An organization is looking for a Data Architect to design and build the data backbone of a national AI factory. You will play a key role in shaping the data architecture that enables cutting-edge research and innovation across the country. You will ensure that complex, multi-source input datasets as well as the models and artefacts are stored reliably and made accessible. This project contributes to Europe’s ambition to strengthen its AI capabilities and technological sovereignty.
What you do
- Technical development of the data portfolio on the AI factory infrastructure (data landing zones, data transfer across different storage backends, supporting pipelines for distributed data loading for multi-GPU/multi-node training, etc.)
- Activities to design the data governance and access control model and its technical implementation
- Testing and implementation of technical services to manage training data as well as model outputs (provenance, versioning, metadata, etc.)
- Strict security and compliance for sensitive data (e.g., via implementation of secure enclaves)
- Design and support building of scalable data pipelines for large-scale AI workloads
- Supporting integration with European data spaces and national sectoral data infrastructures
- Contributing to the data service catalogue and user documentation
- Advising project teams on their end-to-end AI workflows
What they ask
- BSc/MSc level in computer science, data science, information science or equivalent
- A thorough understanding of host and file systems security, authentication and authorization principles in GNU/Linux environments
- Knowledge of databases (relational, non-relational), object storage (S3-compatible), shared parallel file systems (POSIX), and data catalogues
- Hands-on Experience with tools for full lifecyle of ML/AI workflows (e.g., MLFlow, wandb, etc.) and with data versioning and lineage tooling (e.g. DVC, LakeFS, Delta/Iceberg)
- Good command of Python; experience with containerised data pipelines
- Strong command of English; Dutch is a plus
- Strong hands-on experience with AI workflows and engineering tooling
- Experience with data governance and/or privacy regulation (e.g., GDPR, AI act)
Preferences.
- Experience in management of large AI datasets, with versioning, reproducibility, and automated data-quality monitoring
- Knowledge of NLP data pipelines (language detection, PII detection, text normalisation)
- Familiarity with workflow orchestration tools (e.g. Airflow)
- Knowledge of linked data, RDF
- Experience with distributed data processing on compute infrastructure (HPC systems, Kubernetes)
- Experience with ETL/ELT pipelines, data lakes, data warehousing
- Knowledge of FAIR principles and experience with metadata standards
How to apply
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