Associate Director of RWD Engineering

Purpose

The RWD Data Engineering is a technical role that supports the end‑to‑end engineering vision for Lilly’s real‑world data (RWD) infrastructure. These individuals design and execute the scalable, cloud‑native pipelines and data products that allow HEOR, SDIA, Statisticians, Medical, and Clinical teams to generate evidence faster, more reproducibly, and at greater scientific depth than is possible through traditional vendor engagements. This position supports the creation of sophisticated data engineering products, automates data ingestion and product creation processes, and leads special projects for Global Medical Affairs – Health Economics and Outcomes functions and the broader enterprise.

It also identifies and advocates standard processes across the data asset lifecycle, working closely with other data domain and analytics leaders. Collaboration with multi‑functional teams guides the technical implementation of data products, ensuring scalability, reliability, and performance. The ideal candidate possesses deep expertise in data engineering, strong problem‑solving skills, and a passion for leveraging data to drive business outcomes. This role reports to the Director – RW Data Engineering and works closely with HEOR Central and the BIA organization.

• Lead the design, development, and implementation of cloud‑native data products and high‑throughput data pipelines that transform raw real‑world data into scalable, reliable, analysis‑ready assets supporting analytics, reporting, and evidence generation.
• Lead the Analytic Data Products Strategy to deliver key data assets that enable streamlined, compliant execution and analytics.
• Own the end‑to‑end lifecycle of RWD data products, from requirements gathering and prototyping through production deployment and optimization, ensuring scalability, reliability, performance, and reproducibility across cloud environments (e.g., Databricks, AWS S3, Azure Data Lake).
• Build, optimize, and maintain ETL/ELT ingestion and transformation pipelines for large‑scale, multi‑modal RWD — including claims, complex EHR data, and other linked healthcare datasets — handling data volumes ranging from tens of millions to billions of records.
• Implement and manage lakehouse‑style data architectures (e.g., medallion bronze/silver/gold patterns) using Databricks and cloud object storage (AWS S3, ADLS) to produce versioned, partitioned, and audit‑ready data assets.
• Write and maintain reusable, version‑controlled transformation logic incorporating healthcare coding and terminology standards (e.g., ICD‑10/ICD‑9, NDC, RxNorm, SNOMED, CPT/HCPCS, LOINC) to produce domain‑level datasets such as demographics, diagnoses, treatments, procedures, encounters, and labs.
• Optimize SQL and distributed processing workloads (e.g., Spark‑based jobs) for performance across very large datasets, applying partitioning, indexing, predicate pushdown, denormalization, and other optimization strategies appropriate to analytical workloads.
• Translate analytic, business, and research requirements into reproducible data extraction and transformation logic, supporting cohort construction, temporal logic, and consistent reuse of RWD across teams.
• Apply deep understanding of healthcare data structures and standards when engineering data products, ensuring datasets are fit for purpose for downstream analytics and compliant with scientific, regulatory, and audit expectations.
• Establish and implement standard engineering practices and methodology across the data asset lifecycle, including automated data ingestion, data quality checks, integrity testing, validation, monitoring, alerting, and documentation from source table to analysis‑ready output.
• Contribute to CI/CD pipeline setup, code review, and testing standards, ensuring all transformation code is version‑controlled, tested, and deployable in a reproducible manner.
• Collaborate closely with multi‑functional partners – data scientists, statisticians, analytics leaders, and other technical teams – to understand business and technical requirements and develop documentation of RWD engineering standards, transformation templates, code list…