Junior Data Engineer
Descrizione dell'offerta
YOUR NEW COMPANY
✨ Kirey is one of the leading European groups in technology consulting and system integration, with a distinctive positioning in the field of data‑driven innovation.
Through a comprehensive portfolio of solutions—including Data & AI, Cloud, Software Development, Cybersecurity, Infrastructure & Automation, and Monitoring—Kirey transforms data complexity into intuitive, accessible solutions, empowering clients to achieve their business goals.
Headquartered in Italy, with a solid international presence and nearly 1,500 employees, Kirey has delivered more than 10,000 projects for leading clients across the Insurance, Banking, Manufacturing, Retail, Public Administration, and Services & Energy sectors.
At the heart of all this are people, who at Kirey find the space to fully express their talent and to feel like an active part of the company’s value chain. This is made possible through continuous investment in improvement processes, supported by training; innovative ideas and encouragement of creativity, driven by consolidated expertise; and a strong commitment—reflected in dedicated initiatives—to inclusion and diversity.
YOUR ROLE
We are looking for a motivated Junior Azure Data Engineer with 1–3 years of experience to join our Data & Analytics team, working within the Microsoft Azure ecosystem, particularly Azure Fabric.
Responsibilities
- Design, build, and maintain data ingestion and transformation pipelines.
- Develop and manage ETL/ELT workflows using Azure services.
- Work with data stored in cloud-based data lakes and warehouses. Build and manage Lakehouse architectures
- Develop data models and transformations using Spark / SQL within Fabric
- Work with OneLake and structured/unstructured enterprise data
- Ensure data quality, reliability, and performance optimization.
- Collaborate with cross-functional teams to deliver data-driven insights.
- Support data modeling and integration activities within the Azure environment.
- Contribute to documentation and continuous improvement of data processes.