Description

Own the strategic direction and technical health of graph infrastructure for biomedical research and clinical data.

Responsibilities

• Conduct health checks on current graph infrastructure (AnzoGraph, Neo4j, or Stardog) to assess stability and scalability.
• Evaluate existing RDF/OWL or Property Graph schemas against industry standards like MeSH, SNOMED, or UMLS.
• Identify bottlenecks in ingestion pipelines and optimize complex query execution using Cypher or SPARQL.
• Define a strategic roadmap including build-vs-buy decisions, cloud migration strategies, and GraphRAG integration with LLMs.
• Translate technical graph concepts into value-driven insights for non-technical stakeholders in Research and Clinical teams.

Required Skills

• 10+ years of experience with Graph Databases, specifically deep proficiency in Labeled Property Graphs (LPG) or RDF/Triple Stores.
• 15+ years of total professional experience in enterprise architecture or data engineering.
• Proven experience handling biomedical data types, including Gene-Disease associations, Chemical compounds, and Patient journeys.
• Strong understanding of Linked Data principles, URI strategies, and ontology modeling.
• Experience building ETL/ELT pipelines feeding graphs from unstructured sources (PDF publications) and structured sources (EDC, LIMS).
• Proficiency in Python and Java.
• Expertise in querying languages: SPARQL, Cypher, or Gremlin.
• Experience implementing Graph Data Science algorithms (centrality, community detection) or integrating graphs with Machine Learning.

Preferred Skills

• Familiarity with Bio-Ontologies such as OBO Foundry, ChEMBL, or Ensembl.
• Specific experience with AnzoGraph, Neo4j, or Stardog.

Key Skills

Education

Bachelor's degree