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Formation Bio is an AI-driven pharma company founded in 2016 (originally TrialSpark) that accelerates drug development and clinical trials through technology platforms. Backed by a16z, Sequoia, Sanofi, Thrive Capital, and others, the company partners with pharma, research organizations, and biotechs to advance drug candidates past clinical proof of concept.
As Associate Director of Data Engineering, you will lead the data engineering team responsible for Formation Bio's data platform—the foundation for all company decisions from drug asset selection to trial execution. You are accountable for the pipelines, warehouse, and data products your team builds; the speed and quality of their work; and creating conditions for engineers to grow and perform at a high level.
This is an AI-native engineering organization. You and your team will be fluent in modern AI tools, including agentic coding systems, as a core part of how you build pipelines, model data, investigate data quality issues, and improve workflows. You'll coach your team to treat AI as a permanent force multiplier while instilling the judgment to validate output and operate trustworthy systems, especially for clinical and regulated data.
You will lead a team of data engineers and evolve an established data platform for Formation Bio's next growth stage. Key responsibilities include clarifying ownership, strengthening reliability and governance, and increasing the speed at which trusted data supports clinical operations, asset evaluation, and decision-making. You'll partner closely with Data Science, Analytics, Clinical Operations, Biostatistics, and Business Development to set technical direction, unblock your team, represent data engineering needs in cross-functional planning, and promote a culture of trusted, well-governed data.
Specific responsibilities: hire, coach, and develop data engineers; set technical direction and roadmap for data engineering; partner with stakeholders on pipeline and data model design; oversee data platform architecture including ingestion from clinical/operational/vendor sources, orchestration, transformation, and Snowflake warehouse management; establish and maintain strong data quality, observability, lineage, and documentation practices; own data governance for regulated and sensitive data including access controls, auditability, and traceability for clinical trial systems.
