Senior Data Architect – Semantics & Knowledge Engineering
C
CraftwareData & AI
Fully remote service deliveryContractSenior
Salary7991 - 9444 CHF per hour b2b currencySource=conversion; 7346 - 8682 GBP per hour b2b currencySource=conversion; 8562 - 10119 EUR per hour b2b currencySource=conversion; 10017 - 11839 USD per hour b2b currencySource=conversion; 36960 - 43680 PLN per hour b2b currencySource=original
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Job Details
- Required Skills
- GraphQLSnowflakeRESTful APIsDatabricks
Requirements
- Bachelor's degree in computer science, information science, knowledge engineering, computational linguistics, or equivalent practical experience.
- Proficiency in designing and implementing conceptual, logical, and physical data models.
- Advanced knowledge of ontologies and taxonomies, with proficiency in Semantic Web technologies and standards (RDF, OWL, SHACL, SPARQL, TopBraid EDG).
- Proficiency with graph databases — both triple stores and labeled-property graph systems — including virtual knowledge graph technologies such as Ontop or Stardog.
- Extensive experience with API design patterns such as REST and GraphQL.
- Strong engineering background (data engineering, software engineering, or cloud engineering).
- Proficiency in at least one programming language, with basic Git version control practices.
- In-depth knowledge of FAIR Data Principles, Linked Data principles, and Data Mesh architecture, with demonstrated practical application.
- Strong analytical and communication skills; ability to work collaboratively in a team.
Responsibilities
- Design and implement conceptual, logical, and physical data models and semantic layers across the organization.
- Design, build, and maintain ontologies, taxonomies, and knowledge graphs using both triple stores and labeled-property graph technologies, including virtual knowledge graph approaches.
- Collaborate with domain experts, data scientists, and data engineers to elicit tacit knowledge and validate knowledge representations.
- Design data-oriented APIs and integration patterns that make knowledge structures interoperable and consumable by humans, systems, and AI agents.
- Monitor emerging technologies in knowledge engineering, data integration, and semantic AI, and participate in code reviews and engineering best practices.
- Drive cross-team alignment on data and knowledge architecture decisions, aligning initiatives with business, digital, and IT strategy.
- Embrace FAIR and Linked Data principles to shift the organization from siloed to networked, democratized data use.
- Design and implement measures to protect data confidentiality, integrity, and availability.
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