Product Manager, Growth & Analytics, Beeper

Automattic, the fully distributed company behind WordPress.com, WooCommerce, Tumblr, and Beeper, is looking for a Product Manager, Growth & Analytics to join the Beeper team. You will own product execution for growth initiatives, working directly with Beeper leadership to figure out what makes users stick, what blocks them, and what makes them pay, and then do something about it. You are not the kind of PM who waits for someone else to pull the data: you write your own SQL, build your own dashboards, and use what you find to make better decisions. Overlap with North American time zones (Eastern preferred) is important for working closely with the leadership team.

Responsibilities

• Own the product process for growth: specs, user stories, success criteria, and experiment tracking, kept tight so engineering is not guessing at scope and leadership is not guessing at outcomes.
• Run experiments end-to-end: hypothesis, spec, instrumentation, launch, results, closing the loop every time.
• Build and maintain the dashboards leadership relies on: activation, retention, conversion, and engagement numbers the team checks every week.
• Investigate when metrics move: when activation drops or retention shifts, you figure out why and what to do about it.
• Work with engineering and support to validate problems and confirm whether what shipped actually worked.
• Keep product documentation current: feature specs, release notes, and internal reference docs that keep a distributed team aligned.

Qualifications

• Four to six years in product management, growth, or a closely related role where you owned product execution.
• Strong SQL skills (CTEs, window functions, cohort analysis, funnel queries). This is a hard requirement, not a nice-to-have.
• Experience with a cloud data warehouse, building dashboards and recurring queries.
• Ability to write a spec that engineering builds against and a data summary that leadership makes decisions with.
• Experience running experiments, knowing the difference between shipping a feature and learning something.
• Helpful: experiment design and A/B testing frameworks, messaging or consumer products, subscription or freemium models, and enough statistics to know when a result is meaningful.