Head of Data

BetterSleep

10h•$200,000 - $250,000

About The Position

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. 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.

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

Nice To Haves

Background in consumer subscription apps (health, wellness, entertainment, productivity)
Built internal data apps using AI frameworks (Claude/OpenAI APIs, LangChain, agent tooling) — dashboards, Slack bots, automated anomaly alerts, natural language analytics interfaces, or similar
Deep experience with attribution frameworks and data modeling

Responsibilities

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