Associate Director - Data Engineering Lead

At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We’re looking for people who are determined to make life better for people around the world.

Data Engineering Lead

Lilly’s Purpose  
At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our 39,000 employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We’re looking for people who are determined to make life better for people around the world. 

Come help us transform Lilly’s Quality Management System!
Lilly has initiated a multi-year QMS transformation program that will enable 1) simplified, standardized, and optimized QMS processes, 2) innovation and agility for unprecedented growth with new sites, modalities, and partnerships, and 3) modernization of solution infrastructure by moving to cloud-based solutions and advanced technology.  The MQ Tech at Lilly Global Quality team is actively looking for a Data Architect to join the NextGen QMS Program.  

What You’ll Be Doing
You will be responsible for defining, designing, building, and maintaining the data ingestion and integration vision, strategy, and principles for the NextGen QMS Program.  You will define standards for naming, describing, governing, managing, modelling, transforming, and searching data from Lilly’s NG QMS solution.  You will be primarily responsible for developing automated data pipelines. You will be a key contributor to the Integration and Data Workstream of the NextGen QMS Program with a focus on QMS data publication strategy.  

How You’ll Succeed
•    Partner with Lilly architects, software vendor, and third-party implementation partner to develop and execute on a technical strategy for NG QMS data structure and data products.
•    Work with business to identify future uses for Lilly data and anticipated business results and enable processes to support these needs. 
•    Develop and provide expertise in enterprise data domains; this includes data relationships, data quality, understanding of business data needs and the associated technology toolsets and methodologies. 

Leadership 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, aligning 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 improve efficiency and reduce manual intervention, leveraging tools and frameworks 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 and implement solutions for real-time and batch data integration.
• Ensure seamless data flow between systems and applications, optimizing data transfer and transformation processes for performance and scalability.

Technical Expertise:

• Stay abreast of emerging technologies and trends in data engineering, continuously evaluating and adopting new tools and techniques to enhance our data infrastructure.
• Provide technical leadership and guidance on data engineering best practices, coding standards, and performance optimization techniques.
• Hands-on involvement in 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 to ensure data quality, consistency, and complian