Knowledge Graph Engineer (f/m/x)

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Client:

ZEISS Group

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–

EU work permit required:

Yes

Job Reference:

462b4a0f86cb

Job Views:

1

Posted:

05.05.2025

Expiry Date:

19.06.2025

Job Description:

Your Role

You will play a crucial role in shaping the future of Data & Analytics and actively contribute to the success of the digital transformation at ZEISS. Within the Enterprise Data Management Team of our rapidly growing Data & Analytics organisation, you will help our business units to design, build, and maintain knowledge-based systems, driven by the vision to enable everyone to effectively find, learn, share, & innovate throughout the enterprise.

You will:

1. Integrate information into knowledge-graph based systems and engage in data & knowledge / ontology modelling, knowledge graph construction, and validation.
2. Develop and apply algorithms based on graph queries / analytics (e.g., SPARQL), graph reasoning (e.g., RDFS+, OWL), AI/ML (e.g., NLP, graph embeddings) to enable various analytics use cases leveraging knowledge graphs.
3. Work in a team with data scientists, data & software engineers & product owners to create scalable, reusable, and performant data-oriented applications, including chatbots, recommender systems, search, and other knowledge-based systems.
4. Convey technically difficult concepts in a clear and understandable way, translate business needs to technical specifications, and provide guidance for knowledge graph & ontology development to other teams.
5. Evaluate the latest methods and technologies and support the design of our data & analytics platforms and governance infrastructures. Continuously improve quality and performance while owning applications from idea to operations.

Your profile

1. An excellent university degree (MSc/PhD) in information technology, computer science, natural sciences, or similar training.
2. At least 3 years of working experience in data integration, building (semantic) knowledge graphs, querying (graph) data using standard query languages (SQL, SPARQL).
3. Familiarity with machine actionable languages (e.g., RDF), linked data principles and standards, & OWL-based ontology modelling in commonly used tools.
4. Solid understanding of at least one of Graph Analytics, Machine Learning / Deep Learning, Natural Language Processing.
5. An independent (“can-do”), solution-oriented and team-oriented way of working, not afraid to take on responsibility.
6. Strong desire to learn & upskill at the intersection of data, science & industry.
7. Experience with the following is welcome and considered as a plus: Cloud technologies like Microsoft Azure (e.g., Synapse, Databricks, Azure DevOps), CI/CD using e.g., git, docker, Agile software-development using Scrum / Kanban.

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