Knowledge Modelling Product Manager - Contract - Remote in the UK

Remote - candidates may work from anywhere in the UK

Contract

Market rate - via Umbrella

Role Overview

The client is seeking an experienced Knowledge Modelling Product Manager to support the successful adoption of a semantic abstraction layer across its central platform team and multiple business units.

This role requires strong, hands-on knowledge of modelling expertise and the ability to bridge the gap between semantic technologies and the operational needs of teams that are new to ontology-based approaches.

You will work closely with platform architects, engineers, data specialists and subject-matter experts to establish modelling standards, develop canonical domain models and build sustainable semantic-modelling capability across the organisation.

You will work closely with:

• Data Portfolio Managers
• Semantic Platform Administrators
• Platform Architects and Engineers
• Data Modellers
• Data Engineers
• Subject-Matter Experts
• Business-unit stakeholders

Key Responsibilities

1. Client Platform Team Enablement

• Train platform architects and engineers in semantic-modelling fundamentals, including OWL, RDF/RDFS, SKOS, SPARQL, graph-database operation, ontology-design patterns and common modelling pitfalls.
• Guide the engineering team in the implementation of ontology-management services, ensuring that technical decisions support the intended business outcomes.
• Establish semantic standards for the Client Platform, including naming conventions, annotation requirements, foundational ontology-alignment patterns and shared vocabularies.
• Work collaboratively with relevant architecture, data and governance teams to ensure consistent implementation of these standards.
• Provide expert guidance to technical teams without taking ownership of software engineering or platform-infrastructure delivery.

2. Business-Unit Enablement

• Work directly with subject-matter experts and data modellers across the organisation to develop their first canonical domain models.
• Facilitate structured workshops in which subject-matter experts articulate their domain knowledge and data modellers translate it into formal semantic-model decisions.
• Apply a hands-on and pragmatic approach rather than relying on theoretical training alone.
• Build capability progressively by initially working alongside teams, then coaching them and ultimately enabling them to operate independently.

Develop reusable guidance materials, including:

• Modelling guides
• Worked examples based on real business domains
• Ontology-design patterns
• Decision frameworks for common modelling questions
• Help teams make practical decisions about model granularity, class hierarchies, properties, relationships and reuse.

3. Stakeholder Engagement and Adoption

• Explain the business value of the semantic layer to non-technical stakeholders using clear, outcome-focused language.
• Present tangible examples of how well-designed canonical models support business and technology outcomes.
• Address stakeholder concerns honestly, including where semantic approaches introduce additional effort and where that investment is expected to deliver value.
• Promote adoption across culturally and technically diverse stakeholder groups.

Demonstrate how semantic modelling can improve:

• Data findability
• Interoperability
• Intellectual-property protection
• Cross-business data understanding
• Application-development
• Data-product descriptions
• Integration efficiency and cost

4. Modelling Quality Assurance

• Act as the expert reviewer within the model-publication process during the initial increments of the Client Platform.

Review submitted models for:

• Structural quality
• Standards compliance
• Pattern adherence
• Reusability
• Interoperability readiness
• Define clear and practical criteria for what a high-quality canonical domain model looks like.
• Produce concrete examples that teams can use as reference models.
• Identify and challenge modelling anti-patterns before they become Embedded across the organisation.
• Ensure that data and governance policies are reflected correctly in the models, while recognising that policy ownership sits with the relevant governance teams.

Essential Experience

• Significant hands-on ontology-development experience within an industrial, commercial or enterprise environment.

Practical expertise in:

• Web Ontology Language - OWL
• Resource Description Framework - RDF
• RDF Schema - RDFS
• Simple Knowledge Organization System - SKOS
• SPARQL
• OWL API
• Experience designing, developing and maintaining enterprise semantic models or canonical domain models.
• Experience operating open-standards graph databases, including configuration, data loading, querying and performance considerations.
• Demonstrable ability to translate complex knowledge from subject-matter experts into formal semantic models.
• Experience introducing semantic technologies to teams with limited or no previous exposure to ontology-based approaches.
• Evidence of achieving successful adoption and capability transfer, rather than solely delivering technical artefacts.
• Experience facilitating requirements-gathering and domain-modelling workshops with technical and non-technical participants.

Essential Skills

• Strong ontology-engineering and knowledge-modelling capability.
• Ability to explain semantic-modelling concepts to non-technical audiences without unnecessary jargon.
• Ability to work with specialist domain experts and extract the knowledge required to produce coherent, usable models.
• Strong requirements-analysis and stakeholder-management skills.
• Comfortable working in an environment where the technology and operating model are still developing.
• Pragmatic, patient and able to provide clarity in ambiguous situations.
• Strong views on modelling quality, balanced with the flexibility to respond to practical delivery constraints.
• Excellent written communication skills, with the ability to produce concise and usable guidance rather than academic documentation.
• Clear and straightforward verbal communication.
• Comfortable working across multidisciplinary and multicultural teams.
• Able to influence technical decisions without direct ownership of engineering delivery.

Desirable Experience

• Broader graph-database experience.
• Requirements-life cycle management.
• Product-management or platform-product experience.
• Data-governance implementation.
• Enterprise data architecture.
• Team leadership, coaching or capability development.
• Experience working across multiple business units or federated organisations.

Scope of the Role

• The role is intended to build lasting capability within the platform team and wider business. The objective is to move progressively from hands-on delivery to coaching and advisory support as internal teams become more autonomous.
• This is.*not a software-engineering role*. The contractor will not be responsible for building the underlying platform infrastructure but must have sufficient technical expertise to guide the teams responsible for its delivery.
• This is.*not a data-governance ownership role*. Governance policies will be owned by the appropriate governance stakeholders; this role will help ensure those policies are implemented effectively within semantic models and platform practices.