Scientific Data Engineer →

Scientific data engineer

Orbis is growing fast. As our biology and chemistry teams grow, we need our data infrastructure to scale as well. We are looking for a Scientific Data Engineer to build systems and structures that capture data accurately and smoothly so that the science team can focus on the science. You’ll work closely with biology, chemistry, design and data science teams to understand how they work and what they need from their data, identify areas for improvement and then build solutions using both commercial and custom tools.

This is a chance to join a growing biotech company at a crucial moment as we invest in our lab and data infrastructure, helping build a cutting-edge data platform, designing workflows and building pipelines.

This role would suit someone who loves data and code. Someone with a chemistry or biology background with a computational focus, or a software engineer with experience working in a scientific environment.

You will be working onsite in Copenhagen.

Responsibilities: Develop and manage the Orbis data platform, spanning commercial software and custom solutions,

including implementing new systems and migrating existing data

Work with chemistry, biology and data science teams to gather requirements, prototype solutions, and

harmonise data operations across the organisation

Write and maintain ETL data pipelines to streamline data ingestion and organisation

Safeguard data integrity and quality in line with FAIR principles

Collaborate with software engineers and data scientists to enable AI&ML operations

Act as the go-to contact for lab scientists using our data products and infrastructure

Qualifications & experience A degree in chemistry, biology, bioinformatics or a related field

A strong passion for data in drug discovery

3+ years of industry experience in a scientific environment, with hands-on exposure to drug discovery

data and how it flows from design through synthesis and testing

Good working knowledge of Python and common data libraries

Experience with relational databases and SQL

Experience designing data structures for scientific data

Strong communication skills and a track record of working collaboratively with scientists

Nice to have Experience with cloud infrastructure (AWS or GCP)

Software engineering practices such as Git, CI/CD, Docker and code testing

Experience with ELN or LIMS systems (e.g. Dotmatics, CDD Vault, Revvity Signals)

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