xbowcareers - Product Analytics Engineer

• Own product analytics end-to-end - From event taxonomy and instrumentation through to dashboards and insight delivery. You're responsible for the full pipeline, not just one layer of it • Build and extend instrumentation - We have frontend event tracking in place. You'll improve it, add server-side event capture, and ensure our event taxonomy is consistent, complete, and actually useful • Design dashboards PMs use daily - Build views that answer the questions product managers ask most: feature adoption, user engagement, conversion through key flows, retention signals. • Define what we measure and why - Create and maintain our event taxonomy and measurement framework. Decide what's worth tracking, what's noise, and how events map to the outcomes we care about • Surface insights proactively - Don't wait for someone to ask. Spot usage patterns, adoption trends, and drop-off points, and bring them to the product team with context • Enable self-service analysis - Build data models, saved queries, and documentation so PMs and other stakeholders can explore usage data without filing a request every time • Maintain data quality and trust - Instrument tests, monitor event pipelines, and fix issues before anyone notices the numbers are wrong. Analytics that people don't trust are worse than no analytics • WHAT YOU'VE DONE • Built product analytics systems from early stage - you've defined event taxonomies, instrumented applications, and created dashboards that product teams relied on for real decisions • Worked with event-based analytics platforms like Amplitude, Mixpanel, PostHog, or Heap - not just consumed dashboards, but configured the platform, defined events, and managed the data pipeline • Implemented both client-side and server-side event tracking - you understand the difference, the trade-offs, and when each is appropriate • Written code to ship instrumentation - whether that's TypeScript on the frontend, Python or Node.js on the backend, or SQL for data modelling. You're not asking engineers to implement your specs; you're committing code yourself • Designed metrics and dashboards that changed how a product team operated - you can point to a specific decision that was made differently because of something you built • Worked in a startup or high-growth environment where you had to prioritize ruthlessly - you know the difference between "interesting to know" and "need to know to ship" • WHAT SETS YOU APART • You think in user journeys, not isolated events. You don't just count clicks - you understand what sequence of actions leads to a successful outcome and instrument accordingly • You have strong opinions about data quality. You'd rather have 10 well-defined, trustworthy events than 200 noisy ones • You can work across the stack. You're comfortable reading frontend component code to understand what to track and backend service code to understand what actually happened • You know the difference between a metric and an insight. You don't just report that activation dropped 5% - you dig into why and bring a hypothesis • You're technical enough to ship code but product-minded enough to know what's worth measuring. You've seen analytics implementations that track everything and answer nothing, and you know how to avoid that • You communicate clearly with non-technical stakeholders. Your dashboards have context, your insights have recommendations, and your event names make sense to someone who didn't write them • OUR STACK (FOR THIS ROLE) • Event-based analytics platform - you'll help evaluate and own the choice • TypeScript / React - frontend application where client-side events are captured • Node.js / Fastify - backend services where server-side events need to be added • PostgreSQL - primary database • GitHub (Issues, Projects, Actions) • Gong, Salesforce - customer feedback sources you'll occasionally pull from to correlate usage with sentiment