Associate Director, Data Engineering (Real World Data)

Responsibilities

• Lead the RWD Intelligence function within Data Science, owning data strategy, sourcing, and delivery of analysis-ready datasets
• Architect and maintain the supporting infrastructure (pipelines, ingestion workflows, data models, lakes/marts) across EHR/EMR, claims, registries, and genomics-linked cohorts
• Drive adoption and extension of harmonization frameworks (e.g., OMOP CDM) across heterogeneous data sources, leveraging AI/ML tools for entity resolution, ontology mapping, data quality monitoring, and automated harmonization
• Manage RWD vendor relationships end-to-end: evaluate providers, negotiate data use agreements, broker new partnerships, and integrate acquired datasets into the platform
• Partner with Data Science, Clinical Development, Business Development, and Engineering teams to define RWD use cases (trial feasibility, synthetic control arms, epidemiology, label expansion) and productize ad hoc pipelines into scalable, production-grade systems
• Foster a culture of data quality rigor, documentation, and reproducibility across all RWD assets

Requirements

• BSc or MSc in biomedical informatics, computational sciences, epidemiology, or a related quantitative field
• 5+ years of industry experience working directly with real-world data (EHR/EMR, claims, registries, linked biobank data) in pharma, biotech, health tech, or consulting, with at least 2+ years in people management
• Strong data engineering proficiency (pipelines, ingestion frameworks, data models, data lakes/marts) combined with deep working knowledge of biological and medical ontologies (ICD, SNOMED CT, MedDRA, RxNorm, ATC) and harmonization standards, particularly OMOP CDM
• Demonstrated experience with RWD procurement and vendor management: evaluating data providers, negotiating agreements, and integrating new data assets
• Proven ability to deliver RWD-derived insights across multiple drug development use cases (e.g., trial design, epidemiology, comparative effectiveness, label expansion), with familiarity across the development lifecycle from target selection through post-market
• Proficiency with modern AI/ML tools, including large language models, and their applications in data engineering and harmonization workflows
• Strong communication skills with the ability to translate complex data infrastructure concepts for clinical, scientific, and executive audiences

Preferred Qualifications

• PhD in biomedical informatics, epidemiology, computational biology, or a related field
• Experience with large-scale biobank and genomics-linked RWD platforms (UK Biobank, FinnGen, All of Us), with a track record of building RWD infrastructure that directly influenced drug acquisition, licensing, or portfolio decisions
• Familiarity with additional biomedical data modalities (scientific literature mining, -omics datasets, molecular data integration) and with data science/analytics methodologies applied to RWD (causal inference, trial simulation, propensity score methods)
• Background transitioning data infrastructure from research/ad hoc to production-grade systems in regulated environments
• Experience working at the intersection of data engineering, data science, and business strategy in pharma/biotech

Compensation and Benefits

• Total Compensation Range: $204,500 - $267,000
• Equity, comprehensive benefits, and generous perks