Senior Semantic Operations & Data Specialist

What we’re looking for The Senior Semantic Operations & Data Specialist provides the technical and operational capability that supports the enterprise semantic and ontology function. Working closely with the Ontology Manager, the role is responsible for discovering, analysing, transforming, validating and publishing semantic assets derived from enterprise data, metadata, reference data and external information sources.

The role supports the development and maintenance of ontologies, taxonomies, controlled vocabularies, business glossaries and knowledge graph assets. The role acts as the bridge between enterprise data sources, metadata platforms and semantic assets, applying semantic technologies, data analysis, automation and modern AI-assisted techniques to support the delivery of governed, reusable and business-aligned semantic assets.

The role is responsible for the preparation, transformation, validation and publication of semantic assets but does not own enterprise application development, infrastructure management, platform administration, enterprise integration services or production data engineering. What will you be doing Semantic Discovery & Data Analysis (40%) Relational Data Discovery Query and analyse enterprise databases to profile source systems, inspect schema structures and identify candidate business concepts, classifications and reference data.

Work with business and technical stakeholders to understand the meaning, usage and context of source data assets. Identify semantic modelling opportunities within operational and analytical data sources. Concept Discovery & Knowledge Extraction Analyse structured, semi-structured and unstructured information sources to identify candidate concepts, relationships and classifications. Utilise AI-assisted tooling, natural language processing techniques and analytical approaches to accelerate concept discovery and semantic enrichment activities.

Support the identification of semantic duplication, inconsistencies and data quality issues requiring governance review. Data Wrangling & Semantic Preparation Cleanse, standardise and reconcile terminology, hierarchies, classifications and reference data from multiple sources. Transform legacy vocabularies, spreadsheets and business classifications into reusable semantic assets. Maintain supporting datasets used in ontology, taxonomy and glossary development activities.

Semantic Asset Delivery & Knowledge Graph Enablement (40%) Semantic Modelling Support Develop and maintain RDF, SKOS, OWL, Turtle and JSON-LD representations of approved semantic models. Create mappings between business concepts, source data and semantic representations. Support ontology, taxonomy and controlled vocabulary publication processes. SPARQL & Graph Operations Develop, test and optimise SPARQL queries to support semantic analysis, validation, reporting and knowledge graph operations.

Support concept scheme maintenance and semantic asset lifecycle activities within enterprise graph platforms. Assist with semantic publication and synchronisation activities across approved platforms. Entity Resolution & Identifier Management Implement and maintain URI minting standards and identifier management practices. Support synonym management, concept reconciliation and entity matching activities. Establish and maintain mappings between internal semantic assets and relevant external standards, classifications and reference datasets.

Semantic Validation & Quality Assurance Develop and maintain SHACL validation rules and semantic quality controls. Execute semantic testing, compliance reviews and impact assessments. Monitor semantic assets for adherence to modelling standards, governance requirements and quality expectations. Metadata & Semantic Operations (20%) Metadata Integration Support the publication and synchronisation of semantic assets within metadata, glossary and governance platforms.

Maintain links between semantic assets, business glossaries and metadata catalogues. Contribute to semantic lineage, classification and metadata management activities. Semantic Asset Lifecycle Management Maintain ontology and semantic asset repositories using Git and GitHub. Support Ontology CI/CD processes, including automated validation, testing, release packaging and version management. Contribute to semantic asset release, publication and change management activities.

Stakeholder Collaboration Collaborate with domain experts, business analysts, architects and engineering teams to clarify business terminology, definitions and classifications. Support semantic modelling workshops and knowledge discovery activities. Contribute to semantic standards, documentation and best practices. Skills and Experience Essential Experience working with ontologies, taxonomies, controlled vocabularies, metadata or knowledge graph solutions.

Strong understanding of RDF, RDFS, SKOS, OWL, SHACL and Linked Data principles. Experience writing and optimising SQL and SPARQL queries. Experience using Python for data wrangling, transformation, automation and semantic processing. Experience with semantic libraries and frameworks such as RDFLib or similar technologies. Strong data analysis, profiling and data quality management skills. Experience cleansing, reconciling and transforming complex business datasets.

Experience working with Git, GitHub and version-controlled asset management. Understanding of URI design, namespaces, semantic identifiers and reference data management. Strong analytical, problem-solving and investigative capabilities. Ability to communicate effectively with both business and technical stakeholders. Desirable Experience with Graphologi, PoolParty, TopBraid EDG, Semaphore or similar semantic management platforms.

Experience with OpenMetadata, DataHub, Apache Atlas or related metadata platforms. Familiarity with SHACL validation frameworks and ontology quality assurance approaches. Experience supporting ontology publication and semantic asset lifecycle management. Familiarity with GitHub Actions or similar CI/CD tooling for semantic asset validation and release processes. Experience using AI-assisted tooling for semantic discovery, entity extraction and knowledge enrichment.

Understanding of knowledge graph architecture and graph database technologies. Experience mapping internal semantic assets to external standards, taxonomies or industry reference data. Behavioural Competencies Data Detective Enjoys exploring complex and often undocumented data sources, uncovering meaning and transforming fragmented information into structured, governed knowledge assets. Pragmatic Builder Focuses on delivering practical, maintainable and reusable semantic solutions, balancing technical quality with business value.

Semantic Quality Mindset Takes pride in accuracy, consistency and governance, ensuring semantic assets meet agreed standards and remain fit for enterprise use. Collaborative Communicator Works effectively with both technical and non-technical stakeholders, translating complex data structures and semantic concepts into clear business language. Continuous Learner Keeps abreast of developments in semantic technologies, metadata management, AI-assisted tooling and knowledge graph practices, applying new approaches where appropriate.

What’s in it for you Our rapidly growing, award-winning business offers a dynamic environment for talented, entrepreneurial professionals to achieve results and grow their careers. Argus recognizes and rewards successful performance and as an Investor in People, we promote professional development and retain a high-performing team committed to building our success. Competitive salary and company bonus scheme Group pension scheme Group healthcare and life assurance scheme Hybrid working environment (currently one day in office) 25 days annual holiday with incremental increase up to 30 days Subsidised gym membership Season ticket travel loan Cycle to work scheme Flexible benefits platform (ability to buy additional medical cover, life assurance, dental cover, holiday, critical illness, travel insurance & health screening) Extensive internal and external training