Senior Semantic Engineer

As Senior Semantic, you enable enterprise semantic assets by discovering, transforming, validating and publishing ontologies, taxonomies and knowledge graphs. You will collaborate with the Ontology Manager to bridge data sources, metadata platforms and semantic assets, applying AI-assisted techniques and semantic technologies to deliver governed, reusable assets. This role focuses on semantic discovery, data preparation, model delivery, validation, and lifecycle management across platforms. You will shape how business concepts map to data and drive governance and quality in semantic assets.

• Competitive salary and bonus
• Group pension scheme
• Group healthcare and life assurance
• Hybrid working (two days in office)
• 25 days holiday (up to 30)
• Gym membership subsidy

• Semantic Discovery & Data Analysis: profile source systems, understand data meaning with stakeholders, identify semantic modelling opportunities, analyse structured/unstructured data for concepts and relationships, ensure data quality and governance fit.
• Data Wrangling & Semantic Preparation: cleanse and reconcile terminology across sources, transform legacy vocabularies into reusable assets, maintain datasets for ontology and glossary development.
• Semantic Asset Delivery & Knowledge Graph Enablement: develop RDF/ SKOS/ OWL representations, create mappings, support publication and lifecycle of semantic assets; SPARQL query development, knowledge graph operations.
• Entity Resolution & Identifier Management: manage URIs, synonyms, concept reconciliation and mappings to external standards.
• Semantic Validation & Quality Assurance: develop SHACL rules, perform testing and impact assessments, monitor adherence to standards.
• Metadata & Semantic Operations: publish and synchronise assets across metadata platforms, manage lineage and metadata catalogues; support CI/CD for semantic assets.
• Stakeholder Collaboration: work with domain experts and engineers, contribute to standards, workshops, and best practices.

• Experience with ontologies, taxonomies, vocabularies, metadata or knowledge graphs.
• Strong understanding of RDF, RDFS, SKOS, OWL, SHACL and Linked Data.
• SQL and SPARQL proficiency.
• Python for data wrangling and semantic processing.
• Experience with semantic libraries like RDFLib.
• Data analysis, profiling and data quality management skills.
• Git/GitHub and version-controlled asset management.
• URI design and reference data management.

• Analytical
• Collaborative Communicator
• Continuous Learner
• RDF/RDFS/SKOS/OWL/SHACL
• SPARQL
• Python