Data & AI Engineer

Language: English

2 days a week onsite

Only EU Citizen

Tasks:

• Develop and maintain data pipelines that integrate multiple heterogeneous data sources, including both structured and unstructured information.
• Implement data ingestion processes, including batch and near-real-time processing.
• Perform data cleansing, validation and standardization to ensure reliable and consistent datasets.
• Apply metadata tagging and support data lineage across relevant data flows.
• Contribute to the development and maintenance of the Data and Product Catalogue.
• Implement data quality checks, validation rules and monitoring mechanisms across data pipelines and analytical workflows.
• Support the identification and remediation of data quality issues in cooperation with Data Stewards and relevant governance teams.
• Contribute to the creation of reusable datasets and data products from heterogeneous information sources.
• Design solutions that combine structured data, such as databases and tabular datasets, with unstructured data, such as documents, reports and text.
• Transform unstructured information into formats suitable for analysis, reporting and AI enabled processing.
• Enable unified analytical workflows and reporting across mixed data types.
• Support AI-driven processing of document-centric data.
• Implement analytics capabilities that support operational and strategic decision-making workflows.
• Contribute to the design and implementation of AI-enabled use cases.
• Ensure AI outputs are explainable, traceable and supported by appropriate human-in the-loop controls.
• Automate data pipelines, reporting workflows and recurring analytical processes.
• Implement event-driven processing, alerts and triggers where relevant.
• Support monitoring, logging and operational observability of platform processes.
• Implement security controls aligned with EU requirements, including identity and access management, encryption of data at rest and in transit, and audit logging.
• Support the separation of classified and unclassified environments.
• Contribute to solutions that can be deployed in secure or air-gapped environments.
• Build modular and scalable platform components using open standards and APIs.
• Contribute to integration with existing EDA systems and external data sources.
• Support hybrid and sovereign deployment approaches.
• Produce clear technical documentation and support knowledge transfer to relevant stakeholders.

Mandatory Requirements:

Data Engineering & Architecture

• Proven experience in designing and implementing data pipelines, including ETL/ELT.
• Strong knowledge of data lake and data warehouse architectures.
• Experience with modern data platforms, such as Microsoft Fabric, Copilot, the Azure ecosystem, and open-source data platforms.

Handling Structured & Unstructured Data (Critical Requirement)

• Demonstrated experience in handling and integrating structured data, such as databases and tabular datasets, and unstructured data, such as documents, reports, PDFs, and 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.

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.

Engineering Practices

• Knowledge of DevOps, Git, CI/CD, and pipeline automation.
• Ability to deliver in multi-stakeholder environments.

Additional Desirable Skills:

• Experience with classified or restricted environments, such as EUCI or equivalent.
• Familiarity with Microsoft Purview or similar governance tools.
• Ability to create and work with MCP servers.
• Experience with data catalogues and business glossaries.
• Experience with cross-domain data handling.
• Experience with hybrid or sovereign cloud environments.
• Experience in public sector, or EU institutions.
• Experience in AI explainability or human-in-the-loop systems.