Data Engineer

It's more than a job

In this IT role, you will manage, develop, and support the technology infrastructure that powers Kuehne+Nagel’s global systems and operations. This contribution is vital to making both special and ordinary moments in the lives of people a reality. In fact, your work enables our teams to deliver fresh fruit, time-sensitive healthcare products and much more to people all around the world. Working in IT at Kuehne+Nagel contributes to more than we imagine.

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As a Data Engineer (m/f/d), you will design, build, and maintain reliable data pipelines that support reporting, analytics, and data-driven decision-making. Working closely with analysts, product owners, and other engineers, you will contribute to the development of scalable data solutions while following established engineering best practices. You will take ownership of your work, collaborate across teams, and continuously improve the quality, reliability, and performance of our data platform.

How you create impact

• Develop, maintain, and enhance scalable data pipelines and transformations, including supporting the modernization and migration of legacy data processes.
• Build and maintain data models that enable efficient reporting, analytics, and business insights.
• Apply data engineering best practices, including coding standards, testing, documentation, version control, and CI/CD, to deliver high-quality solutions.
• Develop and maintain data orchestration workflows, ensuring reliable, observable, and efficient data processing.
• Collaborate with analysts, product owners, and engineering colleagues to understand business requirements and deliver effective data solutions.
• Monitor data quality, troubleshoot technical issues, and contribute to continuous improvements in platform reliability and performance.
• Participate in code reviews, knowledge sharing, and team discussions to help improve engineering practices and support a collaborative team environment.

What we would like you to bring

• Bachelor's degree in IT, Computer Science, or a related field, or equivalent practical experience, with 5+ years of experience in data engineering.
• Strong expertise in SQL, data transformation, and data modeling methodologies (e.g., dimensional modeling and Data Vault) for large-scale data warehouse environments, ideally using Snowflake.
• Hands-on experience with data transformation tools, particularly dbt, as well as version control systems (Git/GitLab), CI/CD practices, and data orchestration platforms.
• Proficiency in Python; experience with additional programming languages is an advantage.