Product Data Analyst

Who we are:

Evoplay is one of the leading iGaming providers, recognized for its innovative approach to game development and technological solutions. The key to this success is the teamwork of like-minded people who are passionate about a common goal and love games.

Our mission is to surprise not only the iGaming market but the entire entertainment industry with our interesting and unique products. Our game portfolio of 150+ titles, where each is a unique and exciting story, is presented all over the world, including Europe, Latin America, Asia, and the CIS.

The expertise we’re looking for:

• 3+ years in product or data analytics.

• Strong SQL: complex joins, window functions, query performance on large datasets (ClickHouse experience is a plus).

• Hands-on experience with at least one BI tool (QuickSight, Power BI, Tableau or similar).

• Solid understanding of statistics for experiments: significance, power, common pitfalls such as peeking and multiple comparisons.

• Python or R for analysis beyond what SQL covers.

• Ability to explain findings to non-analysts in plain language

• English (B2 or higher).

Would be a plus:

• iGaming experience and familiarity with core metrics: GGR (gross gaming revenue, total bets minus total wins), RTP (return to player, the share of bets paid back as wins), bet volume, retention.

• Experience with data quality checks or building data pipelines.

Your responsibilities will include:

• Own analytics for a set of games or a product area: KPIs, regular reporting, deep dives into player behaviour.

• Design and analyse A/B tests for game mechanics and features, from hypothesis and sample size to the final read-out.

• Build and maintain dashboards in QuickSight / Power BI on top of data in ClickHouse.

• Work with game designers, product managers and data engineers to define metrics and make sure tracking is correct.

• Take part in improving our BI setup, including data models and reporting migration.

What we expect:

We are looking for someone who treats an analytical area as their own, not as a queue of tickets. In practice that means:

• You take a question from a stakeholder and work out what decision it is meant to support, before you write any SQL.

• You notice when a number looks wrong (in your report or someone else’s) and chase it to the root cause instead of passing it on.

• You keep your dashboards and data pipelines alive after release: you know who uses them, check that they still show correct data, and retire what nobody needs.