Senior Growth Analyst - Calendars

readdle70; United States; Remote; USD 90,000 - 150,000; Full time

Job summary

readdle70 is seeking a Growth Analytics Lead to own the analytics foundation for a subscription-based calendar product. You will integrate product, marketing, and monetization analytics to uncover opportunities across the customer journey, improve measurement, and guide prioritization with evidence.

You will analyze large datasets, build dashboards, define event tracking, and design experiments to drive growth.

Qualifications

• At least four years of experience in growth, product, or marketing analytics.
• Strong SQL skills and experience with BigQuery or a comparable cloud data warehouse.
• Experience creating dashboards and visualizations with Tableau or Looker, plus analytics platforms such as Amplitude, Mixpanel, Heap, Hotjar, or PostHog.
• Understanding of funnels, cohorts, retention, attribution, experimentation, segmentation, churn, lifetime value, and subscription monetization.
• Ability to work with marketing attribution systems and, ideally, Python or R for analysis and automation.
• Strong communication and business judgment, with the ability to act as a strategic partner to product, marketing, and engineering stakeholders.

Responsibilities

• Analyze large datasets to identify trends, funnel drop-offs, retention opportunities, and revenue drivers.
• Build and maintain self-service dashboards and reports for product and marketing teams.
• Define, document, and quality-check event tracking for features, campaigns, and experiments.
• Measure acquisition, onboarding, usage, purchasing, cancellation, pricing, and migration journeys.
• Design and monitor A/B tests and launches with sound statistical methods and long-term impact tracking.
• Develop forecasts and scenario models for installs, conversion, retention, and revenue.

Skills

SQL

BigQuery

Dashboards

Amplitude

Mixpanel

Tableau

Looker

Funnel analysis

Retention analysis

Experimentation

Python

R

Communication

Tools

Tableau

Looker

Amplitude

Mixpanel

Heap

Hotjar

PostHog

BigQuery

Python

R

Job description

Role overview

Own the analytics foundation for the growth of a subscription-based calendar product. This role combines product, marketing, and monetization analysis to uncover opportunities across the customer journey, improve measurement, and guide prioritization with evidence.

Responsibilities

• Analyze large datasets to identify trends, anomalies, funnel drop-offs, retention opportunities, and revenue drivers.
• Build and maintain self-service dashboards and reports that establish trusted growth metrics for product and marketing teams.
• Define, document, and quality-check event tracking for features, campaigns, and experiments.
• Measure acquisition, onboarding, usage, purchasing, cancellation, pricing, and migration journeys.
• Design and monitor A/B tests and launches, validating results with sound statistical methods and long-term impact tracking.
• Develop forecasts and scenario models for installs, conversion, retention, and revenue, while proactively presenting hypotheses and recommendations.

Requirements

• At least four years of experience in growth, product, or marketing analytics, ideally in a consumer or subscription software environment.
• Strong SQL skills and experience with BigQuery or a comparable cloud data warehouse.
• Experience creating dashboards and complex visualizations with tools such as Tableau or Looker, plus product analytics platforms such as Amplitude, Mixpanel, Heap, Hotjar, or PostHog.
• Understanding of funnels, cohorts, retention, attribution, experimentation, segmentation, churn, lifetime value, and subscription monetization.
• Ability to work with marketing attribution systems and, ideally, Python or R for analysis and automation.
• Strong communication and business judgment, with the ability to act as a strategic partner to product, marketing, and engineering stakeholders.

Nice to have

Experience with causal inference, quasi-experiments, econometrics, incrementality testing, marketing mix modeling, machine learning, or time-series forecasting.