Description

Lead the assessment, optimization, and strategic roadmap for biomedical graph infrastructure.

Responsibilities

• Conduct health checks on existing graph infrastructure (AnzoGraph, Neo4j, or Stardog) and evaluate schema scalability.
• Identify bottlenecks in ingestion pipelines and complex query execution using Cypher and SPARQL.
• Define strategic roadmaps including build-vs-buy decisions, cloud migration, and GraphRAG integration.
• Translate technical graph concepts into value-driven insights for Research and Clinical stakeholders.

Required Skills

• 10+ years of experience with Graph Databases (LPG or RDF/Triple Stores).
• Deep proficiency in Python and Java.
• Strong command of SPARQL and Cypher query languages.
• Experience with AnzoGraph, Neo4j, or Stardog.
• Proven track record handling biomedical data (Gene-Disease, Chemical compounds, Patient journeys).
• Expertise in ETL/ELT pipelines for structured (EDC, LIMS) and unstructured (PDF) sources.
• Strong understanding of Linked Data principles, URI strategies, and ontology modeling.

Preferred Skills

• Experience implementing Graph Data Science algorithms (centrality, community detection) or integrating Graphs with Machine Learning.
• Familiarity with industry standards like MeSH, SNOMED, or UMLS.

Key Skills

Education

Bachelor's degree