Data Engineer Joboplysninger | DLG

Data Engineer Joboplysninger | DLG

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Data Engineer

Lokation:

Fredericia, DK, 7000

Ansøgningsfrist:

7th of August 2026

Do you want to help build scalable and high-quality data solutions? Join us as a Data Engineer and contribute to the development of data products that support business decisions across DLG.

About the position

As a Data Engineer, y ou will contribute to the development and maintenance of our data platform and data pipelines. Working closely with experienced engineers and stakeholders, you will help deliver reliable, scalable, and well-structured data solutions that support business needs.

You will be part of an environment where collaboration, continuous learning, and quality are highly valued. While working with an established architecture and engineering framework, you will have the opportunity to develop your technical skills and grow your expertise in data engineering and cloud-based data platforms.

In this role you will

As our Data Engineer, you will work with the design, development, and maintenance of data pipelines and data products on Databricks. You will contribute to building solutions that are reliable, maintainable, and aligned with industry-leading engineering standards.

Your key responsibilities will include:

Develop and maintain data pipelines using Python, PySpark, and/or SQL

Support data ingestion processes across batch and streaming workloads

Implement data quality checks and validation processes within existing frameworks

Contribute to CI/CD processes and follow established data engineering development practices

Work with data governance principles, including data classification, access control, and compliance requirements

Collaborate with engineers and stakeholders to deliver high-quality data solutions and resolve technical challenges

About you

You are a data engineer with a focus on building reliable and well-structured data pipelines. You are comfortable working within an established architecture and contributing to the delivery of high-quality data solutions as part of a collaborative engineering team.

You have a curious mindset and take pride in delivering maintainable solutions. You communicate proactively, collaborate effectively with others, and are comfortable raising questions or challenges when needed.

We imagine that you:

Are proficient in Python and SQL

Have experience working with Lakehouse or medallion architecture principles

Have experience with data ingestion patterns, including batch and/or streaming workloads

Are comfortable using Git or similar version control tools

Have a solid understanding of CI/CD principles and data development practices

Have hands-on experience with Databricks and understand data quality and governance concepts

Communicate fluently in English, both written and spoken

Furthermore, it is an advantage if you:

Have experience with Databricks Asset Bundles / Declarative Automation Bundles (DABs) or similar deployment and configuration tooling

Have worked with streaming technologies such as Auto Loader, Kafka, Event Hubs, or similar solutions

Have experience working with Azure, AWS, or Google Cloud Platform (GCP)

What we offer

At DLG, you will join a collaborative and ambitious organisation where data plays an increasingly important role in supporting business decisions and creating value across the business.

We offer a flexible and supportive work environment with excellent opportunities for professional development, knowledge sharing, and career growth. You will work closely with experienced colleagues and become part of a culture characterized by collaboration, trust, and continuous improvement.

Practical information

The position is full-time and based in either Fredericia or Flensburg. Start date is expected to be in October/November.

Please submit your CV - applications will be reviewed on an ongoing basis. We expect to invite for the first round of interviews in the beginning of August.

If you have any questions that are not answered in the above, please contact Head of Data Engineering, Thomas Nielsen, at

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