What are the responsibilities and job description for the Enterprise Solutions Graph Database/ Architect position at TechSpace Solutions Inc.?
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.