Analytics Engineer

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.

• 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.

• 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.

• 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.

• 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.

Note: candidates who don't match "Must Have" criteria will not be considered.

• 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.

• 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.