Advisor - Data Architect, Data Foundry
Data Modeling & Ontologies
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 Preparedness4Insight.
Implement semantic data standards (RDF, OWL, SPARQL) and ontology engineering practices to create interoperable, machine-readable scientific data.
Data Platform & Lakehouse Architecture
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.
Knowledge Graph & Specialized Data Systems
Design and implement knowledge graphs (Neo4j, Amazon Neptune, TigerGraph) 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.
Cross-Functional Partnership
Partner with scientific software engineers to ensure data architectures are implementable, performant, and well-documented.
Collaborate with Methods4Insight 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.