Ontology, Knowledge Graph & Semantic Layer Lead

Role overview

Responsibilities

• Define target-state architecture and strategy for the enterprise ontology, knowledge graph, and semantic layer.
• Design enterprise semantic models, taxonomies, and ontologies aligned with business and data domains.
• Build the roadmap, governance framework, standards, and operating model.
• Lead initial implementation and guide foundational ontology and knowledge graph capabilities.
• Identify and prioritize business use cases and quick wins.
• Align with data governance, metadata management, MDM, data products, data catalog, and AI initiatives.
• Advise on tooling, platform selection, and architecture decisions.
• Build internal capability through knowledge transfer and mentoring.

Experience and background

• 10+ years in data architecture, semantic technologies, knowledge engineering, and knowledge management.
• Proven leadership of enterprise-scale ontology initiatives, knowledge graph implementations, and semantic architecture programmes.
• Strong grounding in ontology engineering, taxonomy development, knowledge representation, semantic modeling, and metadata management.
• Experience defining target-state enterprise semantic architectures.
• Solid understanding of data governance, metadata management, enterprise architecture, and business architecture.
• Experience using ontologies and knowledge graphs for AI, GenAI, RAG, enterprise search, and knowledge management.

Technical expertise

• Deep experience with several of RDF, OWL, RDFS, SKOS, SPARQL, SHACL, property graphs, LPG and RDF graph models, semantic reasoning, and inferencing.
• Hands-on experience with platforms such as Stardog, GraphDB, TopBraid, PoolParty, Neo4j, Amazon Neptune, Microsoft Fabric/Purview semantic capabilities, or the Databricks knowledge graph ecosystem.
• Excellent stakeholder management and ability to engage executive and technical audiences.

Role environment