Enterprise Solutions Graph Database/ Architect with AnzoGraph

REQ-00049758Enterprise Solutions Graph Database/ Architect with AnzoGraphMoon Township}Long Term

Duration:

12 Months Seeking a seasoned Graph Database & Knowledge Graph Expert to perform a comprehensive study of our existing platform. Evaluate our current architecture, data ontology, and query performance to provide a strategic roadmap. The goal is to evolve our Knowledge Graph into a robust, scalable engine that accelerates different Pharma areas ( drug discovery, clinical insights, and cross-departmental data democratization).

Key Responsibilities :

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Platform Assessment:

Conduct a "health check" on current graph infrastructure (e.g., AnzoGraph, Neo4j, or Stardog). •

Ontology & Schema Review:

Evaluate existing schemas (RDF/OWL or Property Graph) for scalability and alignment with industry standards like MeSH, SNOMED, or UMLS. •

Performance Optimization:

Identify bottlenecks in ingestion pipelines and complex query execution (Cypher/SPARQL). •

Strategic Roadmap:

Define a "Way Forward" report including recommendations on build-vs-buy decisions, cloud migration, and integration with LLMs (GraphRAG). •

Stakeholder Alignment:

Translate technical graph concepts into value-driven insights for non-technical stakeholders in Research and Clinical teams.

Required Qualifications :

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Graph Expertise:

10+ years of experience with Graph Databases. Deep proficiency in LPG (Labeled Property Graphs) or RDF/Triple Stores. •

Pharma Domain Knowledge:

Proven experience handling biomedical data types (e.g., Gene-Disease associations, Chemical compounds, Patient journeys). •

Semantic Web Standards:

Strong understanding of Linked Data principles, URI strategies, and ontology modeling. •

Data Engineering:

Experience with ETL/ELT pipelines that feed graphs from unstructured (PDF publications) and structured (EDC, LIMS) sources. •

Advanced Analytics:

Experience implementing Graph Data Science algorithms (centrality, community detection) or integrating Graphs with Machine Learning.

Technical Stack :

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Graph DBs:

AnzoGraph (MUST HAVE), Neo4j, Stardog, •

Languages:

Python, Java, SPARQL, Cypher, or Gremlin. •

Bio-Ontologies:

Familiarity with OBO Foundry, ChEMBL, or Ensembl. Less Posted on: 04-07-2026 Posted by: Sumant Sagoi