Data Engineer in Data Product Engineering
If you choose to apply, please read the job ad in full and include a short cover letter. We are most interested in hearing from the real you, so we encourage authentic applications written in your own words.
The opportunity in a nutshell
Interested in combining hands-on data engineering with close collaboration across the business? This role could be a good fit for someone with 4+ years of experience who enjoys building reliable data products and working with others to make them useful.
Role: Join as a Data Engineer in our Data Product Engineering team within Schibsted's Data & AI organization, where we build reliable, well-governed, and scalable data products used across Finance, HR, Product, Subscription, and other parts of the business.
Core tools and practices: Our everyday toolkit includes SQL and Python as core languages, together with Snowflake, dbt, Airflow, AWS, Terraform, and Git, supported by solid engineering practices around testing, documentation, CI/CD, and version control.
Company: Schibsted is home to some of the most established and widely used media brands in the Nordics, including Aftenposten, VG, Svenska Dagbladet, Aftonbladet, E24, Bergens Tidende, and Stavanger Aftenblad. Across our broader Data & AI focus, data, analytics, and AI help us build better products, support smarter decisions, and create value across those newsrooms and businesses.
Location: This role is based in Oslo or Stockholm. We work in a hybrid setup, which means this is not a fully remote role, and candidates need to reside in Sweden or Norway and be able to work from one of our offices at least 2 days a week.
Why this role: You will join a relatively new team with room to contribute, learn, and help shape how we work as we continue to build trusted data products across Schibsted.
Sounds like your kind of role? Read on
Who are you?
We are looking for someone with a solid foundation in data engineering who is also excited by the opportunity to keep learning, deepen their craft, and grow together with the team.
You have around 4+ years of experience in data engineering or a closely related role, with a track record of building reliable data solutions in production environments.
Strong SQL skills are important, along with a solid understanding of data modeling, data warehousing, and how to design data products that are useful, trustworthy, and maintainable.
You can write and debug SQL and Python yourself at a strong professional level. AI-assisted tools are welcome, but this role requires solid enough fundamentals to solve everyday engineering problems without depending totally on them.
On the Python side, we are looking for good software engineering fundamentals, including the ability to structure maintainable applications and apply object-oriented design principles where appropriate.
You have hands-on experience with dbt in production environments and solid working knowledge of the wider toolkit we use, including Airflow, Snowflake, AWS, Terraform, Git, and Python and / or similar tools.
You will likely enjoy this role if you are comfortable making pragmatic technical decisions in environments where requirements evolve and not everything is fully defined upfront.
You care about engineering quality and can contribute to practices around testing, documentation, observability, governance, and cost-efficient pipeline design.
Clear communication matters just as much as technical strength. We value people who can work well with both technical and non-technical stakeholders, explain tradeoffs clearly, and contribute positively to team collaboration and knowledge sharing.
What's the job like?
As a Data Engineer, your job is to help us build data as a product, not just pipelines that move data from one place to another. You will create robust data models, improve the reliability and scalability of our pipelines, and help shape the engineering practices that make our data products trusted and reusable across Schibsted Media.
What you will be expected to do
Collaborate with stakeholders to translate business needs into pragmatic, well-scoped data solutions.
Design, build, and maintain reliable pipelines and transformations in Snowflake, dbt, Airflow, and Python.
Develop scalable data models that support analytics, reporting, and future AI use cases.
Improve technical quality through testing, documentation, monitoring, and maintainable engineering practices.
Contribute to good architectural and implementation decisions across the team.
Contribute through code reviews, pairing, and knowledge sharing within the team.
Work closely with adjacent platform and Data & AI teams to align on standards, dependencies, and shared ways of working.
What success looks like
Success in this role means growing into a confident contributor who understands our data landscape, ways of working, and stakeholder needs. Over time, that includes contributing across a mix of smaller support tasks through our weekly rotation as well as larger pieces of work, helping build reliable pipelines and data models, and developing the judgment to deliver pragmatic solutions together with the team. Depending on your experience and the needs of the work, that can mean contributing as part of a team or taking the lead on a project.
Why join us?
You will join a team that is still shaping its ways of working, which means there is real room to contribute, learn, and grow.
You will work on data products with visible impact across Schibsted, supporting both technical and non-technical users in domains such as Product, Subscription, Finance, and HR.
You will be part of a company whose work matters in everyday life across the Nordics, helping support trusted media brands through better data, analytics, and AI capabilities.
You will work in an international environment with strong trust, autonomy, and good opportunities for learning through engineering communities and collaboration across Data & AI.
Got your attention? Let us hear from you!
The application period closes on 28 September 2026. We review applications on a rolling basis, so we encourage you to apply as soon as possible.