Data Architect

MET Group is an integrated European energy company, headquartered in Switzerland, with activities in natural gas and power, focused on multi-commodity wholesale, trading and sales, as well as energy infrastructure and industrial assets.

MET Group is represented in 22 countries: Albania, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Czech Republic, Croatia, France, Germany, Greece, Hungary, Italy, Poland, Romania, Serbia, Singapore, Slovakia, Spain, Switzerland, The Netherlands, Turkey and Ukraine.

MET Group is present in 33 national energy markets and 44 international trading hubs. The Group has a significant end-consumer presence in Belgium, Croatia, Italy, Hungary, Romania, Slovakia, Spain, and The Netherlands.

MET Group has 1350+ permanent staff. The company is owned 90% by MET employees and 10% by Keppel Infrastructure, a wholly owned subsidiary of  Keppel Corporation*.

* Listed on the Singapore Exchange

Purpose of the role

Drive the design and evolution of our enterprise data models in the Digital Collaboration Platform (Dataspot). You will partner with business and technology stakeholders to translate business understanding into high-quality Conceptual Data Models and to document Logical Data Models close to implementation, ensuring clear lineage and traceability across our models (Conceptual Data Model, Reference Data Model, Metrics Model, Data Quality Model and Logical Data Model).

Essential responsibilities

• Drive hands‑on data modelling initiatives and contribute to the design and evolution of the enterprise data architecture.
• Translate business understanding and requirements into clear, consistent conceptual and logical data models that support scalable and reusable solutions.
• Collaborate with business and technology stakeholders to assess data and business requirements for projects and initiatives, ensuring modelling outcomes align with enterprise standards.
• Document and maintain implementation‑close logical data models and ensure traceability and lineage between business concepts and their technical representations across systems.
• Capture, document, and maintain data definitions, relationships, lineage, and related metadata in the enterprise Digital Collaboration Platform.
• Facilitate modelling workshops and working sessions with business and technical stakeholders to clarify concepts, structures, and dependencies.
• Apply data modelling standards and best practices consistently and contribute to their continuous improvement through reusable patterns and pragmatic recommendations.

Strong hands‑on modelling skills with a practical understanding of data governance, reference data, and metadata management, and contributes to the continuous improvement of modelling practices and standards.

• Early professional stage with strong analytical and logical thinking skills.
• Solid understanding of data modelling and data architecture concepts, with hands‑on experience applying them in real projects.
• Experience in translating business requirements into structured data models and documentation.
• Working knowledge of common analytical data modelling approaches (e.g. star schema, data vault) and awareness of modern architectural paradigms such as data mesh.
• Familiarity with at least one modern data stack technology (e.g. dbt, Snowflake, Databricks), with an understanding of how conceptual and logical models are implemented in practice.
• Strong ability to work collaboratively in cross‑functional environments, communicate clearly with technical and non‑technical stakeholders, and facilitate discussions and workshops to drive alignment.
• A strong quality mindset and attention to detail, with the ability to apply modelling standards and best practices consistently.
• Fluency in English (spoken and written)
• Working knowledge of SQL, with the ability to understand data structures, joins and transformations.

Nice to have

• Understanding of modern data architectures (e.g. data warehouses, data lakes / lakehouse concepts)
• Exposure to analytics or data platforms in enterprise environments.
• Experience with metadata management or data catalog tools (e.g. Dataspot or comparable platforms).
• Familiarity with basic data quality and data lifecycle concepts.