Data Platform Engineer (Data & Metadata Modelling)
- Design and implement data models aligned with international metadata standards (SDMX, DDI, DCAT, etc.)
- Collaborate with data architects, domain experts, and standards bodies to maintain semantic consistency
- Develop and maintain SDMX artefacts (data structures, codelists, dataflows, mappings)
- Ensure high metadata quality, consistency, and validation using industry-standard tools
- Support the adoption and integration of metadata registries across the organization
- Operate and extend tooling for metadata modelling, including SDMX Matrix Generator, FMR, and related libraries
- Develop scripts and utilities to automate metadata generation, validation, and transformation
- Integrate metadata tooling with data pipelines and data catalog platforms
- Contribute to the development of custom plugins, connectors, and APIs for SDMX/DDI tools
- Integrate metadata models with enterprise data platforms and business intelligence tools
- Manage the versioning, publishing, and governance of metadata artefacts
- Support the deployment of metadata services in cloud-native environments (e.g., Kubernetes, Docker)
- Ensure compatibility with evolving data platforms, APIs, and reporting systems
- Enforce metadata governance policies and support compliance with open data standards
- Conduct peer reviews, documentation, and metadata audits for continuous improvement
- Provide training and support for internal teams on SDMX/DDI standards and tools
- Participate in cross-functional working groups on metadata strategy and standardization
- 6+ years of experience with data and metadata modelling, ideally in public sector, statistical, or research environments
- Strong expertise in SDMX (Statistical Data and Metadata eXchange) and DDI (Data Documentation Initiative)
- Hands-on experience with SDMX Matrix Generator, FMR (Fusion Metadata Registry), and related tools
- Familiarity with metadata registries, ontologies, and semantic modelling
- Proficient in XML, RDF, JSON, and associated schema definitions (XSD, SHACL)
- Experience with automation and scripting (Python, XSLT, Bash) for data transformation and validation
- Familiarity with version control (Git), CI/CD workflows, and containerization (Docker)
- Understanding of metadata APIs and integration with data platforms or catalogs
- Exposure to data exchange standards (DCAT, SKOS, OWL) and interoperability best practices
- Bachelor's or Masters degree in Information Science, Computer Science, or related field
- Experience working with statistical agencies, open data portals, or international data organizations
- Knowledge of data governance frameworks and FAIR data principles
- Experience with cloud-native environments (AWS, Azure, GCP) and metadata services
- Participation in international working groups or standardization bodies (e.g., SDMX Technical Group)
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