Senior Data Analyst

October 30, 2026

Hybrid

Senior

Warsaw, Poland

Also worth knowing

English is required
B1 level

Description

A live-streaming platform with 450+ million registered users is hiring a Senior Data Analyst to own a product analytics domain such as 1:1 chat, content packs or live streaming. The role covers end-to-end A/B testing, production SQL/YAML datasets and event instrumentation specs across Android, iOS and Web. It is an on-site position in Warsaw with four office days and one work-from-home day per week.

Responsibilities

Own a business domain — the numbers in it, the events behind them, the pipelines that build them and the experiments that change them
Be the analytics counterpart for a product domain that PMs and engineers come to for any number in that area
Run A/B tests on product features end to end: hypotheses, primary and secondary metrics, audience, monitoring and a clear ship / don't-ship call
Build and maintain the datasets behind your domain as production transformations in SQL and YAML pipeline config with freshness, uniqueness and validity checks
Specify instrumentation before it's built — write the BI event spec for new features across Android, iOS and Web
Verify after release that events actually fire the way the spec said, and chase the gaps when they don't

Requirements

Advanced SQL, including window functions and multi-source joins, with the ability to reason about what a query costs
Strong experimentation background: hypothesis testing, confidence intervals, power and minimum detectable effect
Understanding of experimentation failure modes such as peeking, multiple comparisons and hunting for a favorable segment
3+ years as a product or data analyst on consumer products, with real exposure to both mobile and web
Critical use of AI analytics tooling — an internal Claude-based layer over BigQuery — with checking what it produces
Comfort shipping code: git, merge requests, reading and reviewing someone else's SQL
Working knowledge of product event tracking and why the same event behaves differently across platforms
English at B1 or above for writing specifications and presenting findings to product stakeholders
BigQuery specifically — partitioned tables, clustering and slot-based pricing (strong advantage)
Airflow, dbt or another orchestrated transformation framework (strong advantage)
Having been the analyst on a production incident, not only in the post-mortem (strong advantage)
Python with the usual statistical libraries — pandas, scipy, statsmodels (nice to have)
A BI tool such as Looker, Tableau or Power BI — having built the models and dashboards (nice to have)
Data quality tooling such as Soda or Great Expectations (nice to have)
Experience in social, live-streaming, gaming or another engagement-driven consumer product (nice to have)