Associate Director, Data Engineering

Responsibilities

• Hire, coach, and develop a team of data engineers, including setting goals, delivering feedback, and managing performance.
• Set the technical direction and roadmap for data engineering, prioritizing work across data quality, governance, scalability, and stakeholder experience in partnership with engineering and business leadership.
• Partner with key stakeholders such as: Data Science, Analytics, Clinical Operations, Data Managers, and Business Development on the design and delivery of pipelines and data models that support operational reporting, asset evaluation, and advanced analytics and ML use cases, ensuring your team designs solutions that close gaps and sees them through from concept to implementation.
• Oversee the architecture and maintenance of the data platform, including ingestion from clinical, operational, and third-party vendor sources, orchestration, transformation, and the Snowflake warehouse, across development, staging, and production environments.
• Ensure the team establishes and maintains strong data quality, observability, lineage, and documentation practices, and holds a high bar for engineering quality across the data platform.
• Own data governance practices for regulated and sensitive data, including access controls, auditability, and traceability appropriate for systems supporting clinical trial operations.
• Review and provide feedback on requirements and design documents for new data products and pipelines, and ensure documentation and knowledge-sharing practices stay strong across the team.
• Drive knowledge sharing, training, and mentoring on data engineering fundamentals and modern data tooling throughout the organization.
• Own the team's support rotation coverage for data platform incidents, including staffing, escalation paths, and incident retrospectives.
• Manage team budgeting, resourcing, and reporting for data engineering initiatives.

Qualifications

• 7+ years of total experience across hands-on data engineering and people management, including 3+ years directly managing Data Engineers, Analytics Engineers, or similar.
• A track record of hiring, developing, and retaining strong engineers, with the ability to give direct, actionable feedback and set clear expectations.
• Skilled at directing a team that uses AI tools, including LLMs and agentic coding systems, as a core, daily part of how they build. Able to set a high bar for using AI responsibly to accelerate pipeline development, investigate data quality issues, improve data modeling workflows, and increase the team's overall engineering leverage.
• Exceptional collaboration and communication skills to effectively engage and work alongside stakeholders from diverse disciplines and varying levels of technical expertise, including Data Science, Clinical Operations, Biostatistics, BD, and non-technical partners.
• Experience managing or coordinating technical projects and programs, including resource budgeting, estimates, tracking, and reporting.
• Experience with data governance, access control, and auditability practices, ideally including experience with regulated or sensitive data (clinical, PHI, or similar).
• Experience with Python, SQL, Docker, GitHub, and Terraform/OpenTofu sufficient to guide and evaluate your team's technical decisions.
• Experience overseeing data quality frameworks, testing, and observability tooling for production data pipelines.
• Experience with pharmaceutical, biotechnology, or broader life-sciences industry data is a plus, but not required.