The Amplitude Growth Analyst

“What was the moment this product clicked?” — share yours

A data analyst, growth analyst, or analytics engineer at a Series B–D company who owns Amplitude as the source of truth for product behavior. They are technical enough to write SQL but prefer not to for exploratory analysis. They've mastered the Amplitude chart types. They build dashboards that PMs and executives use but don't fully understand. They're the person in the room who says "let's look at the data" and then actually pulls it up.

• →Answer product behavior questions fast enough to be useful in the meeting, not after it
• →Build dashboards that stay accurate without requiring manual maintenance
• →Connect behavioral data to business outcomes in a way leadership can act on

• —Event schemas that were designed by engineers without analyst input and now
• —require workarounds to answer basic questions
• —Chart configurations that break when the underlying event structure changes
• —The constant negotiation between "what Amplitude can answer" and "what the PM is asking"
• —Cohorts that take minutes to compute when the answer is needed now

• A product decision made without data is a guess — sometimes that's right,
• but you should know which one you're making
• The best analytics infrastructure is invisible to the people who benefit from it
• Every unanswered product question is a missed instrumentation decision from six months ago

Uses Amplitude Growth or Enterprise. Builds charts across Segmentation, Funnels, Retention, Pathfinder, and Experiment. Creates and maintains a shared chart library that the product team uses as a self-service resource. Manages Amplitude taxonomy governance alongside the data engineering team. Has a Slack channel where Amplitude chart links replace dashboard screenshots. Reviews Amplitude Experiment results and presents findings in product reviews. Has had to explain statistical significance to a PM at least five times. Has a slide deck for it now.

• →Cohort computation that's fast enough for exploratory analysis restores
• →cohort-based investigation as a first-pass rather than a scheduled query
• →Taxonomy management tools that surface event schema drift before it corrupts
• →charts reduce the "this chart is broken because the event changed" discovery
• →Chart templates for common product questions (activation, retention, engagement)
• →reduce the time-to-insight for teams without dedicated analysts
• →Experiment analysis that accounts for network effects and novelty bias
• →improves decision quality on A/B test results

Pairs with `mixpanel-primary-user` for the analyst-configured vs. PM self-service analytics tool comparison. Contrast with `posthog-primary-user` for the engineering-embedded vs. analyst-owned analytics philosophy. Use with `data-engineer` for event schema design and instrumentation planning upstream of analysis.