I’m looking for an experienced data analyst who can turn the large volumes of marketing spend, attribution and in-game behaviour data we already collect into clear, actionable insights for our user-acquisition team. The core mission is to improve campaign performance, tighten budget allocation and increase long-term player value across our portfolio of mobile titles. What you’ll be working with We have both marketing and player behavior data flowing from MMPs, ad networks and our internal event pipeline. You will have direct access to raw tables in BigQuery, as well as visualization layers in Looker and Tableau. If you prefer Python, R, SQL or dbt to wrangle data that’s perfectly fine—use whatever lets you move fastest while keeping the work reproducible. Key focus areas • Build and maintain self-serve dashboards that UA managers can rely on daily • Develop and iterate LTV, ROI and payback-period models that stay accurate as our games evolve • Design cohort, funnel and incrementality analyses to reveal where our spend is truly additive • Surface segment-level recommendations that help creative, bidding and geo strategies scale profitably • Package findings in concise presentations for Marketing, Product and Leadership Acceptance criteria 1. Dashboards must refresh automatically and load in under 10 seconds for the main queries. 2. All models delivered with documented SQL/Python notebooks and version control. 3. Recommendations should include projected impact ranges and required spend to reach them. 4. A knowledge-transfer session recorded for our internal wiki. There’s no hard deadline, so you can propose milestones that make sense—for example an initial data audit, then dashboard MVP, followed by modelling and refinement. Quality and clarity matter more to us than speed. If this sounds like the kind of challenge you thrive on, let’s talk about your approach and preferred toolset.