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

Requirements

7+ years in data/analytics roles, with at least 2 years managing a data team
Hands-on analytics engineering experience: you know dbt well, you're comfortable reviewing SQL models and data pipelines, and you can unblock your engineers when things break
Deep understanding of subscription and mobile app metrics: trial conversion, trial-to-paid modeling, LTV, renewal rates, cohort analysis
Experience with mobile UA attribution — MMP (AppsFlyer, Adjust, or similar), SKAN, multi-touch — and the real-world messiness of cross-platform reporting
Comfort with BigQuery (or another cloud data warehouse) as the analytical backbone
Strong instincts for data modeling: you think in facts and dimensions, you care about grain, and you can explain why a join is producing duplicate rows
Product analytics experience: you've owned event instrumentation, built Mixpanel or Amplitude governance frameworks, and partnered with product teams to translate raw events into insight
Business partnering skills: you can translate technical constraints into plain language for product and marketing stakeholders, and you push back when the question is the wrong one
Product mindset: you think about data as a product that serves internal users, not just a function that answers requests
Practical experience building with AI/LLM tools — not just using Copilot or ChatGPT for productivity, but actually designing and shipping AI-assisted workflows, internal analytics agents, or data apps that reduce manual work. You know what's possible with today's models and you're already finding ways to apply them

Benefits

Competitive salary & compensation
Excellent health, dental, and vision coverage
Retirement plan with employer matching
Commuter & lunch benefits (UberEats)
Free access to telehealth & BetterHelp services
Any hardware/software you need to succeed
A product loved by millions — and admired by the press
Awesome people to work with
The chance to help people live a better life, every day

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

dbtSQLBigQueryA/B testingdata modelingevent instrumentationdata governanceAI toolsstatistical significance frameworksdata pipeline management

Soft Skills

business partneringteam managementcommunicationproblem-solvingcollaborationleadershipanalytical thinkingproduct mindsetadaptabilitystrategic thinking