Middle Data Analyst

Analyze player behavior across the full funnel: registration, first deposit, retention, reactivation, churn - and translate findings into actionable product recommendations;

Design, run, interpret A/B tests and quasi-experiments, handle the methodology for measuring feature impact;

Build and maintain cohort-based analyses (LTV, retention curves, payback) to support product and investment decisions;

Define, validate, and monitor product KPIs and metric trees, challenge metrics that don't reflect real business value;

Partner with product managers to prioritize the roadmap based on expected impact, not intuition;

Develop self-service dashboards (Tableau) and data sources to help stakeholders answer routine questions without analyst involvement;

Investigate anomalies in key metrics and communicate root causes clearly to non-technical audiences;

Maintain culture of rigorous, honest analytics: document assumptions, quantify uncertainty, and flag when data can't answer the question;

Own web analytics implementation and data quality: tracking plans, event taxonomy, tag management (GTM), and validation of new tracking releases.

2+ years in Product/Web/ or Data analytics;

Hands-on experience with web analytics platforms (GA4, Amplitude, Mixpanel, or similar), including event design and tracking implementation, not just report consumption;

Working knowledge of Google Tag Manager or a comparable tag management system and ability to read dataLayer specs and debug tracking issues;

Strong SQL - window functions, CTEs, large datasets (Athena or similar is a plus);

Solid grounding in statistics: hypothesis testing, confidence intervals, statistical power, common experiment pitfalls (peeking, selection bias, cohort-maturity bias);

Experience with funnel analysis, cohort analysis, LTV and retention modeling;

Proficiency with a BI tool (Tableau preferred), including data source design;

Understanding of attribution models and their limitations;

Python or R for ad-hoc analysis is a plus (pandas, curve fitting, survival analysis);

English at least at Intermediate level (written and spoken);

Experience with iGaming, or another high-frequency B2C domain (fintech, gaming, e-commerce) will be a plus.

Business-first mindset: you start from the decision that needs to be made, not from the data that happens to be available;

End-to-end thinking: you naturally connect a landing page tweak to its downstream effect on deposits and LTV, rather than optimizing each stage in isolation;

Intellectual honesty: you say "the data doesn't support this" even when the room wants a different answer, and you quantify how confident you actually are;

Attention to data quality: you treat broken tracking as a first-priority incident, because every analysis downstream depends on it;

Clarity over sophistication: you prefer a simple, explainable metric that stakeholders trust to a theoretically elegant one nobody understands;

Ownership: you follow a question from raw event to business decision without waiting for a perfectly specified task.

GROWE TOGETHER: Our team is our main asset. We work together and support each other to achieve our common goals;

DRIVE RESULT OVER PROCESS: We set ambitious, clear, measurable goals in line with our strategy and driving Growe to success;

BE READY FOR CHANGE: We see challenges as opportunities to grow and evolve. We adapt today to win tomorrow.