Senior Data Engineer — Translational Data Products

About this role

Working with Us Challenging. Meaningful. Life-changing. Those aren’t words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this is work that transforms the lives of patients, and the careers of those who do it. You’ll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high-achieving teams. Take your career farther than you thought possible.

Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives. Read more: careers.bms.com/working-with-us .

About the role Our team builds and maintains data products that make R&D data usable and AI-ready: biomarker, biospecimen, clinical trial, omics, among others, assembled into governed, curated, linked, documented, AI-ready products that translational scientists and analysts use for insight generation and decision making. You will own data products end to end — from documenting where the data lives, to unlocking the source system with its data owner, through entity mapping, modeling and validation, to making sure the product answers high-value scientific questions and gets used or retired. You will work alongside data scientists, machine learning engineers, and translational medicine stakeholders, and you will use Claude Code as a normal part of how you deliver.

What you will do ·       Build, operate and own data products. Design, build, test, and maintain ETL/data normalization pipelines on Databricks and AWS that create governed, well-documented datasets in Unity Catalog (both tables and views). ·       Unlock data access. Work directly with data owners and governance partners to bring new sources under governance and make them AI-ready — cataloged, described, permissioned, quality- checked, and discoverable. This may include csv, tsv, json, xlsx, powerpoint, structured documents, etc. ·       Pursue business impact. Engage stakeholders in translational medicine, biomarker sciences, predictive medicine, reverse translation and clinical development to understand and translate the high value questions they are trying to answer into robust data product designs and data maps linking sources to targets (STTM). ·       Translate between business and data. Turn a scientific or business question into a data model and a mapping from the question to the SQL query; carefully describe data constraints / context and considerations into caveats that a stakeholder can understand. ·       Validate everything. Bring a test-driven, validation-driven mindset: expectations and data quality checks in the pipeline, reconciliation against source, unit and regression tests that run automatically in GitHub Actions on every pull request, and documented evidence that a number is correct before anyone depends on it. ·       Engineer for robustness. Create the hooks, guardrails, CDK / CloudFormation infrastructure as code, and automated checks that keep our work reliable and aligned with BMS enterprise IT standards — SDLC, security, SSO / identity and access management, mandatory resource tagging and service registration (CRID, ISR assessment), code review, secrets handling, change management. Run the required cyber checks — dependency, container and infrastructure-as-code scanning (Wiz, Dependabot) on every pull request — and respond quickly when a vulnerability is reported, remediating critical findings within BMS Cybersecurity timelines. ·       Work with ML engineers. Prepare feature-ready and model-ready datasets, and support ML and GenAI workloads built on top of our products; support deployment and accessibility of data products on NVIDIA clusters. ·       Work as part of a team. GitHub-based development, pull requests, code review, automated build/test/deploy pipelines (continuous integration and delivery, CI/CD), infrastructure as code. ·       Continuous improvement. Work with stakeholders to ensure the data products continue to meet their expectations as their work evolves and needs change.

Required qualifications ·    Requires a Bachelor's degree in a relevant discipline along with 4+ yrs of relevant work experience. ·       Demonstrated experience building production data pipelines (ETL/ELT) at scale. ·       Very strong SQL — complex joins, window functions, performance tuning, and the judgment to know when a query result is wrong. ·       Python for data engineering and data science. ·       AWS experience — S3, IAM, Glue, Lambda, ECS and Fargate. ·       Containerized service deployment — package a Python service in Docker and ship it to ECS/Fargate through infrastructure as code (AWS CDK / CloudFormation) and a GitHub Actions pipeline, with health checks, secrets from parameter store, and SSO / OIDC authentication. Our MCP servers, which expose data products to AI agents, are deployed this way and you will maintain your own. ·       CloudOps / DevOps practice: monitoring, alerting, incident response. ·       GitHub fluency: branching, pull requests, code review, and GitHub Actions. ·       Test-driven and validation-driven — unit tests that run in the pipeline, plus regression and reconciliation checks that run against the platform (a Databricks job or SQL check) so a pull request fails when a number changes. Point to a validation suite you built and say what it caught. ·       Command-line fluency — git, shell, the AWS and Databricks CLIs, and daily hands-on use of Claude Code to drive a task end to end. Claude Code is the approved agentic coding tool here; other agents are not permitted at this time. ·       Life sciences data — hands-on experience with clinical, molecular and assay data: patie

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