Graph Database Specialist Neo4j, KuzuDB, PostgreSQL-AGE & Ontology E

Job Summary

Graph Database Specialist

hybrid vector + graph retrieval systems

Key Responsibilities

1. Graph Data Modeling & Ontology Engineering

• Design

  property graph schemas

  for Neo4j / KuzuDB / AGE.
• Build

  domain ontologies

  using:
• RDF, RDFS, OWL
  
• SHACL / SKOS (preferred)
  
• Create hierarchical taxonomies, entity classes, relationships, and semantic constraints.
  
• Convert business concepts into precise knowledge graph structures.
  

2. Graph Database Development

• Design and optimize graph data models in:
  

• Neo4j (Cypher)

• KuzuDB

  (Cypher-like DDL and GQL)

• PostgreSQL-AGE

  (openCypher on Postgres)
• Write efficient graph queries, pathfinding, graph projections, and graph algorithms.
  
• Implement indexing, caching, and query-load optimization.
  

3. Knowledge Graph / Enterprise Graph Systems

• Build scalable

  enterprise knowledge graphs

  from structured/unstructured sources.
• Ingest data using ETL, pipelines, or connectors.
  
• Implement:
  
• Graph enrichment
  
• Schema evolution
  
• Multi-source merging & reconciliation
  
• Work with NLP/LLM teams to align embeddings with graph entities.
  

4. Hybrid RAG & GraphRAG (Optional but Highly Preferred)

• Combine vector search (pgvector/FAISS/Chroma/Pinecone) with graph relationships.
  
• Implement hybrid retrieval pipelines:
  
• Vector Graph traversal
  
• Graph LLM reasoning
  
• Build

  GraphRAG pipelines

  to improve factuality & context grounding for LLMs.

5. Integrations & Backend Support

• Build APIs (Python/Node.js) for graph queries.
  
• Integrate graph engines with business systems, dashboards, or AI agents.
  
• Work with LLM engineers to map graph ontologies to agent tools.
  

6. Performance Monitoring & Security

• Configure monitoring for database health, query latency, and graph-specific metrics.
  
• Setup RBAC, encryption, and role-level access controls.
  
• Ensure optimized schema evolution without downtime.
  

7. Documentation & Collaboration

• Document ontologies, graph schema diagrams, node/relationship definitions.
  
• Collaborate with data architects, product managers, and LLM teams.
  
• Conduct workshops to educate internal teams on graph usage & best practices.
  

Required Technical Skills

Graph Databases

• Strong experience with at least two of the following:
  

• Neo4j (Cypher)

• KuzuDB

• PostgreSQL-AGE

  (openCypher on Postgres)

• ArangoDB / TigerGraph

  (optional)
• Deep understanding of:
  
• Nodes, relationships, labels, properties
  
• Graph projections & graph algorithms
  
• Query planning & graph indexes
  

Ontology & Semantic Web Technologies

• Experience in:
  

• Ontology modeling

  (concepts, classes, relationships)

• RDF, RDFS, OWL

• SHACL validation

• JSON-LD / Turtle / N-Triples

• Ability to design

  semantic schemas

  from business domain knowledge.

Data Engineering & Backend

• Python or Node.js programming.
  
• REST API development (FastAPI/Flask/Express).
  
• Experience with ETL / data pipelines (Airflow, n8n, Pentaho, custom code).
  
• SQL & relational modeling experience.
  

Advanced Skills (Preferred)

• Graph algorithms (PageRank, BFS/DFS, community detection, similarity).
  
• Graph analytics using

  Neo4j Graph Data Science (GDS)

  .
• Experience with:
  
• Vector databases (pgvector, FAISS, Pinecone, Chroma)
  
• Hybrid RAG pipelines
  
• Knowledge extraction from documents using LLMs
  
• Understanding GQL (ISO Graph Query Language) roadmap.
  

Experience Required

• Total Experience:

  38 years

• Graph Database Experience:

  Minimum

  2+ years

  using enterprise graph DBs.

• Ontology Engineering Experience:

  1+ year (preferred but not mandatory).
• Experience with data modeling, backend integration, or data engineering.
  

Preferred Skills

• Knowledge of:
  
• LangChain / LlamaIndex graph integrations
  
• Graph visualization tools (Neo4j Bloom, Arrows, Metaphactory, Linkurious)
  
• Schema versioning & governance
  
• Exposure to graph applications in:
  
• Retail product graphs
  
• Supply chain relationship graphs
  
• Fraud graph analytics
  
• Large enterprise knowledge graph layers