Data Scientist

• Collect business requirements, definition of robust data models and architectures

• Design, and build scalable and reliable data pipelines and workflows in cloud environments

• Apply DevOps practices, including basic Git workflows and involvement in CI/CD pipelines.

• Contribute to maintaining data quality, security, and data governance standards across all data-related activities.

• Collaborate with cross-functional teams to ensure data solutions align with business needs and quality standards.

• Specification and design of presentation interfaces with optimal usability/user experience

• Document processes and tasks to ensure explainability and understanding across the team

• Support the integration of AI-based enrichment and transformation processes into existing data pipelines and workflows.

The following knowledge2, experience and skills are required for the performance of the above listed tasks:

• Business analysis & requirements gathering

• Ability to collect, analyse and translate business needs into technical specifications.

• Data modelling & architecture design

• Skills in designing conceptual, logical and physical data models.

• ETL/ELT and data integration

• Ability to extract, transform, load, clean and merge datasets from multiple sources.

• Building data pipelines & workflows

• Experience with automated workflows and orchestration tools.

• Big data management

• Ability to handle large and complex datasets efficiently.

Specific expertise:

• Εxcellent knowledge in Python, Spark and SQL

• Εxcellent knowledge in designing and building ETL pipelines using tools such as Azure Synapse, Microsoft Fabric and/or AWS Glue

• Εxcellent knowledge of data modelling and database design principles using the Medallion Architecture

• Good knowledge of business intelligence tools, notably Microsoft Power BI

• Knowledge of Machine Learning, Natural Language Processing and Large Language Models (LLMs) fundamentals

Additional skills:

• Understanding of Microsoft Power Platform (e.g., Power Automate, SharePoint Lists)

• Good knowledge with Microsoft Fabric components (Lakehouses, Pipelines, Dataflows Gen2, Notebooks, Semantic Models)

• Good knowledge with cloud environments (AWS or Microsoft Azure)

• Understanding of DevOps practices, including Git workflows and CI/CD pipelines with experience using tools such as Azure DevOps, GitHub, and GitLab.

• Knowledge with no-code / low-code data science platforms such as KNIME and/or Dataiku.

• Familiarity with European Commission IT ecosystem and best practices

• Documenting and organising processes using task management tools (e.g., Jira, OpenProject) and documentation platforms (e.g., Confluence, GitLab Wiki, GitHub Wiki).