Analytics Engineer
Team: Engineering
Location: Europe
Employment type: FullTime
TL;DR
Co-own Formula's marketing & growth analytics alongside the Head of BI — answer the questions that move CAC, LTV, retention, and unit economics, and own the data layer that makes those answers trustworthy. This is a data analyst seat with full engineering keys: SQL and statistics as your first language, dbt / Dagster / Snowflake / Metabase as the toolbox you already know how to use without help.
WHAT WE'RE LOOKING FOR
- A strong analyst first. You don't ship a model unless you can defend the decision it supports.
- Comfortable owning your own pipeline end to end so no one is between you and the data — but the analysis is the deliverable, not the DAG.
- Already lives in marketing and payments data: ad hierarchies, attribution windows, LTV, cohort behavior, refund / chargeback flows.
- Statistically literate. A/B-testing, incrementality, correlation vs. causation — you handle these correctly when no one is checking.
- A curious investigator who walks in with the answer and the recommendation, not the dashboard — and pushes back on the question if it's wrong.
- Comfortable in a no-process environment: forms the request themselves, navigates ambiguity without hand-holding.
- Treats AI tools as a daily multiplier — Claude / Cursor / GPT already built into how the work gets done.
WHAT YOU'LL BE DOING
ANALYSIS THAT MOVES DECISIONS (~60% OF THE ROLE):
- Own A/B test design and analysis end to end — from sample-size planning to readout to recommendation. Make sure the company doesn't ship false positives.
- Monitor and improve LTV-prediction accuracy; explain the gaps between predicted and realized LTV by cohort, channel, geo, product.
- Find funnel bottlenecks and growth opportunities across acquisition, activation, retention and monetization — and bring back specific, prioritized actions.
- Build creative reporting the performance-marketing team actually uses to decide what to scale and what to kill.
- Generate proactive growth and unit-economics ideas grounded in data — not waitlists of requests.
THE DATA LAYER THAT MAKES THE ANALYSIS TRUSTWORTHY (~40% OF THE ROLE):
- Integrate new sources end-to-end — next on the list: Adyen (disputes, commissions, partial refunds) and a clean geo dimension.
- Own the dbt project for your domains: well-modeled, well-documented, well-tested assets the business can self-serve from. Keep tests green, fix existing warnings, retire what's no longer earning its keep.
- Keep Dagster pipelines reliable, cheap, and fresh — SLAs and anomaly detection, not just "did it finish."
- Govern Metabase as a product: access, ownership, naming, self-serve UX, the dashboards people actually open.
- Embed AI tooling (Claude Code, Cursor) into the analytical workflow to compound output, not just tick a box.
MUST HAVE
- Analyst-grade SQL: You can answer almost any business question that fits in a warehouse — by yourself — without hand-holding.
- Statistical foundations you can defend: A/B testing (including sample size, power, and reading negative results), incrementality, correlation vs. causation, cohort thinking. Light ML where it earns its place.
- Hands-on marketing & payments analytics experience: You have personally moved CAC, LTV, retention, or unit-economics with a specific analysis you can walk through.
- dbt + a modern warehouse: (Snowflake, BigQuery, Redshift, or Databricks), you write the models you need yourself; you don't wait for a data engineer.
- Python for analysis and pipelines — pandas, notebooks, light scripting in an orchestrator (Dagster / Airflow / Prefect / similar).
- Russian language for day-to-day work with the team.
NICE TO HAVE
- Experience in a solo or duo data team — you've navigated the chaos yourself.
- Direct work with Facebook Ads API, Google Ads, MMP / attribution platforms; you know how ad hierarchies and attribution windows really behave.
- Forecasting, financial modeling, or unit economics — especially LTV forecasting and cohort modeling.
- BI ownership of Metabase / Looker / Mode as a product (UX, security, access, naming).
- Production use of AI tools (Claude, Cursor, GPT) built into your routine, not just experimented with.
WHAT WE OFFER
- Mission: Help users live longer, healthier lives through innovative products.
- Impact: Directly influence company growth with minimal bureaucracy.
- Compensation: Competitive salary and comprehensive benefits package.
- Work-life balance: Flexible working hours.
- Professional development: Tuition reimbursement.
- Remote work: Fully remote, preference ±2h CET.
- Benefits: Health insurance, gym membership reimbursement, home office support.
