This is a recruitment:
We are Sweden's largest digital healthcare provider. By combining physical clinics with a modern digital platform, we serve millions of consultations every year across Sweden, Germany, and Norway. Our 25-person engineering team operates with startup autonomy inside a profitable, international. We run OpenAI and Anthropic models in production across patient and clinician workflows, and that footprint is growing fast. AI is a real working tool here, not a buzzword. We use it daily, we expect you to as well, and we hire people who treat it as a force multiplier.
...What You'll Do
Our data platform works, but it was built to solve yesterday's problems. We have data scattered across Postgres databases, Amplitude, S3 file dumps, and a tangle of Python jobs that move it around. The reports get built and the business runs on them, but trust is brittle, reconciliation is too manual, and the foundations need work before we can scale to where we're going.
We need someone who can help us rebuild this, not maintain what we have, but reshape it. If you want a place where the warehouse is clean and the pipelines just run, this isn't it. If you want to redesign a data platform from the foundations up, with real ownership and the leverage to make it good, here is what you will own:
- Reshape the data foundations: Drive multi-tenancy, retention policies, and data contracts with backend services.
- Decide the future warehouse: Determine what the warehouse actually should be: self-hosted Postgres + dbt, managed Snowflake or BigQuery, or something else. You will shape this direction.
- Unify disparate data sources: Connect three main data streams that don't currently talk to each other: our operational databases, Amplitude product analytics, and a long tail of S3-based partner integrations.
- Build platform observability: Implement data lineage, freshness checks, and alerting that delivers actionable value.
- Own core business reporting: Own the reports that finance, operations, and the board rely on. Make those reports trustworthy by design, not through manual reconciliation.
- Work directly with domain teams: Collaborate closely with backend, product, and finance teams. You're not a service desk, you're a peer shaping how data gets produced upstream, not just consumed downstream.
- Leverage AI aggressively: Use AI for pipeline boilerplate, schema generation, transformation logic, and exploratory analysis, maintaining the same engineering discipline we hold ourselves to.
You will be part of a small data engineering team. That means a lot of ownership and direct impact without layers of management.
