Head of Data Analytics

BetterSleep

1d•$200,000 - $250,000•Remote

About The Position

BetterSleep is a leading sleep app with a global audience, seeking to build a sustainable growth engine. The data team operates at the intersection of consumer product, performance marketing, and data science, aiming to enhance these areas through data. The team is lean, fast-paced, and focused on building a robust data foundation. The Head of Data Analytics will own and evolve the entire data function, including platform, people, and strategy. This role involves managing a team of data scientists, overseeing the data infrastructure roadmap, and acting as a strategic data partner to leadership. It's a player-coach position, requiring hands-on involvement in reviewing data models and designing data products, alongside strategic direction and business partnership. The data team is transitioning to a platform-first approach with stable pipelines, reliable definitions, self-service tools, and governance to improve organizational efficiency. The Head of Data Analytics will lead this next phase of development.

Requirements

7+ years in data/analytics roles, with at least 2 years managing a data team.
Hands-on analytics engineering experience: proficient with dbt, comfortable reviewing SQL models and data pipelines, and able to unblock engineers.
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 cross-platform reporting.
Comfort with BigQuery (or another cloud data warehouse) as the analytical backbone.
Strong instincts for data modeling: understanding of facts and dimensions, grain, and ability to explain data duplication issues.
Product analytics experience: owned event instrumentation, built Mixpanel or Amplitude governance frameworks, and partnered with product teams to translate events into insights.
Business partnering skills: ability to translate technical constraints into plain language for stakeholders and push back on incorrect questions.
Product mindset: viewing data as a product for internal users.
Practical experience building with AI/LLM tools: designing and shipping AI-assisted workflows, internal analytics agents, or data apps that reduce manual work.

Nice To Haves

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

Responsibilities

Own the architecture and reliability of the UA data pipeline (BigQuery, dbt, Jenkins, AppsFlyer/SKAN attribution, Mixpanel warehouse sync).
Drive the migration of Python transformation scripts into dbt, establishing CI/CD, testing standards, and 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 while maintaining a simple tech stack.
Own the definitions, logic, and reliability of core metrics (installs, trials, trial-to-paid, RPP, ROAS, CAC, LTV, churn).
Lead the attribution methodology (MMP, SKAN 4, SSOT deduplication) and communicate it to UA and executive teams.
Support the experimentation program by assisting with test design, result validation, and building statistical capabilities.
Build self-service data products, including AI-powered tooling (natural language querying, anomaly detection, LLM-assisted reporting), to reduce ad hoc requests.
Own product analytics instrumentation strategy in partnership with engineering (event taxonomy, Mixpanel governance, Firebase event schema).
Translate product analytics into actionable insights for PMs (retention curves, funnel analysis, feature adoption, onboarding optimization).
Ensure the product team can independently answer their own questions.
Manage and develop two data scientists, making hiring decisions for team expansion.
Partner closely with the UA Manager, Growth PM, and Head of Product as the primary business-facing contact for the data team.
Set sprint cadence, manage the data backlog, and focus the team on high-leverage work.
Drive data governance and data semantic layer development to improve the usability of the visualization platform for non-technical stakeholders.
Help the organization identify and automate high-friction internal workflows using AI.
Champion a culture of practical AI adoption within the data team and across the organization.