Data Engineer

DHL Group the logistics provider of the world with over 580,000 employees in more than 220 countries and territories. We are an organization that is not just moving goods around, we have a very clear purpose, and we are: Connecting People. Improving Lives. We provide challenges and opportunities for personal and professional development, we recognize the difference you bring to our business and together we share the pride of building one of the largest world leaders in logistics and supply chain.

Are you ready to shape the next generation of steering within one of the most dynamic logistics environments in the world?

Our data professionals for a German speaking country are at the forefront of transforming how data drives business decisions. We deliver real-time, business-critical insights across reporting, forecasting, and planning – empowering leadership with the intelligence needed to act with precision and speed.

But we don’t stop there.

We are pioneering the digitalization of processes, integrating cutting-edge technologies and emerging trends to redefine how data creates value. Our mission is clear: to turn information into impact and unlock measurable business advantage through smart, scalable, and future-proof solutions.

What sets us apart is our strong team spirit – a culture of collaboration, mutual support, and shared ambition. We believe that the best ideas emerge when diverse minds come together, and we foster an environment where everyone contributes to shaping the future.

If you're a visionary professional with a passion for data-driven strategy, digital innovation, and operational excellence – this is your opportunity to make a difference at scale.

Step into a role where your expertise fuels transformation. Shape the future with us.

Key Responsibilities

Data Engineering & Architecture

• Design and maintain robust, scalable data pipelines using Kubeflow, Python, and Snowflake, ensuring optimal performance and reliability.

• Follow best practice standards for deployment and the use of CI/CD tools.

• Govern end-to-end data architecture with a focus on data quality, security, compliance, and lifecycle management.