Middle Data Analyst for Sport Team

Why this role exists:

This role exists to drive data-informed decision-making within the Sportsbook product by uncovering growth and monetization opportunities through deep product analytics and experimentation. It ensures that product initiatives are measurable, insights are actionable, and key metrics remain consistent and reliable across the organization.

What you’ll drive:

Product Analytics & Experimentation

• Conduct deep-dive analysis of user journeys.
• Identify growth and monetization opportunities.
• Refine product hypotheses.
• Define measurable goals and success metrics.
• Build experiment plans and evaluation logic.
• Conduct A/B test and feature impact evaluation.
• Perform pre/post analysis.
• Measure uplift.
• Perform segmentation.
• Interpret results and trade-offs.

BI, KPI & Insights

• Design, build, and maintain Tableau dashboards.
• Develop and maintain self-service reporting for product and business stakeholders.
• Define and monitor core KPIs (conversion funnels, retention cohorts, ARPU/LTV proxies, sportsbook activity and engagement metrics).
• Ensure metric consistency (“single source of truth”).
• Turn analysis into actionable recommendations, including clear insights, next steps, and expected impact.

What makes you a GR8 fit:

Must-have

• 2+ years of experience as a Data Analyst / Product Analyst in a product environment.
• Strong SQL skills (data extraction, joins, window functions, cohorting, funnel analysis).
• Solid understanding of product metrics and frameworks: funnels & conversion, retention, engagement, churn, LTV, unit economics, cohort analysis.
• Experience partnering with Product Managers: translating hypotheses into measurable metrics, defining success criteria, building analytical approaches.
• Hands-on experience with BI tools (preferably Tableau) and best practices in dashboard design.
• Confident Excel / Google Sheets skills (analysis, pivot tables, basic modeling).
• Basic knowledge of probability and statistics (confidence intervals, significance, distributions).
• English: Intermediate+ (written and spoken).
• Practical experience with A/B testing: experiment design, sample size intuition, guardrail metrics, interpretation, post-analysis.

Nice-to-have

• Python for analysis (pandas, numpy, matplotlib / plotly) and automation of repetitive analisys.
• Experience with Git and versioning analytical artifacts (SQL, dbt, dashboards documentation).