Advisor - Data Architect, Data Foundry
Eli Lilly and Company
August 23, 2026
On-site
San Francisco, CA
$151,500 - $222,200 USD yearly
IT
Responsibilities
- Design and implement data models, schemas, and ontologies for chemical, biological, and automation-generated data.
- Define/maintain controlled vocabularies, metadata standards, and FAIR-compliant frameworks.
- Implement semantic standards (RDF, OWL, SPARQL) and ontology engineering for interoperable, machine-readable data.
- Design and implement lakehouse architecture (Databricks, Snowflake, or equivalent), including storage patterns, partitioning, and query optimization.
- Build/optimize ETL/ELT pipelines (Spark, dbt, or similar) to produce analytical and ML-ready data.
- Implement real-time/streaming integration (Kafka, Kinesis, event-driven patterns) connecting LIMS, instruments, and lab automation.
- Build knowledge graphs (Neo4j, Amazon Neptune, TigerGraph) capturing molecular/target/pathway/experimental relationships.
- Architect specialized systems (TileDB arrays, MongoDB document stores, vector DBs for ML/RAG) and enable cross-data querying/traversal.
- Partner with scientific software engineers, Methods4Insight, and Tech@Lilly; support build-vs-buy decisions.
Required Qualifications
- M.S. or PhD in CS, Data Science, Bioinformatics, Computational Biology, Information Science, or related STEM.
- Data architecture/engineering/scientific informatics: MS 6+ years; PhD 2+ years.
- Deep expertise in one focus area: data modeling/ontologies, lakehouse/data platform (Databricks/Snowflake/Spark), or knowledge graphs/specialized DBs (Neo4j/Neptune/MongoDB/TileDB).
Preferred Qualifications
- SQL; familiarity with multiple database paradigms.
- Life-sciences/pharma data/workflow knowledge.
- Cloud (AWS/Azure/GCP) and integration patterns.
- Semantic web: RDF/OWL/SPARQL/Protégé.
- Graph DB/knowledge graph design patterns.
- Lakehouse (Delta Lake/Unity Catalog), Spark/dbt.
- Streaming/event-driven (Kafka/Kinesis/Flink).
- LIMS/ELN (e.g., Benchling) and instrument integration.
- Vector DBs for ML/RAG; array DBs (TileDB/Zarr).
- FAIR/Data Readiness Level; chemistry (InChI/SMILES) or biology (GO/UniProt) standards.
- Performance-critical programming (C/C++/Rust) and HPC I/O patterns.
Benefits (explicitly stated)
- Company bonus eligibility (depending on performance).
- Comprehensive benefits: 401(k), pension, vacation, medical/dental/vision/prescription, flexible benefits, life insurance, time off/leave, well-being benefits.
- Design and implement data models, schemas, and ontologies for chemical, biological, and automation-generated data.
- Define/maintain controlled vocabularies, metadata standards, and FAIR-compliant frameworks.
- Implement semantic standards (RDF, OWL, SPARQL) and ontology engineering for interoperable, machine-readable data.
- Design and implement lakehouse architecture (Databricks, Snowflake, or equivalent), including storage patterns, partitioning, and query optimization.
- Build/optimize ETL/ELT pipelines (Spark, dbt, or similar) to produce analytical and ML-ready data.
- Implement real-time/streaming integration (Kafka, Kinesis, event-driven patterns) connecting LIMS, instruments, and lab automation.
- Build knowledge graphs (Neo4j, Amazon Neptune, TigerGraph) capturing molecular/target/pathway/experimental relationships.
- Architect specialized systems (TileDB arrays, MongoDB document stores, vector DBs for ML/RAG) and enable cross-data querying/traversal.
- Partner with scientific software engineers, Methods4Insight, and Tech@Lilly; support build-vs-buy decisions.
Required Qualifications
- M.S. or PhD in CS, Data Science, Bioinformatics, Computational Biology, Information Science, or related STEM.
- Data architecture/engineering/scientific informatics: MS 6+ years; PhD 2+ years.
- Deep expertise in one focus area: data modeling/ontologies, lakehouse/data platform (Databricks/Snowflake/Spark), or knowledge graphs/specialized DBs (Neo4j/Neptune/MongoDB/TileDB).
Preferred Qualifications
- SQL; familiarity with multiple database paradigms.
- Life-sciences/pharma data/workflow knowledge.
- Cloud (AWS/Azure/GCP) and integration patterns.
- Semantic web: RDF/OWL/SPARQL/Protégé.
- Graph DB/knowledge graph design patterns.
- Lakehouse (Delta Lake/Unity Catalog), Spark/dbt.
- Streaming/event-driven (Kafka/Kinesis/Flink).
- LIMS/ELN (e.g., Benchling) and instrument integration.
- Vector DBs for ML/RAG; array DBs (TileDB/Zarr).
- FAIR/Data Readiness Level; chemistry (InChI/SMILES) or biology (GO/UniProt) standards.
- Performance-critical programming (C/C++/Rust) and HPC I/O patterns.
Benefits (explicitly stated)
- Company bonus eligibility (depending on performance).
- Comprehensive benefits: 401(k), pension, vacation, medical/dental/vision/prescription, flexible benefits, life insurance, time off/leave, well-being benefits.