Data & AI Engineer

Required Experience Skills (Must-Have) <1.Data> Engineering Architecture • Proven experience in designing and implementing data pipelines (ETL/ELT) • Strong knowledge of data lake / data warehouse architectures • Experience with modern data platforms (e.g. Microsoft Fabric, Azure ecosystem, Open Source data platforms) 2. Handling Structured Unstructured Data (Critical Requirement) • Demonstrated experience in handling and integrating: o Structured data (databases, tabular datasets) o Unstructured data (documents, reports, PDFs, text corpora) • Ability to build pipelines enabling end-to-end exploitation of heterogeneous data • Experience preparing data for reporting and advanced analytics • Ability to structure unstructured information using metadata, classification, and transformation techniques 3. Analytics AI • Experience with analytics development and data modelling • Exposure to AI / ML solutions, particularly on text or document-based data • Understanding of explainability and traceability principles 4. Engineering Practices • Knowledge of DevOps / CI-CD / pipeline automation • Ability to deliver in multi-stakeholder environments 5. Nice-to-Have Skills • Experience with classified or restricted environments (EUCI or equivalent) = • Familiarity with Microsoft Purview or similar governance tools • Experience with: o Data catalogues and business glossaries o Cross-domain data handling o Hybrid or sovereign cloud environments • Experience in public sector / defence / EU institutions • Experience in AI explainability or human-in-the-loop systems 6. Profile Mindset The ideal candidate demonstrates: • Strong engineering mindset with focus on scalable and modular solutions • Ability to operate in highly governed and security-sensitive environments • Pragmatic and structured approach to delivering focused on value and feasibility • Able to adapt in priorities adjustments • Able to adapt in different technologies • Strong collaboration and communication skill