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Linkfire is seeking an experienced data platform engineer to own and evolve our data pipelines. You will build production-grade Python services and maintain Snowflake-driven pipelines, with test coverage and monitoring in place.
You’ll contribute to AI-driven data investigations and work with product managers to embed data in features from idea to production. We value curiosity, T-shaped skills, and a strong bias for practical, scalable design.
This role is open to candidates living within one hour of Danish time, all year round: Western, Central or Eastern European Time (WET, CET or EET). Applications from outside these timezones will not be considered. About Linkfire and the team Linkfire is the marketing platform the music industry uses to connect fans with the music they love. Every click on our smart links runs through our platform, millions of times a day, and artists and labels rely on our reporting to understand how their campaigns perform. Our data platform turns that traffic into analytics inside the product, reporting for major labels, and the numbers the business runs on. Data here is part of the product, not a by-product of it. Engineering is small and distributed, so you get a wide surface area and real ownership. The platform is still taking shape, and you will have a real say in where it goes next: architecture, tooling and the standards we hold ourselves to. You will work alongside the engineers who built this platform, and they will bring you into how it all fits together.
Help steer how the platform evolves, and grow into owning it end to end. Build and maintain our production data pipelines on Snowflake and Airflow, along with the Python services that power them. Build quality in from the start: everything you ship comes with tests and monitoring, rather than having them added later. Help build the AI agents that take repetitive data questions and investigations off the team's plate. Work with Product Managers and stakeholders early, so data is designed into features rather than added afterwards, and explain your choices to people who are not engineers. Take requests or roadmap items from idea to production, including challenging concepts and decisions early. Own the day-to-day flow of data work: requests from product and customer-facing teams, changes to existing pipelines, and new sources and feeds. Model data so analysis and reporting are easy. Dashboards stay with our analyst, but you know what they need and you build for it. Leave the platform better than you found it: documentation, tests, standardisation, and retiring what we no longer need.
What we are looking for Five or more years building production data pipelines. Strong Python. You have built services from scratch, with automated tests, sound design patterns and code that stays easy to maintain and scale. Deep experience with a cloud data warehouse in production, ideally Snowflake. If your depth is in BigQuery, Redshift or Databricks, we will give you room to go deep on Snowflake in your first months. Hands-on with Airflow or a similar orchestrator, and comfortable with Docker, Kubernetes and Argo CD. How you work Product at heart. You see data end to end, from how it is collected to who uses it and why. Curiosity beyond your own stack. We work T-shaped: deep in data, interested in everything around it. You spot problems and bring solutions, and you find the balance between doing it right and getting it done. You use AI daily and have opinions on where it helps. Tell us about it when you apply. You communicate clearly and directly, mostly in writing. The stack This is what we run today. You don’t need experience with all of it, and we’ll help you get up to speed on the rest. Snowflake, with production pipelines in Python on an in-house framework. Airflow on Amazon MWAA for orchestration. Weld for ELT and reverse ETL, Metabase for internal and embedded dashboards. AWS (SNS, S3), Docker, Kubernetes and Argo CD. GitHub Actions, Terraform and Atlantis for CI/CD and infrastructure as code.
Hands-on experience with parts of our stack beyond Snowflake and Airflow, such as Terraform, Weld or Metabase. Event-driven ingestion on AWS. Building with LLMs, for example tooling that automates investigations or answers questions about data.
Copenhagen office if you want one, fully remote if you do not, as long as you live within one hour of Danish time all year round: UTC+0 to UTC+2 in winter, UTC+1 to UTC+3 in summer. That covers WET, CET and EET, from Portugal in the west to the Baltics and Finland in the east. Full-time, permanent.