Data Engineer

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.  Lilly is seeking a highly motivated and skilled Data Engineer to join our innovative team at Eli Lilly and Company. This role involves designing, building, and maintaining robust and scalable data pipelines and infrastructure to support our critical scientific and business initiatives, ultimately contributing to the discovery and development of life-changing medicines.

Data Engineering & Pipeline Development • Design, develop, and optimize scalable data pipelines using Databricks, PySpark, Python, SQL, and Delta Lake to ingest, transform, and load data from diverse sources into data warehouses and data lakes. • Build scalable, efficient Databricks pipelines implementing canonical data models across the medallion architecture (Bronze → Silver → Gold), scoped entirely within the CE trust boundary. • Evaluate and apply Databricks capabilities and integration patterns — Unity Catalog, Delta Lake, Databricks Workflows, serverless compute, Lakebase, and ingestion connectors — selecting the right tool for each pipeline given performance, cost, and scalability constraints. • Implement and maintain ELT/ETL workflows using Databricks Workflows, Auto Loader, Structured Streaming, and Delta Live Tables (DLT). • Build and maintain CI/CD pipelines (GitHub Actions, Git-based promotion dev → test → prod) for CE data, contract, policy, and agent artifacts. • Automate data ingestion and product creation to reduce manual pipeline maintenance and onboarding time for new CE data sources. Data Governance, Quality & Security • Implement and manage data governance policies, ensuring data quality, integrity, security, and compliance with regulatory requirements (e.g., GxP, HIPAA) and covered-entity constructs. • Implement row/column-level security, masking, and tokenization boundaries so PHI isolation is enforced at the platform layer, in partnership with the Policy-as-Code Engineer's OPA/Rego policies. • Support implementation of data governance capabilities including metadata management, lineage, and access control using Unity Catalog. • Establish and implement data quality, testing, and validation methodology (pytest, DLT/Great Expectations); build monitoring and alerting to proactively catch and resolve pipeline and data issues. Data Modeling & Architecture • Develop and maintain data models, schemas, and metadata for efficient data storage and retrieval. • Design data solutions following Lakehouse and Medallion Architecture (Bronze, Silver, Gold) design principles. • Develop reusable data transformation frameworks and automated data quality checks. • Partner with the CE Data Architect on reference architecture and patterns, providing implementation feedback that keeps designs buildable and performant at scale. • Develop and maintain documentation for data architecture, pipelines, and processes. Collaboration & Innovation • Collaborate with data scientists, analysts, architects, and business stakeholders to understand data requirements and translate them into scalable technical solutions. • Monitor data pipeline performance, troubleshoot issues, and implement solutions to ensure high availability and reliability. • Participate in code reviews, contribute to architectural discussions, and promote best practices in data engineering. •