Head of Data (Sr. Director/Executive Director)

Structure Therapeutics•South San Francisco, CA

4d•$265,000 - $356,000•Hybrid

About The Position

Structure Therapeutics develops life-changing medicines for patients using advanced structure-based and computational drug discovery technology. The company’s platform combines the latest advancements in visualization of molecular interactions, computational chemistry, and data integration to design orally available, superior small molecule medicines that overcome current limitations of biologic and peptide drugs. We are advancing a clinical-stage pipeline of differentiated treatments focused on chronic diseases with high unmet need, including cardiovascular, metabolic, and pulmonary conditions. Structure Therapeutics is led by an experienced group of international drug innovators and financed by top-tier global life sciences investors. The company completed an initial public offering (IPO) in February 2023. With offices in California and Shanghai, Structure Therapeutics has the benefit of being at the center of life science innovation in both the US and China and capitalizing on the strengths of each geographic location. We are seeking a Head of Data (Senior Director or Executive Director) to own the most strategically important technical asset at Structure: the Knowledge System. This is the role that makes Structure’s AI smarter than anything a competitor can buy. Without exceptional data leadership, no AI product delivers differentiated value. You will build and govern the enriched, curated, domain-specific data layer that powers all of Structure’s AI — including ontologies, semantic enrichments, digital twins, and the data pipelines that feed them. You will lead a team of data engineers and data stewards and work in close partnership with the Head of AI Engineering, Head of AI Product, VP of IT, and business unit leads across Clinical, R&D, CMC, Commercial, and G&A. This is a greenfield build with the Crown Jewels at stake: the person who succeeds understands that data infrastructure is a strategic asset, not a utility.

Requirements

12+ years of experience in data engineering, data architecture, or knowledge management, with meaningful time in a regulated data-intensive industry (biotech, pharma, health tech, or equivalent)
Demonstrated track record of building knowledge infrastructure — not just pipelines — in complex domains; understands the difference between a data warehouse and a knowledge system and has built the latter
Fluency in modern data stack: cloud data warehouses and lake house architectures (Snowflake, Databricks, Iceberg), orchestration, and streaming ingestion (Kafka)
Experience with ontologies, knowledge graphs, or semantic data modeling
Working knowledge of GxP data governance frameworks (GCP, GMP, 21 CFR Part 11) and what they require of data classification, access controls, and audit trails
Strong FAIR principles fluency and operational experience applying them in a real data program
Experience leading data engineers and data stewards as a combined team; able to develop technical depth while building domain accountability
Collaborative by default: works effectively at the intersection of AI engineering, product management, business units, and IT platform teams
Familiarity with biopharma data systems (EDC, CTMS, LIMS, MES, ERP) and the data challenges of clinical and manufacturing environments
Familiarity with how data feeds AI/ML pipelines — works closely with the Head of AI Engineering and must speak fluently about MLOps data requirements
BS/MS/PhD in Computer Science, Data Science, Bioinformatics, Life Sciences, or a related field

Nice To Haves

Deep biopharma domain expertise: has operated at senior data leadership level inside a biotech or pharma organization through meaningful inflection points (IND, NDA, platform scale-up), with a track record that peers and scientific leaders in the industry recognize
Exceptional people development: a reputation for building and elevating data teams — known for developing both technical talent and domain expertise in the people they’ve led
Executive presence: communicates with authority and clarity at the executive level; can represent data strategy credibly to board members, investors, and scientific advisory boards
External thought leadership: recognized in the biopharma data and knowledge management community through publications, conference contributions, advisory roles, or industry engagement — someone whose perspective on data infrastructure carries weight in the field

Responsibilities

Build and maintain the enriched data layer that powers all AI at Structure, including defining and maintaining ontologies and semantic enrichments, building and managing the data catalog, curating datasets, and owning digital twins.
Lead a team of data engineers who build and maintain the pipelines that ingest, clean, transform, and deliver data from Structure’s clinical, manufacturing, and research systems into the Knowledge System.
Own data quality, documentation, and governance across the AI function’s data assets, managing data stewards accountable for quality in specific domains and maintaining the data catalog with FAIR principles applied.
Operationalize FAIR data principles (Findable, Accessible, Interoperable, Reusable) across Structure’s AI data assets.
Work with Legal and Compliance to ensure the Knowledge System meets applicable data governance requirements, including GxP data classification, access controls for regulated data, IP protection, and compliance with data use agreements.
Build and manage the data environments required for vendor evaluation, providing vendors access to production-grade data in a controlled, Structure-operated environment.
Work closely with the functionally-aligned PMs to understand the data needs of each strategic pillar and ensure the data roadmap serves Clinical, G&A, CMC, Commercial, and R&D.