Knowledge Graph Engineer | Semantic Data Specialist

About the role

A mid-to-senior level, full-time Knowledge Graph Engineer is sought to design, develop and maintain knowledge graphs, semantic data models, ontologies and intelligent data integration solutions. The role combines knowledge graph engineering, semantic technologies, data architecture and information modelling to connect complex datasets, improve data discoverability and enable advanced analytics and AI-driven applications.

Key responsibilities

• Design, develop and maintain enterprise knowledge graphs and semantic data solutions, including graph-based data models, ontologies, taxonomies, vocabularies and semantic schemas.
• Integrate data from relational databases, document repositories, APIs, data warehouses and external sources, and build ingestion, transformation, normalization and enrichment pipelines.
• Implement entity resolution, entity linking, deduplication and relationship extraction; map heterogeneous datasets to shared semantic models.
• Develop and optimise graph queries with SPARQL, Cypher or equivalent, and work with RDF, RDFS, OWL, JSON-LD and other semantic web standards.
• Build and maintain graphs on Neo4j, GraphDB, Stardog, Amazon Neptune or equivalent, and integrate them with ML, NLP and RAG solutions.
• Develop APIs exposing graph data, set up data validation rules, and maintain documentation and semantic modelling standards.
• Collaborate with data engineers, architects, data scientists and domain experts, and support data governance, metadata management and information architecture.

Required profile

• Bachelor's or Master's degree in Computer Science, Information Systems, Data Engineering, AI, Knowledge Engineering or a related field.
• Proven experience in knowledge graph engineering, semantic data modelling or graph databases.
• Experience designing ontologies, taxonomies and controlled vocabularies.
• Strong analytical, problem-solving and communication skills, with attention to data accuracy and consistency.

Required skills

• Graph databases (Neo4j, Amazon Neptune, GraphDB, Stardog) and query languages (Cypher, SPARQL, Gremlin).
• RDF, RDFS, OWL, JSON-LD and linked data principles.
• SQL, relational databases, data structures and data integration.
• Python or another programming language; REST APIs and data serialization.
• Entity resolution, record linkage, relationship extraction and knowledge graph population (advantageous).
• Natural language processing, information extraction, LLM/RAG integration and cloud platforms (beneficial).

What we offer

• Competitive salary and comprehensive employee benefits.
• Professional development, training and support for technical certifications.
• Career progression toward Senior Knowledge Graph Engineer, Semantic Data Architect, Knowledge Engineering Lead or Head of Knowledge Engineering.