Senior Director – Data; RWD Architect - Engineer

Director RWD Data Engineering

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve.

This is hard, urgent, selfless work—but it's work worth doing. If you're driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us.

The Director RWD Data Engineering is a hands-on technical role that leads the end-to-end engineering vision for Lilly's real-world data (RWD) infrastructure. This individual leads design and executes 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 job involves a depth of understanding of the multi-modal RWD ecosystem across Lilly's therapeutic areas to contextualize and drive RWD to the necessary end-user data products by leading the creation of sophisticated data engineering products, creating processes for automation of data ingestion and product creation, and leading special projects for Global Medical Affairs – Health Economics and Outcomes functions and the broader enterprise.

Further, this position will be responsible for identifying and advocating standard processes across the data asset lifecycle, working closely with other data domain and analytics leaders. Collaborating closely with multi-functional teams, you will lead 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 position reports to HEOR Central and is embedded within the BIA organization and works in close partnership with HEOR, SDIA, Statisticians, Medical, and Clinical teams.

This job description is intended to provide a general overview of the job requirements at the time it was prepared. The job requirements of any role/position can change over time and can include additional responsibilities not specifically described in the job description. Consult with your supervisor regarding your actual job responsibilities and any related duties that might be required for the role/position.

• 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…