Data Analyst

This is a fully remote position, open to applicants anywhere in the world.

📋 Description

• Examine player behavior throughout the entire funnel: registration, initial deposit, retention, reactivation, and churn, transforming insights into actionable product suggestions.

• Design and execute A/B tests and quasi-experiments, managing the methodology to assess feature impact.

• Create and maintain cohort-based analyses (LTV, retention curves, payback) to inform product and investment decisions.

• Establish, validate, and track product KPIs and metric trees, questioning metrics that fail to represent genuine business value.

• Collaborate with product managers to prioritize the roadmap based on anticipated impact rather than intuition.

• Develop self-service dashboards (Tableau) and data sources to enable stakeholders to address routine inquiries independently of analyst support.

• Investigate discrepancies in key metrics and relay root causes clearly to non-technical stakeholders.

• Uphold a culture of thorough, honest analytics: document assumptions, quantify uncertainty, and highlight instances when data cannot resolve the question.

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

⛳️ Requirements

• Minimum of 2 years of experience in Product, Web, or Data analytics.

• Strong proficiency in SQL, including window functions, CTEs, and handling large datasets (experience with Athena or a similar tool is advantageous).

• Practical experience with web analytics platforms (GA4, Amplitude, Mixpanel, or similar), encompassing event design and tracking implementation, rather than merely report consumption.

• Familiarity with Google Tag Manager or an equivalent tag management system, along with the ability to read dataLayer specifications and troubleshoot tracking issues.

• A solid foundation in statistics, including hypothesis testing, confidence intervals, statistical power, and awareness of common experimental pitfalls (e.g., peeking, selection bias, cohort-maturity bias).

• Proficient in funnel analysis, cohort analysis, LTV modeling, and retention modeling.

• Skilled in using a BI tool (preferably Tableau), including data source design.

• Knowledge of attribution models and their limitations.

• Familiarity with Python or R for ad-hoc analysis is an advantage (experience with pandas, curve fitting, survival analysis).

• Proficiency in English, at least at an Intermediate level (both written and spoken).

• Experience in iGaming or another high-frequency B2C sector (such as fintech, gaming, or e-commerce) is a plus.

🏝️ Benefits

• Focus on Health & Wellness.

• Comprehensive Global Medical Coverage.

• Opportunities for Professional Growth.

• Benefits Programs (including compensation for gym, dental, psychological services, etc.).

• Rewards Based on Performance.

• A Dynamic Work Environment.