Engineering Manager, GrowthNew$220K–$450K

The opportunity

Sentry is looking for an Engineering Manager to lead our Growth team (internally called "Value Discovery") and help expand our product-led business by helping customers discover and get the most out of our platform.

What you'll do

  • Lead work across Sentry's self-serve funnel: signup and onboarding, first-run activation moments in-product, trial and upgrade paths, and pricing and billing surfaces in partnership with the Billing team — largely in our Python/Django and TypeScript/React codebase

  • Evolve our experimentation foundations, in close partnership with our Data: team — flagging, instrumentation, and readouts are real but far from finished

  • Run a fast, disciplined experimentation loop where hypotheses are argued before they're built

  • Hold conversion and activation alongside churn, support volume, and 90-day: retention, and report all of them — including the experiments that come back flat

  • Grow the engineers on your team, including the underrated skill of doing: excellent work on something that might be deleted next week

  • Believe Growth done well is a form of customer service, not a conversion engine

What they're looking for

  • + years managing engineers, including time owning a metric-driven team, on: top of a strong engineering background
  • Technically credible with senior engineers: our EMs engage substantively in architecture and technical decisions, and every EM passes a technical assessment at a senior IC bar
  • Strong product sense: you identify where a change will create the most value, not just where a metric can be moved
  • Experience helping users get the most out of a complex product
  • Working fluency with experimentation frameworks, feature flags, A/B testing, and analytics instrumentation
  • Able to talk to users credibly about their workflows, tooling, and pain: points, with genuine opinions about the developer tools you've used
  • Bonus: you've worked on growth at a developer tools company, built: experimentation infrastructure from a low starting point, or run experiments at volumes where significance wasn't guaranteed