Data & Analytics Lead
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Job Description
Data & Analytics Lead building Tapouts’ analytical foundation across growth, product, payments, and coaching operations. Defining metrics, modeling customer journeys, and guiding retention and quality decisions.
Responsibilities:
- Establish clear, shared definitions for core metrics: acquisition, activation, retention, churn, CAC, and lifetime value
- Build a unified view of the customer journey across marketing, payments, product activity, coaching sessions, and cancellations
- Develop retention and churn analyses that identify when and why customers leave
- Create a framework for evaluating coach quality, incorporating outcomes, engagement, and satisfaction
- Build and maintain clean, documented analytical data models and decision-oriented dashboards
- Identify gaps in tracking and data quality, partnering with engineering and business teams to resolve them
- Partner with growth, product, operations, and coaching leadership to turn business questions into measurable analyses
- Understand and document tapouts major data sources and customer journey within the first three months
- Align the organization on definitions for its most important business metrics
- Deliver a reliable initial view of acquisition, retention, churn, and coaching activity
- Produce at least one analysis that changes or informs an important business decision
- Establish a trusted analytical data foundation used across the company within six to twelve months
- Give leadership reliable visibility into CAC, retention, lifetime value, and unit economics
- Build a fair and actionable framework for measuring and improving coaching quality
Requirements:
- Strong experience in product analytics, business analytics, analytics engineering, or a similarly hands-on data role
- Advanced SQL skills and experience working with imperfect data from multiple systems
- Strong business judgment — the ability to translate ambiguous questions into measurable analyses
- Experience with customer funnels, cohort analysis, retention, churn, segmentation, CAC, and lifetime value
- Experience building analytical data models in a modern data warehouse
- Comfort operating independently in an early-stage environment with limited existing data infrastructure
- Experience with subscription, marketplace, education, coaching, or consumer services businesses
- Familiarity with Python, dbt (or comparable transformation tools), and modern BI platforms
- Experience as an early or first data hire
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