Head of Data Analytics

The Opportunity BetterSleep is one of the most downloaded sleep apps in the world, with 85M+ downloads and a global audience of people actively trying to sleep better. We're at an inflection point, moving from a legacy growth model to one built around unit economics, demand quality, and sustainable acquisition. The opportunity: build the growth engine for a brand that millions already use, with startup-level ownership and the resources of a public company. Our team sits at the intersection of consumer product, performance marketing, and data science — and we're building the data foundation to make all three faster and smarter. We run sophisticated UA programs across Facebook, Apple Search Ads, TikTok, and CTV; we run a continuous experimentation program across subscription pricing, onboarding, and content; and we have a growing Firebase + BigQuery data platform that powers it all. We're a lean, high-conviction team and we move fast. The Role We're looking for a Head of Data to own and evolve the entire data function at BetterSleep — platform, people, and strategy. You'll manage a team of data scientists, own the roadmap for our data infrastructure, and serve as the strategic data partner to product, growth, and executive leadership. This is a player-coach role: you'll spend real time in the work — reviewing dbt models, designing data products, unblocking your team — while also setting direction and partnering with the business on the decisions that matter. The data team at BetterSleep was historically weighted toward research and data science. We've been rebuilding it with a platform-first mindset: stable pipelines, reliable definitions, self-service tooling, and governance that makes the whole organization faster. You'll inherit that momentum and lead the next phase of that build. What You’ll Do Data Platform & Engineering 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 Analytics & Measurement 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 Product Analytics 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 People & Partnerships 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 Data Governance & AI-Forward Operations 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