Senior Director - Data Architecture,Data Foundry

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.

Overview Lilly Small Molecule Discovery is purpose-built to create molecules that make life better for people. Discovery Technology and Platforms (DTP) accelerates molecule discovery by building foundational platforms, streamlining lab operations through advanced technologies and data connectivity, and investing in novel capabilities. Data Foundry is a multidisciplinary team within DTP that enables AI-native drug discovery through four integrated pillars:
Architecture4

Insight, Methods4

Insight, Automation & Scale4

Insight, and Preparedness4

Insight. Together, these pillars provide the scientific foundation, data assets, analytical capabilities, and automation frameworks that empower both researchers and AI systems to accelerate molecular discovery.

Position Summary We are seeking a Senior Director, Data Architecture to define and drive the scientific information architecture that powers AI-native drug discovery across Molecular Discovery. As a senior leader within Architecture4

Insight, you will establish the architectural vision, standards, and roadmap that transform scientific data into a strategic asset for discovery scientists, computational researchers, and autonomous AI agents. You will provide technical leadership across the Data Architecture capability, shaping scientific data platforms, ontologies, metadata frameworks, knowledge graphs, and AI-ready data ecosystems. As a player-coach, you will lead a small team of architects while remaining deeply engaged in the most critical data architecture decisions across Molecular Discovery.

This role requires deep expertise in scientific data architecture, strong technical leadership, and the ability to influence strategy across organizational boundaries. Success will be measured by the quality, adoption, scalability, and scientific impact of the data architectures that enable Molecular Discovery.

Scientific Information Architecture Define and drive the scientific information architecture strategy and implementation. Establish standards and architectural principles for data modeling, metadata management, semantic interoperability, ontology governance, and FAIR data implementation. Develop roadmaps for scientific data platforms, metadata capabilities, knowledge systems, and AI-ready data assets.

Technical Leadership & Influence Serve as a senior technical leader and architecture authority across Data Foundry initiatives. Mentor architects, engineers, and scientific informatics professionals. Personally contribute to architecture design, technology evaluation, and solution development for high-priority initiatives.

People Leadership & Technical Coaching Lead a team of Data Architects supporting scientific information architecture across Molecular Discovery. Foster a culture of technical excellence, innovation, and scientific partnership. Promote architectural consistency, knowledge sharing, and adoption of best practices across Architecture4

Insight.

Architecture Stewardship Develop and maintain reference architectures, design patterns, and architectural standards. Measure and improve platform interoperability, metadata quality, discoverability, and architecture adoption across Molecular Discovery. Help shape the long-term evolution of Data Foundry's scientific data ecosystem.

Data Modeling, Ontologies & Semantic Architecture Enable unified discovery and exploration of scientific knowledge across previously disconnected systems and data sources including molecules, targets, pathways, assays, experiments,…