Join Eli Lilly and Company as a Data Engineering Lead

At Lilly, we unite caring with discovery to make life better for people around the world. As a global healthcare leader headquartered in Indianapolis, Indiana, our 39,000 employees worldwide work to discover and bring life-changing medicines to those in need, improve the understanding and management of diseases, and give back to our communities through philanthropy and volunteerism. We prioritize putting people first and are looking for passionate individuals determined to significantly impact global healthcare.

About the Role: Data Engineering Lead

We are on a mission to transform Lilly’s Quality Management System (QMS). Our multi-year QMS transformation program aims to simplify, standardize, and optimize QMS processes, innovate and grow with new sites and partnerships, and modernize our solution infrastructure by migrating to cloud-based solutions and advanced technologies.

Key Responsibilities

Leading and Strategy

• Lead and mentor a team of data engineers, fostering a culture of collaboration, innovation, and excellence.
• Define and drive the data engineering strategy in alignment with business objectives and technical requirements.
• Collaborate with cross-functional teams including data scientists, analysts, and business stakeholders to understand data requirements and translate them into actionable engineering solutions.

Data Pipeline Development

• Design, develop, and maintain scalable and efficient data pipelines to support data analytics, reporting, and machine learning initiatives.
• Implement best practices for data ingestion, integration, transformation, and storage, ensuring data quality, reliability, and accessibility.
• Automate data pipeline processes to enhance efficiency using tools such as Apache Airflow, Apache Kafka, and AWS Glue.

Data Ingestion and Integration

• Lead the development of data ingestion and integration processes, sourcing data from various internal and external sources.
• Collaborate with stakeholders to define data ingestion requirements for real-time and batch data integration.
• Ensure seamless data flow between systems, optimizing data transfer and transformation processes for performance and scalability.

Technical Expertise

• Stay updated on emerging technologies and trends in data engineering to enhance our data infrastructure continually.
• Provide technical leadership and guidance on data engineering best practices, coding standards, and performance optimization techniques.
• Engage in hands-on data engineering tasks, including coding, debugging, and troubleshooting complex data pipeline issues.

Quality Assurance and Governance

• Establish and enforce data engineering standards, policies, and procedures for data quality, consistency, and compliance.
• Implement monitoring and alerting mechanisms to proactively address data pipeline issues, ensuring minimal disruption to business operations.
• Collaborate with data governance and security teams to enforce data privacy and security measures across the data lifecycle.

Persistent Pod Responsibilities

• Lead support and enhancement of solutions from a persistent pod standpoint.
• Create operations metrics, measure data effectiveness, and drive operational stability of models.
• Work with manufacturing and quality leadership teams to align on operational needs and standards.

Vendor and Partner Engagement

• Collaborate with vendors and partners to leverage external expertise and technologies, ensuring alignment with organizational goals and standards.
• Evaluate and select appropriate vendors and partners to support data and AI initiatives, fostering strong relationships and driving successful outcomes.

What You Should Bring

• Strong communication, leadership, teamwork, project delivery, and problem-solving skills.
• Experience with architectural processes (e.g., blueprinting, reference architecture, governance).
• Knowledge of external data standards (e.g., HL7, CDISC, SDTM) and vocabularies (e.g., MedDRA, RxNorm, SNOMED).
• Skills in data modeling, warehousing, integration, and governance, with an understanding of data security, standards, and cloud architecture principles.
• Experience influencing IT and business strategies to achieve large-scale outcomes.
• Proficient in various data modeling tools like Erwin Data Modeler, ER/Studio, and Lucidchart.
• Experience with data