Advisor - Data Architect, 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.

Location:

San Francisco, CA

Reports to:

Lead, Data Architecture (R9), Architecture4

Insight

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 optimized 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 (data infrastructure and scientific software), Methods4

Insight (analytical and computational methods), Automation & Scale4

Insight (lab automation and agentic workflows), and Preparedness4

Insight (data governance and readiness). These pillars empower every Lilly scientist to make optimal decisions by providing seamless access to data, insights, and AI-driven capabilities—serving both human scientists and autonomous AI agents.

We are seeking Data Architects at multiple levels to design and build the data infrastructure that makes AI-native drug discovery possible. You will create the schemas, ontologies, data models, knowledge graphs, and platform architectures that transform raw scientific data into machine-actionable, FAIR-compliant, insight-ready assets—serving both discovery scientists and autonomous AI agents. This role is the foundation of Architecture4

Insight. Everything the software engineering team builds—pipelines, APIs, prototypes—depends on the data models and platform architecture this team designs. You will work with deep knowledge of scientific data (chemical, biological, HTE, automation-generated) to create custom-fit solutions, then partner with Tech@Lilly to scale and maintain them. The role spans three focus areas depending on expertise: data modeling & ontologies, data platform & lakehouse architecture, and knowledge graph & specialized data systems.

You will independently design schemas, select technologies, and make build-vs-buy recommendations for their domain.

Design and implement data models, schemas, and ontologies for chemical, biological, and automation-generated data that serve discovery workflows across the portfolio. Define and maintain controlled vocabularies, metadata standards, and FAIR-compliant data frameworks in partnership with Preparedness4

Insight. Implement semantic data standards (RDF, OWL, SPARQL) and ontology engineering practices to create interoperable, machine-readable scientific data.

Design and implement data lakehouse architecture using modern platforms (Databricks, Snowflake, or equivalent), including data storage patterns, partitioning strategies, and query optimization. Build and optimize ETL/ELT pipelines using Spark, dbt, or similar tools to transform raw scientific data into analytical and ML-ready formats. Implement real-time and streaming data integration (Kafka, Kinesis, event-driven patterns) connecting LIMS, instruments, and lab automation systems to the data infrastructure.

Design and implement knowledge graphs (Neo4j, Amazon Neptune, Tiger Graph) that capture molecular, target, pathway, and experimental relationships across the discovery landscape. Architect specialized data solutions: array databases (TileDB) for genomics/imaging, document stores (MongoDB) for experimental records, and vector…