About this role
· 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
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· 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.
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· Strong expertise in RDF/RDFS, OWL, SPARQL, SHACL and SKOS, with experience using ontology modeling tools such as Protege or equivalent.
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· Hands-on experience with Neo4j or similar graph platforms and working knowledge of Cypher, Gremlin, SPARQL and graph performance optimization.
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· Practical experience with lakehouse/data lake architectures, Azure data services or Microsoft Fabric, plus Python, PySpark and SQL.
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· Experience with GraphRAG, vector databases, hybrid retrieval and integration with Azure OpenAI or other LLM-based platforms.