GraphDB / Knowledge Graph Engineer

Phase: Initial phase for design, expanding in later phases

Required experience: 6+ years in data engineering, with 3+ years hands-on GraphDB / knowledge graph work.

Role summary

Designs and builds the graph layer that models asset topology and ontology. Turns the connected structure of assets, systems, and relationships into a queryable knowledge graph that complements the lakehouse and enables richer downstream use cases.

Key responsibilities

• Design the graph data model for asset topology and ontology.
• Build and populate the knowledge graph from integrated source data.
• Define ontologies and relationships that reflect the physical and operational asset estate.
• Provide graph query interfaces for insights and downstream applications.
• Collaborate with the architect and data engineers to keep the graph in sync with the lakehouse.
• Prepare the graph layer to support agentic and advanced analytics use cases in later phases.

Must-have skills and experience

• Hands-on GraphDB / knowledge graph experience (Neo4j, TigerGraph, or similar).
• Ontology and semantic modeling experience.
• Strong graph query skills (Cypher, Gremlin, SPARQL, or equivalent).
• Ability to translate asset and topology data into a coherent graph model.
• Comfort integrating graph stores with a broader data platform.

Nice to have

• Experience modeling industrial assets, facilities, or building topology.
• Familiarity with RDF / OWL and reasoning.
• Exposure to graph-powered analytics or agentic use cases.

Relevant stack

GraphDB (Neo4j / TigerGraph or similar), Cypher / Gremlin / SPARQL, RDF / OWL, integrated with Azure / Snowflake data platform.

General attributes

• Proactive and self-driven, able to take ownership and move work forward without waiting to be told.
• AI-enabled in day-to-day work, comfortable using AI tools and copilots to accelerate delivery and quality.
• Strong self-learner who stays current with evolving tools, platforms, and practices.
• Good team player who collaborates well across engineering, operations, and stakeholder groups.

Required Skills

Graph query languages Neo4j, TigerGraph, or similar Ontology and semantic modeling Cypher, Gremlin, or SPARQL

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