Senior Director – Data; RWD Architect - Engineer

Purpose

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.

• 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.
• Lead 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 repositories, and pipeline performance guidelines.
• Provide technical consultation to collaborators on appropriate use of data products and underlying RWD assets, including structural limitations of specific data sources, join…