(Senior) Data Engineer (m/f/d) - Azure

Your Mission:

As part of our Data Engineering team, you’ll design, build, and maintain robust data pipelines using Azure Data Factory, Databricks, Airflow, and other cloud-native tools. You'll shape how we ingest, transform, and serve data across our organization—powering analytics, reporting, and decision-making at scale.

This is a hands-on, impact-driven role for someone who enjoys working across big data, cloud platforms, and data infrastructure. You’ll collaborate with product, analytics, and engineering teams across countries and functions.

What you are expected to do:

• Design and implement scalable ETL/ELT pipelines in a lakehouse architecture using Databricks, Azure Data Factory, Azure Data Lake, and other modern tools.
• Create analytical data models and structures optimized for performance and usability.
• Drive best practices in data engineering, including CI/CD with Azure DevOps, Git workflows, testing, and monitoring.
• Integrate workflows and orchestration using Airflow and other tools as needed.
• Troubleshoot, monitor, and improve data quality, pipeline reliability, and job performance across distributed systems.
• Work with stakeholders to understand data requirements and deliver scalable, maintainable solutions.
• Stay on top of trends in cloud data platforms, open-source frameworks, and engineering practices.

What you should bring:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 5+ years of experience in data engineering, with a focus on cloud-native platforms.
• Hands-on expertise with Azure Data Factory, Databricks, Azure Data Lake, SQL, and Python.
• Familiarity with DevOps workflows (e.g., Git, Azure DevOps) and orchestration tools like Airflow.
• Solid understanding of data warehousing, data modeling, and modern data architecture.
• Strong communication skills and the ability to work cross-functionally with business and technical stakeholders.
• A proactive mindset, capable of owning outcomes and navigating ambiguity.