Senior Semantic Operations & Data Specialist

Overview

In this senior role you will enable enterprise semantic assets by discovering, transforming, validating and publishing ontologies, taxonomies and knowledge graphs. You will bridge data sources, metadata platforms and semantic representations, using AI-assisted techniques to deliver governed, reusable assets. You’ll work closely with the Ontology Manager to shape semantic standards and publication workflows, balancing technical rigor with business value.

Pay / Benefits

• Competitive salary and company bonus scheme
• Group pension scheme
• Group healthcare and life assurance
• Hybrid working environment
• 25 days annual holiday (up to 30)
• Subsidised gym membership

Responsibilities

• Semantic Discovery & Data Analysis: profile enterprise databases, understand data meaning, identify semantic opportunities, extract candidate concepts and classifications; utilise AI-assisted tooling for concept discovery and quality governance.
• Semantic Asset Delivery & Knowledge Graph Enablement: model semantic structures (RDF, SKOS, OWL, Turtle, JSON-LD), create mappings, and support publication and lifecycle of semantic assets; perform SPARQL queries and graph operations.
• Entity Resolution & Identifier Management: manage URI minting, synonym handling, and entity matching; map internal assets to external standards.
• Semantic Validation & Quality Assurance: develop SHACL rules, conduct validation, governance reviews and quality monitoring.
• Metadata & Semantic Operations: publish and synchronise assets with metadata platforms, manage semantic lineage and metadata catalogues; support CI/CD for semantic assets.
• Stakeholder Collaboration: align terminology with business experts, run modelling workshops and contribute to standards and best practices.

Key requirements

• Experience with ontologies, taxonomies, controlled vocabularies, metadata or knowledge graphs.
• Strong understanding of RDF, RDFS, SKOS, OWL, SHACL and Linked Data.
• SQL and SPARQL query writing and optimization.
• Python for data wrangling and semantic processing.
• Experience with semantic libraries (e.g., RDFLib).
• Data analysis, profiling and data quality management.
• Git, GitHub and version-controlled asset management.
• URI design, namespaces and reference data management.

• Analytical thinking
• Effective communication with technical and business stakeholders
• Collaborative mindset
• SPARQL
• RDF/OWL/RDFS
• SKOS

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