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AI Summary
TL;DR
Co-own Formula's entire analytics function alongside our Head of BI — half builder, half investigator, in one seat. This is not a data engineer and not a product analyst; it's the rare full-stack data person who makes both possible without a team in between, on a modern stack (Snowflake, dbt, Python, Dagster, Metabase) with AI woven into daily work.
What We're Looking For
Someone who has been the strongest data person on a small team and is ready to do it again — with a co-owner mandate, not an executor seat.
A pragmatic builder rather than a craft-obsessed engineer: ships the model that earns its keep, not the architecture diagram.
A curious investigator who walks into the room with the answer, not the dashboard — and can push 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, not a novelty — already builds Claude / Cursor / GPT into how the work gets done.
What You'll Be Doing
Own and evolve the dbt project — design the data layer, write production models, optimize heavy queries, keep the warehouse honest.
Build and run pipelines in Dagster across product, marketing (Facebook Ads, Google Ads, attribution), and finance sources.
Run end-to-end analyses that change decisions in growth, product, marketing, and finance — from the question through the SQL to the recommendation.
Co-design the analytics roadmap with the Head of BI: what we measure, what we automate, what we retire.
Embed AI tooling into the analytical workflow to compound the team's output, not just tick a box.
Must Have
Hands-on hybrid experience: personally written dbt models and personally run analyses that moved business decisions.
Strong SQL and dbt in a modern warehouse (Snowflake, BigQuery, Redshift, or Databricks); able to design a data layer from scratch.
Python at the level of pipelines and analytical notebooks — pandas, applied statistics, light ML where it earns its place.
Statistical literacy you can defend — A/B testing, incrementality, correlation vs. causation handled correctly.
Russian language for day-to-day work with the team.
Nice to Have
Experience in a solo or duo data team — no process, no committees, navigated the chaos yourself.
Deep understanding of how paid acquisition works (Facebook Ads API, Google Ads, attribution modeling) where you actually moved CAC or LTV.
Forecasting, financial modeling, or unit economics — especially LTV forecasting and cohort modeling.
Production use of AI tools (Claude, Cursor, GPT) built into your routine, not just experimented with.
What We Offer
Inspiring Mission: Help users live longer, healthier lives through innovative products.
Impact: Directly influence company growth with minimal bureaucracy.
Attractive Compensation: Competitive salary and comprehensive benefits package.
Work-Life Balance: Flexible working hours.
Professional Development: Tuition reimbursement.
Remote Work: Fully remote, with a preference for candidates within ±2 hours of CET.
Benefits: Health insurance, gym membership reimbursement, home office support.
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