Databricks - Ontology / Knowledge Graph Engineer

Job Title: Databricks - Ontology / Knowledge Graph EngineerLocation: RemoteOverview: We are building an enterprise-wide ontology and semantic layer to sit on top of our Databricks environment (Unity Catalog / Lakehouse), supporting AI use cases as well as long-term data governance and metadata management. This role extends our existing team driving early research and prototyping, and need an experienced ontology/knowledge graph specialist to validate our direction, mentor the team, and deliver our first production reference use case.ResponsibilitiesReview the ontology approach already in progress and provide expert feedback and course corrections.Help define whether/how the ontology layer should be tightly or loosely coupled to Databricks, given native ontology support is still in preview.Guide tooling decisions across ontology-agnostic frameworks (e.g., LinkML, RDF/OWL, TTL) and knowledge graph technologies (e.g., Neo4j) versus native Unity Catalog approaches.Partner with our internal team to move an existing gold-layer prototype into a Databricks-native sandbox Build out a reference use case to prove out the approach end-to-end, then iterate based on results.Support integration with Databricks Genie so semantic definitions and synonyms improve AI response accuracy and reduce hallucination.Advise on how business-unit-specific ontologies (e.g., auto vs. home) roll up into a shared enterprise domain model without duplicating standards.Document patterns and best practices so the internal team can extend the ontology to additional lines of business independently.Required ExperienceProven, hands-on experience designing and implementing ontologies and/or knowledge graphs in a real production environmentStrong semantic modeling background - comfortable defining taxonomies, entity relationships, and domain hierarchies.Experience with Databricks (Unity Catalog, Lakehouse architecture); comfort working in environments where native ontology tooling is still maturing.Familiarity with ontology/knowledge graph frameworks such as LinkML, RDF/OWL, or graph databases (e.g., Neo4j).Ability to work independently, review and constructively critique existing technical direction, and mentor an internal engineer newer to the discipline.Experience translating high-level architecture into a working, iterable reference implementation.What Success Looks Like: A working, documented ontology use case for Global Auto running on Databricks, validated best practices for extending the model to other lines of business, and a more confident, less isolated internal team equipped to carry the initiative forward.