Associate Director: Data Engineering Lead - LillyDirect Platform

Associate Director – Data Engineering Lead - LillyDirect Platform

You will join a growing team of business intelligence and analytics professionals focused on Lilly's first-party patient data initiatives. The Associate Director – Patient Data Engineering Lead will own the data engineering foundation for LillyDirect, Lilly's direct-to-patient pharmacy platform.  

This role is responsible for designing and delivering patient analytics data products that span prescription initiation, dispensing fulfillment, and end-to-end patient journey analytics — integrating LillyDirect data flows into the broader consumer first-party data ecosystem. A critical dimension of this role is ensuring HIPAA authorization and SPI (Sensitive Personal Information) consent governance: from consent capture and preference center architecture to enterprise-level revocation propagation across integrated systems and intake pharmacy partners.  

You will build scalable ingestion pipelines for pharmacy partner data, implement tokenization and identity resolution for linked patient analytics, and deliver semantic layers that enable downstream BI, reporting, and agentic AI use cases.  

This position works closely with the LillyDirect product team, intake pharmacy partners, the consumer first party data platform team, and BIA platform enablement — translating patient analytics requirements into governed, scalable, privacy-first implementations.  

The ideal candidate combines deep data engineering expertise with a strong command of healthcare privacy regulations and a passion for enabling trusted patient analytics at enterprise scale. This position works with the Sr. Director – Consumer Data Engineering. 

Key Objectives: 

• 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. 
• 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. 
• 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. 
• 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. 
• 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. 
• Deliver semantic layers and analytics-ready data products — curated datasets, data dictionaries, and governed access patterns enabling downstream BI reporting, self-service analytics, and agentic AI use cases. 
• Govern data access and privacy controls in coordination with legal, cyber, and privacy teams; own data recertification milestones, Reltio/Cassie integrations, and consent scoping decisions. 
• Collaborate closely with multi-functional teams — LillyDirect product team, intake pharmacy partners, consumer 1PD analytical platform team, and BIA platform enablement — to translate business requirements into technical specifications and deliver governed, high-quality data products. 

Basic Qualifications 

• Bachelor's and/or Master's Degree in Computer Science, Engineering, Statistics, Information Technology, Information Management,

• 5+ years of experience in data engineering, with a focus on developing data products and solutions in healthcare, pharma, or similarly regulated environments. 

• 5+ years of expertise in SQL, Python

• 5+ years of hands-on experience with cloud-native data engineering tools (ex. Databricks, Delta Lake, Apache Spark) and enterprise data warehouse platforms (ex. Snowflake, Redshift, or equivalent), with exposure to large-scale patient or clinical datasets.

• Experience with cloud platforms such as AWS or Azure

• Qualified applicants must be authorized to work in the United States on a full-time basis. Lilly will not provide support for or sponsor work authorization or visas for this role, including but not limited to F-1 CPT, F-1 OPT, F-1 STEM OPT, J-1, H-1B, TN, O-1, E-3, H-1B1, or L-1

Additional Information 

• 0-25% travel depending on business need 

Additional Skills/Preferences 

• Experience designing and executing data ingestion pipelines for complex, multi-source data environments.
• Solid understanding of data modeling and database design.
• Knowledge of HIPAA regulations and healthcare data privacy requirements; experience with consent management or SPI governance frameworks preferred.
• Familiarity with patient data models such as OMOP/Common Data Model or equivalent preferred.
• Experience with patient analytics or healthcare data ecosystems: prescription, dispensing, intake pharmacy, or medical claims data. 
• Deep understanding of HIPAA authorization frameworks, SPI consent management, and consent revocation propagation across integrated systems and CRM/marketing platforms. 
• Tokenization and identity resolution experience for linking first-party and third-party patient data at enterprise scale. 
• Semantic modeling and data dictionary development experience (Power BI, Microsoft Fabric, or equivalent BI platforms). 
• Familiarity with Databricks Unity Catalog, Delta Lake, and lakehouse architecture patterns. 
• Consistent record of leading complex, multi-stakeholder data engineering projects in regulated or compliance-driven environments. 
• Knowledge of DevOps practices and CI/CD tools; GitHub-based version control and analytics scripting workflows. 
• Experience with Agile/Scrum methodologies and project management tools such as JIRA. 
• Ability to bridge technical and non-technical stakeholders, translating patient analytics requirements into scalable, privacy-first technical implementations

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