Formation Bio
Associate Director, Data Engineering
$214K–$267K
New York, NY +4 · Director · Full-time · Posted Aug 31, 2026
- Location
- New York, NY
Boston, MA
San Francisco, CA
116 Huntington Ave., Back Bay, Boston, MA, 02116
16 E 34th St., New York City, NY, 10016 - Experience
- 7+ years total across hands-on data engineering and people management, including 3+ years directly managing Data Engineers, Analytics Engineers, or similar.
About the job
Leads the data engineering team and platform, setting technical direction and ensuring reliable, governed data supports clinical operations, asset evaluation, and company decisions.
What you'll do
- Hire, coach, and manage data engineers, including setting goals and managing performance.
- Set the data engineering roadmap and oversee platform architecture, pipelines, warehouse, data quality, and governance.
- Coordinate with cross-functional stakeholders and manage incident support, team resources, and budgets.
What they ask for
- 7+ years of combined hands-on data engineering and people management experience, including 3+ years directly managing Data Engineers, Analytics Engineers, or similar.
- Track record of hiring, developing, and retaining engineers; experience coordinating technical projects and programs, including budgeting and reporting.
- Working knowledge of data governance, access control, and auditability; experience with production pipeline data quality, testing, and observability.
- Working knowledge of Python, SQL, Docker, GitHub, and Terraform/OpenTofu; strong point of view on modern data stack tooling.
Nice to have
- Pharmaceutical, biotechnology, or life-sciences data experience.
- Experience with regulated or sensitive data, such as clinical data or PHI.
Pay
$213,500 - $267,000 (USD; total compensation range)
Tools
Snowflake, Dagster, Airflow, dbt, Python, SQL, Docker, Terraform/OpenTofu
AI focus
Leads a team using AI tools, including LLMs and agentic coding systems, as part of daily engineering work to build pipelines, investigate data quality, improve data modeling, and streamline workflows; emphasizes responsible use and validation, especially with clinical and regulated data.
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