Enterprise Solutions Graph Database/ Architect

Job title : Enterprise Solutions Graph Database/ Architect

Location : Upper Providence Township, PA (Onsite)

Duration : 12+ Months

Experience : 15 Years

Role Overview

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

• 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
• 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 Preferences

• Graph DBs: AnzoGraph, Neo4j, Stardog,
• Languages: Python, Java, SPARQL, Cypher, or Gremlin.
• Bio-Ontologies: Familiarity with OBO Foundry, ChEMBL, or Ensembl.