Associate Director: Data Engineering Lead - Lillydirect Platform

Eli Lilly Eli Lilly · Pharma · United States · Remote

The Associate Director – Patient Data Engineering Lead will own the data engineering foundation for Lilly's direct-to-patient pharmacy platform (LillyDirect). This role is responsible for designing and delivering patient analytics data products, integrating data flows, and ensuring HIPAA and SPI consent governance. The role involves building scalable ingestion pipelines, implementing tokenization and identity resolution, and delivering semantic layers for downstream BI, reporting, and agentic AI use cases. The ideal candidate has deep data engineering expertise, a strong command of healthcare privacy regulations, and experience enabling trusted patient analytics.

What you'd actually do

  1. Lead the design, development, and delivery of patient analytics data products supporting LillyDirect pharmacy: patient journey tracking, prescription initiation, dispensing fulfillment, and program engagement analytics.
  2. Integrate LillyDirect data flows into the consumer first-party data ecosystem, ensuring patient analytics align with and extend the broader consumer engagement platform and 1PD data standards.
  3. Architect HIPAA-compliant data infrastructure for the LillyDirect platform — including HIPAA authorization capture, SPI consent management, enterprise vs. LillyDirect consent scoping, and revocation propagation across all integrated systems and journeys.
  4. Design and implement tokenization and identity resolution pipelines linking prescription, dispensing, and intake pharmacy partner data to enable end-to-end, privacy-governed patient journey analytics.
  5. Build scalable, reliable data ingestion pipelines for pharmacy partner data (C5/LDP, intake pharmacy feeds); define data quality standards and validation processes for enterprise-scale patient datasets.

Skills

Required

  • SQL
  • Python
  • cloud-native data engineering tools (ex. Databricks, Delta Lake, Apache Spark)
  • enterprise data warehouse platforms (ex. Snowflake, Redshift, or equivalent)
  • AWS or Azure

Nice to have

  • data ingestion pipelines for complex, multi-source data environments
  • data modeling
  • database design
  • HIPAA regulations and healthcare data privacy requirements
  • consent management or SPI governance frameworks
  • patient data models such as OMOP/Common Data Model
  • patient analytics or healthcare data ecosystems: prescription, dispensing, intake pharmacy, or medical claims data
  • tokenization and identity resolution
  • semantic modeling
  • data dictionaries

What the JD emphasized

  • HIPAA authorization and SPI (Sensitive Personal Information) consent governance
  • HIPAA-compliant data infrastructure
  • HIPAA authorization frameworks, SPI consent management, and consent revocation propagation