Data and Knowledge Graph Engineer

Overview

In this role you will design and implement semantic layers, turning complex domains into ontologies and knowledge graphs that connect data, systems and AI solutions. You will own end-to-end design from discovery to production, enabling governance, analytics and generative AI. You’ll work across industries, collaborating with stakeholders to deliver scalable, trustful data foundations. The opportunity centers on shaping semantic architectures that power data-driven insights and AI-enabled use cases.

Responsibilities

• Facilitate discovery workshops with stakeholders to model domains
• Design, build, and maintain ontologies, taxonomies, and semantic models
• Develop and operate end-to-end knowledge graphs including ingestion, mapping, and entity resolution
• Standardise data across disparate systems using semantic technologies
• Create and optimise graph queries and expose data via APIs/services
• Integrate knowledge graphs with AI solutions for analytics and generative AI
• Prototype and productionise AI-powered features including prompt engineering and evaluation
• Advise on semantic architecture, governance, and best practices

Key requirements

• Degree in Computer Science, Computational Linguistics, Information Science, or related field, or equivalent hands-on experience
• Proven track record delivering semantic layers, ontologies, and knowledge graphs in production
• Strong understanding of semantic web technologies and knowledge representation (RDF, OWL, SPARQL, Cypher); experience with graph databases (Neo4j, GraphDB)
• Solid Python programming skills for data integration and automation
• Experience with modern data architectures (data lakes/warehouses), governance, and master/metadata management
• Hands-on experience with Generative AI and LLMs (RAG, embeddings, vector search, GraphRAG); experience with LangChain, LlamaIndex
• Strong stakeholder skills and ability to communicate complex concepts to non-technical audiences
• Experience in cloud environments (Azure preferred); knowledge of Git, CI/CD, containers, and client-facing delivery

• Stakeholder management
• Clear communication to non-technical audiences
• Cross-functional collaboration
• RDF
• OWL
• SPARQL

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