Data Engineer

Viktor is the AI employee. He lives in Slack and Microsoft Teams, connects to thousands of tools, and does real work for real companies: finance, marketing, ops, engineering. We're building the product that replaces half the SaaS stack.

The team is small. The scope is not.

You own the truth. You use code and AI agents to build the pipelines, answer the questions, and ship the dashboards everyone else relies on. You own whether the numbers are right, not whether you typed every query yourself. You don't wait for someone to tell you what's happening; you go find out, then bring the answer to the team. Equal parts engineer and analyst. You ship.

The data foundation is already in use and still evolving. You improve the warehouse, pipelines, dashboards and alerts while keeping them dependable as the product and company change.

We are moving to lifetime gross profit, LTGP, as the value the ad platforms bid on. A first model runs in shadow today. The conversion tracking, attribution data and pipelines behind it are part of this role.

You'll join our analytics function early, with no data lead above you. The foundation is in motion, not finished: you take real ownership of the warehouse, pipelines, dashboards, and alerts, and build them out. You report to Peter Albert.

• Own the ClickHouse warehouse end-to-end. Schema, ingestion, backfills, performance. You decide how it's structured and what gets in.

• Build and maintain the pipelines. Prod Postgres, Stripe, Slack, ad platforms, whatever's next. You build and operate them, with tests, monitoring and safe backfills. You own the recovery when they fail and verify the data, not just that the job ran.

• Own conversion tracking and attribution data. The events and values we send to Meta and Google, the attribution data that comes back, and the feed that carries LTGP to the platforms. Miloš runs this today and stays on it; you add coverage and extend it.

• Run the BI layer. Hex dashboards for growth, finance, support, product. You build, maintain, and prune them; dead dashboards are worse than none.

• Answer the questions that move the business. Retention cohorts, credit and unit economics, channel attribution, churn, pricing impact. You bring answers proactively.

• Stand up monitoring and alerting. We hear about a metric moving the wrong way from your alerts, not from a customer.

• Work with the co-founders and leaders across the company. You scope, prioritize, ship fast, and push back when the question isn't the right one.

• The team makes decisions faster because the numbers are clear and trusted.

• People stop building parallel spreadsheets. The dashboard is the source of truth.

• When a metric moves, you knew first.

• The co-founders stop pulling numbers themselves, because you're better at it.

• New hires can answer most business questions without asking anyone.

• Metrics understanding. You understand what business metrics mean and how they are calculated. You reason about definitions, denominators, cohorts and segmentation, spot misleading comparisons, and connect metric changes to what is happening in the business.

• Strong Python engineering for ingestion, transformations and orchestration. You build, test and maintain reliable pipelines, not just notebooks.

• Agentic engineering is part of your daily workflow. You use coding agents to move faster while taking responsibility for correctness, maintainability and production behavior.

• Familiar with columnar warehouses (ClickHouse, Snowflake, BigQuery, Redshift). Bonus for ClickHouse, since that's what we're on.

• Built BI layers from scratch (Hex, Metabase, Looker). You know a dashboard that gets used from one bookmarked once and forgotten.

• Product mindset. Proactive, challenges assumptions, biases toward action.

• Founder mentality. You act like an owner of the function, not a ticket-taker in it.

• Munich or Warsaw.

• No layers. No data lead above you. You own the foundation.

• The work you ship defines how the company makes decisions. As the warehouse and BI layer improve, every team gets faster.

• The questions are real: retention cohorts, credit economics, channel attribution at the unit level. The answers change what we do next quarter.

• Your data decides what the ad platforms bid on. Matt, who owns paid acquisition, is a named stakeholder and you sync with him weekly.

• The co-founders already believe data is strategic. No convincing anyone it matters.

• Experience taking ownership in a small, fast-moving team.

• Familiarity with Stripe and SaaS metrics: MRR, ARR, NDR, LTV. You've helped close the books, not just queried them.

• Hands-on with conversion tracking and attribution data: Meta Conversions API, Google Ads conversion imports, server-side event pipelines.

• Background in ML or forecasting. Helpful, not essential.

• You've worked where founders pulled their own numbers and you loved replacing that habit.

Small team, high trust, low process. Decisions are made by owners, not committees. You will ship your first week. You will talk to users your first day.

We're building an AI employee that does real work across a company's tools.

This is a rare window: everyone here owns something real. Not a task. A surface of the company that customers depend on.

That doesn't last forever. Right now, it's still true.

Top-of-the-market salary and the kind of ownership that only exists at this stage.

The best work happens when you're in the room. This role is Munich or Warsaw.