of data solutions across functional teams.
• Commitment to engineering excellence, documentation, and process improvement; functional knowledge of Life Sciences R&D and clinical trial operations is highly preferred.

Benefits

Bristol Myers Squibb offers a comprehensive benefits package including health coverage, wellbeing support, financial well-being and protection, and work-life benefits. Additional incentive cash and stock opportunities may be available based on eligibility. For more details, visit careers.bms.com/working-with-us.