Job Description

Job Title: Databricks - Ontology / Knowledge Graph Engineer

Location: Remote

Overview: 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.

Responsibilities

• Review 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 Experience

• Proven, hands-on experience designing and implementing ontologies and/or knowledge graphs in a real production environment
• Strong 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.

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