Data Engineer
Skatteguiden
Tech-first. Ego-free. User-obsessed. Over a million Danes use Skatteguiden to understand and optimise their taxes — automating tax tracking, surfacing deductions people didn't know they had, and connecting directly to the Danish tax authority to do it. We're an independent, 50-person company based in Copenhagen with a 4.7-star rating and one mission: make financial insight accessible to everyone, not just those who can afford an accountant.
None of that works without a data platform people can trust. Ours is built on Snowflake, Dagster, and dbt, turning messy source data into the numbers people rely on. We're investing further in it, and we need a data engineer who can take real ownership of it — from infrastructure and ingestion down to the SQL.
What you'll be working on
- Own end-to-end data ingestion pipelines — from source systems into Snowflake — partnering with our product and platform teams to shape data contracts and keep pipelines resilient as those systems evolve.
- Own orchestration in Dagster — scheduling, dependencies, observability, and failure recovery across the pipeline.
- Own our Snowflake infrastructure and custom components as Infrastructure-as-Code — provisioning, environments, access, cost — using Terraform (and ideally DCM).
- Push our data engineering practices forward — testing, CI/CD, semantic layer modeling, performance on Snowflake.
- Shape how we use AI in the data stack.
- Design and maintain dbt models that turn raw Snowflake data into trustworthy, well-tested data products — including owning dbt Cloud itself: environments, jobs, and deployments, not just the modeling layer.
- Get exposure to Java (or another OOP language) and event-driven architecture.
- A track record of owning data ingestion pipelines end-to-end, including the messy parts — source system quirks, backfills, schema drift.
- Experience owning orchestration in a tool like Dagster (or similar — Airflow, Prefect), and comfort being the one who gets paged when a DAG fails.
- Experience managing cloud data infrastructure as code — Terraform required, DCM preferred.
- Strong hands-on experience with dbt and a cloud data warehouse — including dbt Cloud administration (environments, job configuration, deployment), not just model development.
- Comfort working directly with engineering teams outside of data, translating platform and product needs into data models.
- Java or another OOP language, and comfort with event-driven architecture.
- Exposure to applying AI/LLMs within a data platform.
- Real ownership — a small team where what you design ships, infrastructure included, not one buried in a 40-person backlog.
- A modern stack, not legacy debt: Snowflake, Dagster, dbt Cloud, Terraform, Evidence, Lightdash — built recently, built to be extended.
- Work that sits close to product and platform, not isolated in a data silo — your input shapes what gets built upstream, not just what you transform downstream.
- A problem worth solving: Danish tax law is genuinely complex, and modeling it well is a real engineering challenge.
- Impact you can point to — your pipelines and models power a product over a million Danes actually use to get their taxes right.
- Office first in Copenhagen — five minutes from Rådhuspladsen, lunch at Claus Meyer, dinner covered if the day runs long.
Start: As soon as possible Reports to: Head of Data & Operations Planning
Hiring process
- 30-minute online screening call with recruiter.
- 1-hour onsite case session with hiring manager and team member.
- 30-minute onsite meet-and-greet with CEO.