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.
- Positive 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