Senior Data Engineer: Build Scientific Data Pipelines

Novonesis; Gladsaxe Kommune; Hybrid; DKK 800,000 - 1,100,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

Novonesis seeks a (Senior) Data Engineer to design and deliver robust data pipelines across Benchling and Databricks. You will work closely with scientists and local digital teams to translate scientific needs into scalable data solutions and ensure data quality and lifecycle ownership.

The role combines hands-on delivery with technical leadership, emphasizing reusable components, APIs, and modern data-engineering practices while balancing short-term needs with long-term sustainability in a

Qualifications

• MSc degree in Computer Science, Software Engineering, Data Engineering, Bioinformatics, or related field.
• Strong experience designing, building, and maintaining production-grade data pipelines and operational data solutions.
• Advanced proficiency in Python and SQL; experience with APIs and data modeling.

Responsibilities

• Design, develop, and implement robust data pipelines and reusable components across platforms.
• Collaborate with scientists and digital teams to translate needs into data solutions.
• Provide leadership on architecture, APIs, data quality, testing, deployment, and lifecycle management.
• Strengthen data foundations by identifying reusable patterns and components.

Skills

Python

SQL

APIs

Data modeling

Version control

Automated testing

Software engineering practices

Education

MSc degree in Computer Science, Software Engineering, Data Engineering, Bioinformatics, or related field

Tools

Databricks

Benchling

Job description

Novonesis seeks a (Senior) Data Engineer to design and deliver robust data pipelines across Benchling and Databricks. You will work closely with scientists and local digital teams to translate scientific needs into scalable data solutions and ensure data quality and lifecycle ownership.

The role combines hands-on delivery with technical leadership, emphasizing reusable components, APIs, and modern data-engineering practices while balancing short-term needs with long-term sustainability in a