Who We Want

An experienced Data Engineer.

You bring 3+ years of building and running data pipelines in production, ideally at a SaaS company. You've owned a data platform, or a large part of one, and you're comfortable making the architecture calls yourself.

Strong in SQL and dbt.

You write and review complex SQL with ease. You've built dbt projects with layered models, tests, documentation.

At home in the Cloud.

You have worked with Data Warehouses in the Cloud, ideally AWS Redshift. BigQuery or Snowflake is also fine.

Reliability-minded.

You think in tests, freshness checks, idempotent loads and backfills. You want to know the data is wrong before a stakeholder tells you.

Curious about the business.

You want to know what MRR, churn or an "active workspace" really mean, and you model data so those definitions hold everywhere they're used. Experience with subscription billing or product usage data is a plus.

Who We Don’t Want

Pure dashboard builder.

You'd rather spend your days building dashboards and answering ad-hoc questions. That's our Business Analyst's job; yours is to build what they work from.

Pure pipeline plumber. 

You move data from A to B but don't care what it means. Gold tables are only as good as the business understanding behind them.

Over-engineer.

You want Kafka, Spark and a lakehouse for data that fits comfortably in Redshift. We need pragmatic solutions that fit a 100-person company and can grow with it.

Remote-only operator.

You prefer fully remote work and don't want to build relationships in person. The data platform sits between many teams, and that works best with regular time together in our Berlin office.

Our Data Stack

• Warehouse: AWS Redshift
• Ingestion: Airbyte, custom Python
• Transformation: dbt
• Orchestration: Dagster
• Reverse ETL: Hightouch
• BI: Metabase
• Infrastructure & CI: AWS, Terraform, GitHub Actions

Your Day To Day

• Own ingestion from our product database, Chargebee, HubSpot, Pipedrive, Intercom, Amplitude and other tools, using Airbyte where it works and your own Python where it doesn't.
• Design and build the dbt models that take raw data to gold tables, with clear layers, naming and documentation that the Business Analyst and the rest of the company can rely on.
• Run orchestration in Dagster: schedules, dependencies, freshness checks and alerting, so a stalled sync gets noticed within hours.
• Build data quality in: dbt tests, source checks and CI on every pull request.
• Capture history where our source systems only keep the current state, so a question like "which plan was this customer on last March?" has an answer.
• Run the warehouse: Redshift performance and cost, access management, and AWS infrastructure in Terraform.
• Maintain the reverse ETL syncs (Hightouch) that send data back into HubSpot, Intercom and the other tools our teams work in.