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Knowledge Modeller
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This is a free training course followed by the potential for a role with one of our clients.
Title: Knowledge Modeller
Job location: Remote, UK (1 day per fortnight in London or Manchester)
Description:
These job roles are linked directly to free education, and upon completion of our course, you will be equipped to start your new role.
The Role:
Our clients are looking for Knowledge Modellers to join their team.
About You & The Role
As a Knowledge Modeller, you will play a key role in structuring and connecting complex domain data to power cutting-edge enterprise AI and data retrieval platforms. Sitting within the organisation's data and technology team, you will bridge the gap between business subject matter experts and technical delivery teams by translating unstructured business understanding into structured, semantic representations.
- Hands-on experience with conceptual data modelling, logical data modelling, and entity-relationship modelling.
- Strong capability to define entities, attributes, relationships, and cardinality across diverse business domains.
- Proven background modelling complex domains involving many-to-many relationships, hierarchical structures, and rule-based logic.
- Practical experience in knowledge graph modelling, ontology tools (e.g., Protégé), and core graph concepts (nodes, edges, traversal).
- Prior experience working within regulated environments (such as government or financial services).
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Some Things You'll Be Working On
- Conducting knowledge discovery across the organisation, collaborating closely with stakeholders to understand how data and processes relate.
- Facilitating structured stakeholder interviews and workshops to extract implicit knowledge from SMEs, technical documentation, and legacy systems.
- Identifying data inconsistencies, gaps, and duplication across enterprise data sources.
- Defining entities, relationships, and precise semantics across multiple domains.
- Translating unstructured knowledge into structured artefacts, including data models, diagrams, and semantic definitions.
- Developing conceptual and logical data models that directly underpin knowledge graphs, taxonomies, and ontologies.
- Enabling downstream technical teams to build advanced knowledge graphs, Retrieval-Augmented Generation (RAG) pipelines, and AI retrieval systems.
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