Lead Data Analyst

Responsibilities:

• Go deep, not wide. Run in-depth analyses that end in a decision: funnel drop-offs, cohort behavior, segment deep dives, and feature post-mortems. Own the "why, " not just the dashboard.
• Own the churn numbers. Know retention and churn cold by cohort, level, segment, and geo.
• Build churn prediction that fires early enough for an intervention to exist, then prove the intervention moved the metric.
• Predict player value. LTV and revenue forecasting the business can plan against. Robust to seasonality, cohort mix, and content cadence.
• Tune the economy. Sources and sinks, currency inflation, reward-event faucets, bundle pricing, and elasticity. Catch drift before players do.
• Understand paying and non-paying players. What separates them behaviorally, where the conversion moment actually sits, and what it means for pricing and content.
• Run experimentation properly. Design and analyze A/B tests: sizing and power up front, guardrail metrics, and the integrity to call a flat result flat.
• Own the data. Architect and optimize pipelines connecting game telemetry, the warehouse, and ML systems. Data quality, definitions, and cost are yours, not just the final chart.
• Make leadership fluent. Turn analysis into narrative for product and engineering leadership, and hold up under challenge.
• Build self-serve and mentor. Dashboards (Metabase/Looker/Tableau) that answer the recurring questions, and a team of analysts you're actively raising the bar for.

Requirements:

• 6-9 years in data analytics, ideally gaming, consumer tech, or mobile apps.
• Mastery of SQL, non-negotiable. This is the core of the job. Window functions, complex joins across billions of rows, query optimization, and the discipline to write SQL someone else can read six months later. Most of your answers will start here.
• Strong Python for analysis and automation, pandas/numpy, scripting, and pipeline work.
• Judgment about Gen AI. You use LLMs where they earn their place, boilerplate SQL, code review, drafting, summarizing qualitative data, and exploratory scaffolding, and you don't where they don't: numbers you'll present, causal claims, and anything you can't verify.
• You know the difference between a fast answer and a correct one, and you check.
• Big data and pipelines: Redshift, Athena, BigQuery, Hive, or Spark, plus orchestration
• Real statistical foundations: experiment design, causal inference basics, and quantitative modelling you can explain to a skeptic.
• Ownership: projects are driven end-to-end, and people are made better along the way.
• Education: Bachelor's or master's in CS, engineering, statistics, mathematics, or a similar field.
• Visualization: Looker, Tableau, Metabase, or Power BI.