Product Analyst

Що робити

Ownership
Be the analytics counterpart for a product domain1:1 chat, content packs, negotiations, live streaming — you become the person PMs and engineers come to for any number in that area, and the person accountable when it's wrong.
Experiments
Pipelines
Build and maintain the datasets behind your domain Write production transformations as SQL and YAML pipeline config, with freshness, uniqueness and validity checks. Your work goes through code review like any engineer's.

Advanced SQL, including window functions and multi-source joins — and the ability to reason about what a query costs, not only whether it returns.
A strong experimentation background. Hypothesis testing, confidence intervals, power and minimum detectable effect — and the failure modes: peeking at a running test, multiple comparisons, hunting for a segment where the result works. You should also be the kind of person who checks that the experiment itself is sound before trusting its numbers.
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. We use an internal Claude-based layer over BigQuery heavily. We want someone who checks what it produces before passing it on.
Comfort shipping code: git, merge requests, reading and reviewing someone else's SQL. You'll write production pipeline code, not only notebooks and queries.

Python with the usual statistical libraries — pandas, scipy, statsmodels.
A BI tool such as Looker, Tableau or Power BI — having built the models and dashboards, not only read them. We use Looker today but are moving off it, so the particular tool matters much less than having done the work in one.
Data quality tooling such as Soda or Great Expectations.
Experience in social, live-streaming, gaming or another engagement-driven consumer product.

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