• Own the architecture and reliability of the UA data pipeline — BigQuery, dbt, Jenkins, AppsFlyer/SKAN attribution, and our downstream dashboards and Mixpanel warehouse sync • Drive the migration of remaining Python transformation scripts into dbt; establish CI/CD, testing standards, and dev/prod environment hygiene • Partner with engineering on the ingestion layer (ad platform APIs, Firebase, custom subscription backend webhooks) and upstream data quality • Evaluate and introduce new tooling when it's clearly the right call; default to keeping the stack simple • Own the definitions, logic, and reliability of our core metrics: installs, trials, trial-to-paid, RPP, ROAS, CAC, LTV, churn • Lead our attribution methodology — MMP (AppsFlyer), SKAN 4, SSOT deduplication — and translate it clearly to the UA and executive teams • Support our experimentation program: help PMs and the UA team design tests, validate results, and build statistical muscle across the org • Experience building or managing experimentation infrastructure (A/B testing platforms, statistical significance frameworks) • Build self-service data products that reduce the number of ad hoc requests hitting the team — including AI-powered tooling (e.g. natural language querying over Mixpanel/BigQuery, automated anomaly detection, LLM-assisted reporting) where it delivers real leverage • Own product analytics instrumentation strategy in partnership with engineering — event taxonomy, Mixpanel governance, Firebase event schema • Translate product analytics into actionable insight for PMs: retention curves, funnel analysis, feature adoption, onboarding optimization • Ensure the product team can answer their own questions without always needing the data team in the loop • Manage and develop two data scientists; make hiring decisions around expansion of the team as needed • Partner closely with the UA Manager, Growth PM, and Head of Product as the data team's primary business-facing contact • Set sprint cadence, manage the data backlog, and keep the team focused on high-leverage work • Drive data governance and data semantic layer development; make our visualization platform more usable for non-technical stakeholders • Help the organization identify and automate high-friction internal workflows using AI — this is a company-wide priority with executive sponsorship • Champion a culture of practical AI adoption on the data team and beyond