Sr. Data & AI Engineer, Commercialization

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. Key Responsibilities Build, test, and integrate the application components, tools, and utilities that keep the team’s Databricks-native environment running reliably. Own the health of the Databricks application environment day to day — jobs, workflows, notebooks, Unity Catalog — identifying and resolving issues proactively. Design and build AI for Engineering: agents, copilots, and LLM-assisted tooling that predict and fix data quality problems and improve the team’s delivery speed. Build the observability layer — monitoring, alerting, dashboards — that surfaces issues before they affect downstream consumers. Build accelerators and reusable frameworks (using tools such as Claude Code and GitHub Copilot) that reduce repetitive engineering work across the team. Develop ETL/ELT pipelines on Databricks, PySpark, SQL, and AWS (Glue, Lambda, S3, Athena) that hold up at production scale. Lead root cause analysis on production issues, driving fixes and process changes that prevent recurrence. Set the technical standards other engineers on the team will build to. Partner closely with the Platform Engineering team so tooling and infrastructure integrate cleanly. Bring CI/CD and infrastructure-as-code discipline to how the team ships. Mentor junior engineers on observability, automation, and applied use of AI to improve engineering delivery. Skills & Competencies Strong command of Python, PySpark, and SQL — sufficient to build automation and monitoring tooling, not just write transformation logic. Real production experience running a Databricks environment — Delta Lake internals, Unity Catalog, Workflows. A track record of building AI agent or LLM tooling that has measurably improved an engineering team's delivery. A strong orientation toward observability as a default practice rather than an afterthought. Comfort with agent frameworks (LangChain, LangGraph, CrewAI, AutoGen, or similar) and sound judgment on where they are genuinely useful. Working depth in AWS's data services — Glue, Lambda, S3, Athena — sufficient to inform architecture decisions. A habit of writing maintainable code, reviewing others' code constructively, and having previously set technical standards for a team. Sound understanding of data governance, with the ability to build access controls in rather than add them after the fact.