Role Overview
We are seeking an experienced Ontology Specialist to build the semantic foundation of an enterprise Data & AI ecosystem. The role involves designing and implementing enterprise ontologies, knowledge graphs, and reusable semantic models to connect fragmented data across product engineering, manufacturing, supply chain, operations, and commercial functions.
The specialist will collaborate with business stakeholders, enterprise architects, data engineering teams, and AI professionals to develop contextual, governed, and AI-ready data assets that enable advanced analytics, intelligent decision-making, digital twins, recommendation systems, and enterprise AI solutions.
Key Responsibilities
- Design, develop, and implement enterprise ontologies and knowledge graphs across product engineering, manufacturing, supply chain, and operations domains.
- Define graph schemas, taxonomies, controlled vocabularies, and semantic models aligned with enterprise lakehouse platforms, Power BI, AI/ML pipelines, and business applications.
- Integrate structured, semi-structured, and unstructured data using Python, Spark/PySpark, SQL, and data orchestration pipelines.
- Implement entity resolution, relationship extraction, semantic enrichment, and knowledge graph construction.
- Develop and optimize graph queries and analytics using SPARQL, Cypher, Gremlin, and KQL graph semantics.
- Optimize graph performance through effective indexing, partitioning, caching, and query tuning.
- Integrate knowledge graphs with Azure OpenAI, Microsoft Copilot, and agentic AI frameworks to enable GraphRAG, hybrid retrieval, semantic grounding, and explainable AI outcomes.
- Establish ontology lifecycle governance, including semantic consistency, metadata management, data lineage, stewardship, versioning, and change control.
- Collaborate with domain experts, business stakeholders, data architects, and AI teams to translate business requirements into scalable semantic solutions.
- Serve as a technical subject matter expert for ontology engineering, knowledge graphs, and enterprise semantic architecture initiatives.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Data Science, Information Systems, Engineering, or a related discipline.
- 8+ years of experience in data engineering, data architecture, or related technical roles.
- At least 3 years of hands-on experience in enterprise ontology engineering, knowledge graphs, or semantic architecture.
- Strong expertise in semantic web technologies, including RDF/RDFS, OWL, SPARQL, SHACL, and SKOS.
- Experience with ontology modeling tools such as Protégé or equivalent platforms.
- Hands-on experience with Neo4j or similar graph database platforms.
- Proficiency in graph query languages, including Cypher, Gremlin, and SPARQL, along with graph performance optimization techniques.
- Practical experience with enterprise lakehouse/data lake architectures and Azure data services or Microsoft Fabric.
- Strong programming and data processing skills in Python, PySpark, and SQL.
- Experience implementing GraphRAG, vector databases, hybrid retrieval, and knowledge graph integration with Azure OpenAI or other LLM-based platforms.
- Strong analytical, problem-solving, communication, and stakeholder collaboration skills.
Preferred Qualifications
- Experience with Stardog, Ontotext, or equivalent ontology and knowledge graph platforms.
- Familiarity with Data Mesh architectures and enterprise semantic-layer implementations.
- Experience working with manufacturing, automotive, aerospace, industrial products, or engineering data.
- Understanding of digital twins, AI-driven analytics, and enterprise knowledge management.
- Experience delivering scalable semantic solutions across complex enterprise data ecosystems.
Key Technical Skills
- Ontology & Semantic Modeling: RDF/RDFS, OWL, SHACL, SKOS, Protégé
- Knowledge Graphs: Neo4j, Stardog, Ontotext, Graph Schema Design
- Query Languages: SPARQL, Cypher, Gremlin, KQL Graph Semantics
- Programming & Data Engineering: Python, PySpark, Spark, SQL
- Cloud & Data Platforms: Microsoft Azure, Microsoft Fabric, Lakehouse Architecture
- AI & LLM Integration: Azure OpenAI, Microsoft Copilot, GraphRAG, Agentic AI, Vector Databases, Hybrid Retrieval
- Governance: Metadata Management, Data Lineage, Semantic Governance, Versioning, Data Stewardship