Description

You will lead the technical strategy and architectural evolution of graph-based data infrastructures.

Responsibilities

• Conduct health checks on current graph infrastructure including AnzoGraph, Neo4j, or Stardog.
• Evaluate RDF/OWL or Property Graph schemas for scalability and alignment with industry standards.
• Optimize ingestion pipelines and complex query execution using Cypher or SPARQL.
• Define strategic roadmaps covering build-vs-buy decisions, cloud migration, and LLM integration via GraphRAG.
• Translate technical graph concepts into value-driven insights for Research and Clinical stakeholders.

Required Skills

• 10+ years of experience working with Graph Databases.
• Deep proficiency in Labeled Property Graphs (LPG) or RDF/Triple Stores.
• Proven experience handling biomedical data types such as Gene-Disease associations and chemical compounds.
• Strong understanding of Linked Data principles, URI strategies, and ontology modeling.
• Experience building ETL/ELT pipelines from unstructured PDF publications and structured EDC/LIMS sources.
• Experience implementing Graph Data Science algorithms or integrating graphs with Machine Learning.
• Proficiency in Python, Java, SPARQL, Cypher, or Gremlin.
• Bachelor's degree.

Preferred Skills

• Familiarity with AnzoGraph, Neo4j, Stardog, or ChEMBL.
• Knowledge of OB0 Foundry, Ensembl, MeSH, SNOMED, or UMLS.

Key Skills

Education

Bachelor's degree