GenAI Data Engineer for Clinical Operations

Biopharma Careers; United States; On-site; USD 88,000 - 106,000; Full time

Benefits offered by this job

Health Coverage

Wellbeing Programs

401(k) plan

Paid Time Off

Job summary

Bristol Myers Squibb in Princeton, NJ is seeking a Data Engineer to support the Global Drug Development IT group in Cross Study Operations and Specimen Management. You will build data pipelines, ensure governance, and enable GenAI-powered data discovery.

You will work with Databricks, Delta Lake, Unity Catalog and MLflow to deliver scalable data solutions across multiple studies, biospecimen workflows, and data products.

Qualifications

• 2+ years hands-on experience in Data Engineering, Analytics, and AI/ML in a cloud environment.
• Hands-on Databricks experience including Delta Lake, Unity Catalog, and MLflow.
• Strong Python, SQL and Spark skills; familiarity with GenAI concepts and governance.

Responsibilities

• Collaborate with BI&T partners and domain experts to drive data platform adoption.
• Design, build, and maintain scalable production data pipelines for Cross Study Operations and Specimen Management.
• Develop data models and ETL/ELT pipelines using Databricks and cloud tools.
• Ensure data governance, lineage, and access controls across large datasets.
• Implement GenAI-powered data discovery and NLP-driven applications.
• Collaborate on GenAI and LLM-based solutions using RAG, vector embeddings, and fine-tuning.
• Mentor junior analysts and foster a culture of engineering excellence.

Skills

Python

SQL

Spark/PySpark

GenAI

RAG

Prompt engineering

Tools

Databricks

Delta Lake

Unity Catalog

Databricks Workflows

MLflow

Mosaic AI

Job description

Bristol Myers Squibb in Princeton, NJ is seeking a Data Engineer to support the Global Drug Development IT group in Cross Study Operations and Specimen Management. You will build data pipelines, ensure governance, and enable GenAI-powered data discovery.

You will work with Databricks, Delta Lake, Unity Catalog and MLflow to deliver scalable data solutions across multiple studies, biospecimen workflows, and data products.