Data Engineer at Unisystems

Brussels, Brussels-Capital, Belgium -

Full Time

Start Date

Immediate

Expiry Date

18 May, 26

Salary

0.0

Posted On

17 Feb, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Spark, SQL, Azure Synapse, Microsoft Fabric, AWS Glue, Data Modelling, Database Design, Medallion Architecture, Microsoft Power BI, Machine Learning, Natural Language Processing, LLMs, DevOps, Git, CI/CD

Industry

Information Technology & Services

Description

At Uni Systems, we are working towards turning digital visions into reality. We are continuously growing and we are looking for a Data Engineer to join our UniQue team. What will you be doing in this role? Collect and analyze business requirements; define robust data models and scalable architectures. Design and build scalable, reliable data pipelines and workflows in cloud environments. Apply DevOps practices, including Git-based version control and participation in CI/CD pipelines. Ensure data quality, security, and governance standards are maintained across all data-related activities. Collaborate with cross-functional teams to align data solutions with business needs and quality expectations. Specify and design presentation interfaces with optimal usability and user experience. Document processes and tasks to ensure transparency, explainability, and team-wide understanding. Support the integration of AI-based enrichment and transformation processes into existing data pipelines and workflows. What will you be bringing to the team? Master's degree in IT with minimum 11 years of relevant experience (or Bachelor's degree and minimum of 15 years relevant experience). 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. Ability to integrate AI and ML techniques into data workflows for enrichment, categorisation and transformation. Strong, hands-on coding ability for data processing, analytics, and automation. Ability to collect, analyse and translate business needs into technical specifications. Skills in designing conceptual, logical and physical data models. Ability to extract, transform, load, clean and merge datasets from multiple sources. Experience with automated workflows and orchestration tools. Ability to handle large and complex datasets efficiently. Desirable Understanding of Microsoft Power Platform (e.g., Power Automate, SharePoint Lists). Good knowledge of Microsoft Fabric components, including Lakehouses, Pipelines, Dataflows Gen2, Notebooks, and Semantic Models. Good knowledge of 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 of no-code/low-code data science platforms such as KNIME and/or Dataiku. Experience documenting and organizing processes using task management tools (e.g., Jira, OpenProject) and documentation platforms (e.g., Confluence, GitLab Wiki, GitHub Wiki). Proficiency in English at least at level B2. At Uni Systems, we are providing equal employment opportunities and banning any form of discrimination on grounds of gender, religion, race, color, nationality, disability, social class, political beliefs, age, marital status, sexual orientation or any other characteristics. Take a look at our Diversity, Equality & Inclusion Policy for more information.

Responsibilities

The Data Engineer will be responsible for collecting business requirements, defining data models, and designing/building scalable data pipelines and workflows within cloud environments. This role also involves applying DevOps practices, ensuring data quality and governance, and collaborating with cross-functional teams.