Associate Director, Data Engineering

Responsibilities

• Hire, coach, and develop data engineers; set goals, provide feedback, and manage performance.
• Set the data engineering technical direction and roadmap across data quality, governance, scalability, and stakeholder experience.
• Partner with Data Science, Analytics, Clinical Operations, Data Managers, and Business Development to deliver pipelines and data models for reporting, asset evaluation, analytics, and ML use cases.
• Oversee data platform architecture and maintenance, including clinical, operational, and third-party data ingestion, orchestration, transformation, and the Snowflake warehouse across development, staging, and production.
• Establish strong data quality, observability, lineage, documentation, and engineering quality practices.
• Own governance for regulated and sensitive data, including access controls, auditability, and traceability.
• Review requirements and design documents for data products and pipelines and maintain knowledge-sharing practices.
• Drive training and mentoring on data engineering and modern data tooling.
• Manage data platform support rotation, incident escalation, and retrospectives.
• Manage budgeting, resourcing, and reporting for data engineering initiatives.

Requirements

• 7+ years of total experience across hands-on data engineering and people management, including 3+ years managing data engineers, analytics engineers, or similar roles.
• Track record of hiring, developing, and retaining strong engineers, with ability to provide direct feedback and set clear expectations.
• Experience directing teams that use LLMs and agentic coding systems responsibly in daily engineering work.
• Strong knowledge of modern data stack tooling, including Snowflake, Dagster or Airflow, dbt, and batch versus streaming architectures.
• Exceptional collaboration and communication skills across technical and non-technical disciplines.
• Experience managing or coordinating technical projects and programs, including budgeting, estimation, tracking, and reporting.
• Knowledge of data governance, access control, and auditability practices, preferably with regulated or sensitive data.
• Working knowledge of Python, SQL, Docker, GitHub, and Terraform or OpenTofu.
• Experience overseeing data quality frameworks, testing, and observability for production data pipelines.

Nice to Have

• Experience with pharmaceutical, biotechnology, or life-sciences data.

Compensation

• Total compensation range: $213,500–$267,000.
• Compensation may include base salary, equity, comprehensive benefits, and perks.