Advisor - Data Architect, Data Foundry

Location

San Diego, CA;
San Francisco, CA;
Boston, MA;
Louisville, CO;
Indianapolis, IN

Lead, Data Architecture (R9), Architecture4

Insight

Lilly is a global healthcare leader headquartered in Indianapolis, Indiana, focused on discovering and delivering life‑changing medicines.

The Data Foundry is a multidisciplinary team within Discovery Technology and Platforms (DTP) enabling AI‑native drug discovery through four pillars:
Architecture4

Insight, Methods4

Insight, Automation & Scale4

Insight, and Preparedness4

Insight.

We are seeking Data Architects at multiple levels to design and build the data infrastructure that enables AI‑native drug discovery. The role builds schemas, ontologies, data models, knowledge graphs, and platform architectures for scientific data.

• 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 databases for embedding‑based retrieval supporting ML and RAG workflows.
• Build query and traversal patterns that enable scientists and AI agents to ask relational questions across the entire data landscape.

• Partner with scientific software engineers to ensure data architectures are implementable, performant, and well‑documented.
• Collaborate with Methods4
  
  Insight to design data structures that support analytical model training, deployment, and evaluation.
• Work with Tech@Lilly to define scaling strategies, ensure enterprise compliance, and transition data architectures to production‑grade management.
• Contribute to build‑versus‑buy‑versus‑adopt decisions by evaluating commercial and open‑source data platforms against Data Foundry requirements.

• M.S. or PhD in Computer Science, Data Science, Bioinformatics, Computational Biology, Information Science, or related STEM field
• MS (with 6+ years) and PhD (with 2+ years) of data architecture, data engineering, or scientific informatics experience.
• Deep expertise in at least one of the focus areas: relational databases, data modeling and ontology engineering, data platform and lakehouse architecture (Databricks, Snowflake, Spark), or knowledge graph and specialized database systems (Neo4j, Neptune, MongoDB, TileDB).

• Working familiarity with multiple database paradigms — relational, graph, document, columnar, key‑value — and strong SQL skills.
• Understanding of scientific data types and experimental workflows in life sciences or pharma (chemical, biological, HTE data).
• Strong communication skills with ability to translate data architecture concepts for both technical and scientific audiences.
• Familiarity with cloud platforms (AWS, Azure, or GCP) and modern data integration patterns.
• Pharmaceutical or biotech research industry experience, particularly in discovery data management or research informatics.
• Experience…