• Own the data platform end to end: ingestion and the raw layer, the ClickHouse warehouse, and the dbt project on top of it. You're the person who can say whether a number is wrong or the pipeline is.
• Make our numbers trustworthy: reliability, correctness, and a history that holds up, so that a trend is a record of what actually happened rather than a re-derivation of it.
• Own the metrics the company runs on , starting with our customer health score- the definition, the denominator, the eligibility rules and the known limitations, not just the SQL underneath them.
• Turn business questions into durable metrics: work directly with Customer Success, Sales, Product and leadership to understand what they're actually trying to decide, then build something defensible enough to decide on.
• Keep cost and complexity in check as we grow: warehouse architecture, query cost, and the surfaces that deliver data into BI and our CRM. All of it gets harder as the customer base multiplies.
• Set the standard for how data work is done here: one authoritative written definition per leadership-visible number, and the conventions that inform our Data function as it grows with us.

Technical Expertise

• Substantial experience in data engineering or analytics engineering , with production ownership of a warehouse and a transformation layer. We care more about evidence of ownership than about years served.
• ClickHouse in production, or similar: Snowflake, BigQuery, Redshift, Databricks.
• dbt at ownership level: incremental models, snapshots, tests, macros, CI/CD. Ideally you've inherited someone else's dbt project and left it better than you found it.
• Python data engineering: EL tooling, S3, CI-based orchestration that you’ve owned end to end, with no platform team behind you.
• Product event streams: PostHog, Amplitude, Segment, Mixpanel or similar. You understand how user-to-company identity and group association break, and what that does to every metric downstream.

Judgement and Ownership

• You have defined a business KPI end to end and defended it to non-technical people. This is the differentiator for us. An excellent engineer who takes requirements as given is not the right fit for this role.
• Analytical rigour: cohorts, correct denominators, sample size stated before findings. You'll say "we can't tell yet, and here's exactly why" rather than produce a confident chart.
• You act without waiting for a ticket , and you can name something you chose to stop doing.
• You're comfortable in front of senior stakeholders , including C-level, and can hold a technical line without losing the room.

Further Requirements

• Located within CET +/-2 .
• Nice to have: BI tool administration (we use Metabase), CRM and billing systems as data sources, dlt specifically, GDPR-driven data deletion work, Grafana, German, or B2B SaaS and legal-tech domain experience.
• Nice to have: you're used to non-technical colleagues querying data through AI tools, and you design for that.

🎯 Metric Ownership – Own a business-critical metric end to end, definition included — not just the pipeline that feeds it.

🛠️ A Real Platform – ClickHouse and dbt already in production, with genuine problems worth solving. No greenfield theatre, no maintenance seat.

⚡ Direct Access – A flat structure and a direct line to our Head of Engineering, CTO and CEO. No committee between you and a decision.

📈 Growth Stage – Roughly 4x customer growth a year at one of Europe's fastest-growing AI legal tech companies.

🏡 Fully Remote – Enjoy the flexibility of working from anywhere

💻 Top Equipment – State-of-the-art MacBook plus all the accessories you need

🏋️ Wellbeing Perks – Urban Sports Club membership to support your healthy lifestyle

If you're excited about owning the numbers an AI startup runs on and delivering the data that guides us in our scaling journey, we'd love to hear from you.