· Lead the design and implementation of enterprise ontologies and knowledge graphs across business domains, including product and engineering, manufacturing, supply chain and operations.
· Define graph schemas, taxonomies, controlled vocabularies and semantic models aligned with the enterprise lakehouse, Power BI, AI/ML pipelines and business applications.
· Integrate structured, semi-structured and unstructured data using Python, Spark/PySpark, SQL and orchestration pipelines; enable entity resolution, relationship extraction and graph enrichment.
· Develop graph queries and analytics using SPARQL, Cypher, Gremlin and KQL graph semantics; optimize indexing, partitioning, caching and query performance.
· Integrate knowledge graphs with Azure OpenAI, Copilot and agentic AI frameworks to support semantic grounding, GraphRAG, hybrid retrieval and explainable AI outcomes.
· Establish ontology lifecycle governance covering semantic consistency, metadata, lineage, stewardship, versioning and change control in partnership with domain experts.
· Act as technical authority and thought partner for ontology, knowledge graph and semantic architecture initiatives.
Requirements
· 8+ years in Data Engineering, data architecture or related areas, including at least 3 years of hands-on Enterprise Ontology, Knowledge Graph or Semantic Architecture experience.
· Strong expertise in RDF/RDFS, OWL, SPARQL, SHACL and SKOS, with experience using ontology modeling tools such as Protege or equivalent.
· Hands-on experience with Neo4j or similar graph platforms and working knowledge of Cypher, Gremlin, SPARQL and graph performance optimization.
· Practical experience with lakehouse/data lake architectures, Azure data services or Microsoft Fabric, plus Python, PySpark and SQL.
· Experience with GraphRAG, vector databases, hybrid retrieval and integration with Azure OpenAI or other LLM-based platforms.
Summary
Design and implement enterprise ontologies and knowledge graphs, define semantic models and graph schemas, integrate data with Python, Spark and SQL, build graph queries, optimize performance, and connect graphs to Azure OpenAI and GraphRAG. Requires 8+ years in data engineering or architecture, including 3 years hands-on ontology or knowledge graph experience, plus RDF, OWL, Neo4j and Fabric.
Responsibilities
Lead design and implementation of enterprise ontologies and knowledge graphs across product, engineering, manufacturing, supply chain and operations; define graph schemas, taxonomies, controlled vocabularies and semantic models; integrate data with Python, Spark/PySpark, SQL and pipelines; develop SPARQL, Cypher, Gremlin and KQL queries; optimize indexing, partitioning, caching and performance; integrate with Azure OpenAI, Copilot and agentic AI; establish ontology lifecycle governance; act as technical authority.
Qualifications
Strong expertise in RDF/RDFS, OWL, SPARQL, SHACL and SKOS; ontology modeling tools such as Protege; Neo4j or similar graph platforms; lakehouse/data lake architectures, Azure data services or Microsoft Fabric; Python, PySpark and SQL; GraphRAG, vector databases, hybrid retrieval and Azure OpenAI or other LLM-based platforms.
Experience requirements
8+ years in Data Engineering, data architecture or related areas, including at least 3 years hands-on Enterprise Ontology, Knowledge Graph or Semantic Architecture experience.