Middle Data Analyst for Sport Team

Inactive
G
GR8 TechIGaming
Location: AnywhereFull-TimeMiddle
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Salary not disclosed
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Job Details

Languages
English: Intermediate+ (written and spoken)
Experience
2+ years of experience as a Data Analyst / Product Analyst
Required Skills
PythonSQLGitTableauMicrosoft ExcelA/B testingGoogle Sheetsdbt

Requirements

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

Responsibilities

  • Drive data-informed decision-making within the Sportsbook product by uncovering growth and monetization opportunities through deep product analytics and experimentation.
  • Conduct deep-dive analysis of user journeys.
  • Identify growth and monetization opportunities.
  • Build experiment plans and evaluation logic.
  • Conduct A/B test and feature impact evaluation.
  • Perform pre/post analysis and measure uplift.
  • 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).
  • Turn analysis into actionable recommendations, including clear insights, next steps, and expected impact.
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