Lead Data Analyst

Job description

Lead Data Analyst: Own the business problem, not the ticket. Our games are played by millions of people daily. Every session leaves a trail: a level attempted eleven times, a bundle ignored, a player who quietly stopped opening the app on a Tuesday. Billions of these events land in our warehouse each month — and some of them are the difference between a game that grows and one that doesn't. We're looking for a Lead Data Analyst who wants to be handed a problem rather than a query request. You'll decide what's worth investigating, build the models and pipelines to investigate it, and defend the decision in front of leadership. What You'll Do Go deep, not wide. Run in-depth analyses that end in a decision: funnel drop-offs, cohort behaviour, segment deep-dives, feature post-mortems. Own the "why," not just the dashboard. Own the churn numbers. Know retention and churn cold — by cohort, level, segment, 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 behaviourally, 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 optimise 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. What We're Looking Fo r 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 optimisation, 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, summarising qualitative data, exploratory scaffolding — and you don't where they don't: numbers you'll present, causal claims, 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, quantitative modelling you can explain to a skeptic. Ownership: projects driven end-to-end, and people made better along the way. Education: Bachelor's or Master's in CS, Engineering, Statistics, Mathematics, or similar. Visualisation: Looker, Tableau, Metabase or Power BI. Bonus Points: Mobile gaming analytics — retention curves, in-game economy, monetisation, LiveOps. Experience standing up an experimentation platform or feature store. Anything you've shipped that changed a game design decision. If you've ever been frustrated that your best analysis ended in a slide instead of a shipped change — this is the other kind of job.