Data & AI Engineer

COSMOTE Global Solutions NV is seeking a skilled Data & AI Engineer to join our dynamic ICT team. As part of the OTE Group of Companies, we specialize in delivering cutting-edge ICT solutions and services spanning Cloud Services, Data Centre operations, Networking, Cybersecurity, Business Intelligence, Big Data, and more.

In this role, you will contribute to the design, development, and deployment of core platform components, with a focus on data engineering, analytics, and AI-enabled capabilities. You will work across structured and unstructured data to enable reporting, advanced analytics, and AI-driven use cases. Collaborating closely with architects, developers, project leaders, and stakeholders, you will help deliver scalable, high-performance data solutions that meet the evolving technological needs of our clients.

Responsibilities:

• 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.

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, defence or EU institutions.
• Experience in AI explainability or human-in-the-loop systems.

Profile & Mindset:

• Hold, or be in a position to obtain, a valid Personnel Security Clearance Certificate, national or EU PSC at SECRET UE/EU SECRET level.

• Strong engineering mindset, with a focus on scalable and modular solutions.
• A pragmatic and structured approach, focused on value, feasibility and incremental delivery.
• Ability to operate in highly governed and security-sensitive environments.
• Pragmatic and structured approach to delivery, focused on value and feasibility.
• Ability to adapt to changing priorities.
• Ability to adapt to different technologies.
• Strong collaboration and communication skills.