Sr Principal Engineer - Data

Lilly • Indianapolis, IN

4d • $129,000 - $211,200 • Onsite

About The Position

Lilly is seeking an experienced Senior Principal Data, Analytics & AI Engineer to join the Global DIA team within MQ Tech. The role combines data engineering, supply chain expertise, and applied AI. The engineer will architect near-real-time data foundations for AI agents and lead the design of agents and multi-agent workflows that connect information across manufacturing and supply chain systems to provide actionable insights for operational teams.

This is a hands-on delivery role with technical leadership responsibilities. The engineer will own solutions from problem framing through production support, set technical direction for supply chain and manufacturing data modernization and agentic AI, and mentor other engineers. Solutions will support Lilly Supply Chain and Manufacturing operations in production.

The position is located in Indianapolis, Indiana, and is onsite five days per week. The anticipated wage is $129,000–$211,200. Full-time equivalent employees are eligible for a company bonus, depending in part on company and individual performance. Lilly offers eligible employees a comprehensive benefits program, including participation in a company-sponsored 401(k), pension, vacation benefits, medical, dental, vision and prescription drug benefits, flexible benefits such as healthcare and dependent-care flexible spending accounts, life insurance and death benefits, certain time-off and leave benefits, and well-being benefits.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Data Science, Statistics, Supply Chain, or a related field, with 8+ years in data engineering, analytics, or AI/ML and hands-on production GenAI/agentic application delivery.
• Experience with supply chain or manufacturing data and hands-on SAP (S/4HANA/ECC, BW/4HANA), SHARP, MES, and related data and analytics.
• Hands-on advanced statistical, machine-learning, LLM, and agentic application development, including prompt engineering, tool/function calling, RAG, vector search/embeddings, and multi-step workflows.
• Deep knowledge of agent frameworks such as LangChain, LangGraph, or equivalent, and production LLM APIs such as OpenAI, Azure OpenAI, Anthropic, or Bedrock.
• Experience with Azure, AWS, or Databricks.
• Advanced Python; SQL, Java/Scala/R, Power BI/Tableau, and web applications are listed as additional skills or a plus.
• Experience developing production-grade analytics systems end to end.

Nice To Haves

• Master’s degree in Computer Science, Engineering, Data Science, Statistics, Supply Chain, or a related field, with 5+ years in data engineering, analytics, or AI/ML and hands-on production GenAI/agentic application delivery.
• Experience in pharmaceutical, life sciences, or other regulated manufacturing supply chains.
• Track record of taking multiple AI or agentic solutions through production, including evaluation, monitoring, and iteration after go-live.
• Strong command of manufacturing processes and metrics, including OEE, downtime, yield, batch performance, and right-first-time, alongside supply chain metrics.
• Experience with supply chain planning software such as SAP IBP, Kinaxis, or o9, and IoT or streaming operational data.
• Exposure to manufacturing data sources such as OSI PI historians, MES/TrakSYS, PMX, or LIMS.
• Experience in a GxP-regulated environment, including CSV and validation practices.
• Experience with data visualization tools, with Power BI preferred.
• Applied knowledge of responsible AI practices, including model risk, bias, explainability, and data privacy.
• Experience leading globally distributed teams or vendor partners.
• Databricks, AWS, or Azure certifications.

Responsibilities

• Own technical design and architecture for agentic AI and data modernization solutions across the Global Supply Chain and MQ landscape, from problem framing through production support.
• Set standards and reusable patterns for cloud-based data modeling, pipeline design, agent orchestration, and evaluation.
• Mentor and guide junior and mid-level engineers, and lead code and design reviews.
• Partner with senior business stakeholders and IT leadership to shape roadmaps, sequence delivery, make build-versus-buy trade-offs, and direct external vendors and partners.
• Develop and apply working knowledge of Global Supply Chain data domains, including material master, BOM and recipe, procurement, inventory, production, planning, warehouse, transportation, distribution, logistics, quality, and deviations.
• Work across SAP and SHARP, translating source-system semantics into trusted, business-ready data models.
• Apply knowledge of end-to-end supply chain processes and supporting analytics, including service level, inventory turns, cycle time, schedule adherence, and supply risk.
• Correlate supply chain and manufacturing data across ERP, planning, MES, historian, and quality systems to create integrated business solutions.
• Collaborate with business and global teams on agentic solution design, requirements gathering, development, and delivery.
• Design, build, and deploy AI agents and multi-agent workflows for manufacturing and supply chain analysis and decision support, including prompt engineering, tool and function calling, orchestration, and retrieval-augmented generation.
• Define and improve agent performance measurement, including test scenarios, evaluation criteria, accuracy and hallucination monitoring, user feedback loops, observability, and guardrails.
• Implement, test, and deploy production machine-learning and predictive models with attention to performance, scalability, and interpretability.
• Design, build, and maintain scalable, reliable ETL/ELT data pipelines for operational and enterprise data supporting analytics, reporting, and agentic AI.
• Lead migration and re-platforming of legacy supply chain reporting and data assets to modern lakehouse and cloud architectures.
• Monitor and troubleshoot data quality and agent behavior, ensuring data integrity and reliability.
• Document data architecture, data flows, agent designs, prompts, model choices, and deployments to support maintainability and GxP/CSV requirements.
• Ensure compliance with applicable data privacy, security, and responsible AI requirements.
• Stay current with agentic AI and data engineering developments and bring practical advances into the team's work.

Benefits

• Company bonus for eligible full-time equivalent employees, depending in part on company and individual performance.
• Company-sponsored 401(k) participation eligibility.
• Pension.
• Vacation benefits.
• Medical, dental, vision, and prescription drug benefits for eligible employees.
• Flexible benefits, including healthcare and/or dependent-care flexible spending accounts.
• Life insurance and death benefits.
• Certain time-off and leave-of-absence benefits.
• Well-being benefits, including employee assistance, fitness benefits, and employee clubs and activities.

What This Job Offers

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