Data Engineer

Join the Future of DHL

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 BI specialists 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 Pipeline Engineering

• Architect, develop, and maintain scalable and resilient data pipelines using Kubeflow and Python, enabling efficient ingestion, transformation, and enrichment
• Ensure seamless integration of various internal and external data sources into Snowflake, converting them into relational formats optimized for reporting and analytics.
• Build up operational frameworks for logging and monitoring across data pipelines and systems

Data Modeling & AI/ML Enablement

• Design Snowflake-based data models tailored for AI/ML readiness, supporting e.g. predictive analytics, anomaly detection, or automated alerting systems.
• Collaborate with data scientists and business analysts to implement and maintain a robust machine learning life cycle