Sr. Principal Data Engineer - Lakehouse Architecture

Eli Lilly and Company • Us, IN

2h • Onsite

About The Position

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. Tech at Lilly is seeking a highly skilled Senior Data Engineer who can implement and optimize large-scale Lakehouse solutions and drive the evolution of our modern data platform while providing technical leadership to a growing team. The ideal candidate will have hands-on experience with modern data engineering technology stack and a proven track record of leading engineering talent in fast-paced environments.

Requirements

• Proven ability to mentor junior engineers and facilitate knowledge sharing
• Strong project management skills with experience leading multi-functional initiatives
• Coordinate multi-functional projects and ensure effective communication between technical and business teams
• Demonstrated ability to make architectural decisions and drive technical consensus
• Embrace a growth mindset and actively seek opportunities to expand your leadership capabilities and technical mastery
• Knowledge in the pharmaceutical or life sciences domain
• Experience with streaming data technologies (Kafka, Databricks Structured Streaming)
• Familiarity with data cataloging tools
• Familiarity with high performance data service framework (Arrow Flight)
• Expert-level proficiency in Python and SQL for data transformation and pipeline development
• Strong experience with Apache Spark (PySpark or Scala) for big data processing and analytics
• Hands-on experience with cloud platforms (AWS or Azure) and their data services, including S3, Glue, Redshift, IAM, and CloudWatch
• Proficiency with Infrastructure as Code tools (CloudFormation)
• Experience with containerization (Docker, Kubernetes) and orchestration platforms
• Knowledge of data modeling techniques for both analytical and operational workloads
• Hands-on expertise with Databricks: clusters, Delta Lake, Unity Catalog, Workflows, and MLflow integration
• Understanding of data governance, security, and compliance requirements including GxP, HIPAA, or GDPR data frameworks in regulated industries
• Experience orchestrating pipelines with Apache Airflow
• Strong command of dbt for modular, tested, and version-controlled transformations
• Familiarity with data quality testing frameworks such as Great Expectations or dbt tests for schema validation
• Experience with data observability: freshness, volume, schema drift, and anomaly detection (Databricks Lakehouse Monitoring or equivalent)
• Familiarity with open table format interoperability: Delta Sharing or Apache Iceberg
• Knowledge of DataOps practices and data mesh / data product principles
• Exposure to ML platform integration: MLflow experiment tracking, feature stores, or model serving
• Master’s degree in computer science, Engineering, or related technical field
• 3+ years of hands-on experience with Lakehouse architectures (Databricks, Snowflake, or similar)
• 7+ years of overall data engineering experience with large-scale distributed systems, including at least 3 years in a senior or lead capacity

Responsibilities

• Design and implement comprehensive Lakehouse architecture solutions using technologies like Databricks and Snowflake platforms
• Define and carry out medallion architecture standards (Bronze / Silver / Gold) across data domains, ensuring data quality, lineage, and discoverability
• Lead Unity Catalog governance design: schemas, access control policies, and data contracts
• Build and maintain real-time and batch data processing systems using Apache Spark (PySpark/Scala), Kafka, and Databricks Structured Streaming, and similar technologies
• Architect scalable data pipelines that handle structured, semi-structured, and unstructured data to deliver AI ready data.
• Develop data transformation workflows using tools like DBT, Airflow, or Databricks
• Implement data governance frameworks, including data quality monitoring, lineage tracking, data time travel and security protocols.
• Build data pipeline testing frameworks: unit tests, data quality assertions (Great Expectations / dbt tests), and schema validation
• Define and publish data SLAs/SLOs in collaboration with data product owners; own incident response and root-cause analysis for pipeline failures
• Drive adoption of modern data engineering standard processes including Infrastructure as Code, CI/CD, and automated testing
• Collaborate with data scientists, analysts, and business collaborators to translate requirements into robust technical solutions
• Mentor a team of 3-5 data engineers
• Foster a collaborative team culture focused on continuous learning and innovation

Benefits

• company bonus
• company-sponsored 401(k)
• pension
• vacation benefits
• medical, dental, vision and prescription drug benefits
• flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts)
• life insurance and death benefits
• certain time off and leave of absence benefits
• well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities)

What This Job Offers

• Job Type: Full-time
• Career Level: Senior