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# Knowledge Engineer

- **Company:** Fusion Consulting AG
- **Location:** Dresden, Germany
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Business Logic, Data Governance, Graph Database, Knowledge Management, Semantic Web, SPARQL
- **Published:** September 14, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=62f9047fc2a45217

## About the Role

* Experience & Expertise: 3-5 years in engineering, semantic modeling, or information/knowledge management. Proficiency in ontology and taxonomy modeling and formal representation languages (e.g., RDF, OWL, SKOS), with hands-on experience building enterprise ontologies and knowledge graphs (e.g., Stardog, AWS Neptune)
 * Business Translation: Ability to work with SMEs to capture domain logic and turn business definitions into clear, machine-readable structures
 * Strategic Skills: Strong communication, problem-solving and stakeholder management, focused on reuse and scalability across use cases
 * Technical Understanding: Practical experience working with graph databases and knowledge graph platforms (e.g., Stardog, AWS Neptune, or similar), including querying with SPARQL
 * Ways of Working: Ability to work independently and take ownership and accountability on assigned tasks driving execution through completion while being part of a collaborative team
 * Industry Knowledge: Deep understanding of regulatory requirements, data governance practices, and industry trends
 * Language: Strong communication skills in English (spoken and written), be an effective, passionate, trusted advocate and communicator for knowledge graph and Semantic Web technologies

## Description

* Design and Build Enterprise Ontologies & Knowledge Graphs: Develop entity hierarchies, relationship types, and inference rules that allow AI systems to reason correctly across commercial data domains.
 * Formalize Knowledge Representations: Translate business definitions and data models into machine-readable representations (e.g., OWL, RDF), ensuring consistency, reusability, and scalability.
 * Capture Domain Logic: Work with business subject matter experts to elicit, capture, and formalize domain knowledge and business logic into structured ontologies and taxonomies.
 * Maintain Clear Semantic Boundaries: Collaborate with data modelers and semantic engineers to maintain well-defined boundaries between the ontology, the semantic data model, and the knowledge graph.
 * Ensure Standards and Governance: Align knowledge assets with internal standards and semantic web best practices and contribute to governance guidelines for ontology and knowledge graph development.
 * Enable AI and Analytics Use Cases: Ensure knowledge structures support a broad range of downstream consumers including conversational analytics, AI agents, and cross-domain reasoning rather than a single use case.


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