Data Architect – AI-Driven Discovery & Ontologies

BioSpace; San Francisco (CA); On-site; USD 132,000 - 194,000; Full time

Benefits offered by this job

401(k) plan

Pension

Life insurance

Well-being benefits

Job summary

Lilly is seeking Data Architects to design data infrastructure for AI-native drug discovery in San Francisco. You will create schemas, ontologies, data models, and platform architectures transforming raw data into machine-actionable, FAIR-ready assets for discovery scientists and autonomous AI agents.

The role spans data modeling, data platform/lakehouse architecture, and knowledge graph systems, with cross-functional partnerships to scale data solutions and ensure production readiness.

Qualifications

• BS or MS in Computer Science, Data Science, Bioinformatics, Computational Biology, Information Science, or related STEM field; Ph.D. valued for ontology and knowledge graph roles.
• 7 years (BS) or 5 years (MS) of data architecture, data engineering, or scientific informatics experience.
• SQL and experience in multiple database paradigms (relational, graph, document, etc.).
• Authorized to work in the United States on a full-time basis; no visa sponsorship provided.

Responsibilities

• Design and implement data models, schemas, and ontologies for chemical, biological, and automation-generated data.
• Design data lakehouse architecture and ETL/ELT pipelines for ML-ready data.
• Build knowledge graphs and specialized data systems to support ML/RAG workflows.

Skills

SQL skills

Data modeling

Ontology engineering

Knowledge graphs

Education

BS in CS / Data Science

MS in related field

PhD preferred

Tools

Databricks

Snowflake

Spark

dbt

Neo4j

MongoDB

Job description

Lilly is seeking Data Architects to design data infrastructure for AI-native drug discovery in San Francisco. You will create schemas, ontologies, data models, and platform architectures transforming raw data into machine-actionable, FAIR-ready assets for discovery scientists and autonomous AI agents.

The role spans data modeling, data platform/lakehouse architecture, and knowledge graph systems, with cross-functional partnerships to scale data solutions and ensure production readiness.