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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Full Stack Engineer | Experimentation - Product Analytics - **Company:** Greenhouse Software, Inc. - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $167,000 - $209,000 - **Contract:** Permanent contract - **Skills:** A/B Testing, Artificial Intelligence, Automation of Tests, BigQuery, Code Review, Data Warehousing, Software Design Documents, Open Source Technology, OpenAI, Prometheus, Secure Coding, SQL Databases, TypeScript, Web Applications, Usage Analysis, ReactJS, Grafana, AI Coding Agents, Backend, Kubernetes, Statsig, Front End Software Development, Optimizely, Webhooks - **Published:** November 13, 2026 - **Apply:** https://job-boards.greenhouse.io/grafanalabs/jobs/6261013004 ## About the Role We highly value people who are intellectually curious, default to transparency, have a strong bias towards action, and are kind and enjoy working as a team. * Strong full-stack engineering experience. You've built and operated production web applications with TypeScript and React, and backend services in Go (or a similar language, with willingness to work in Go). You value maintainability, automated testing and secure development practices. * Experience building an in-house platform or integrating existing experimentation and A/B testing platform (for example Statsig, Eppo, GrowthBook, Optimizely, LaunchDarkly experiments, or an in-house platform). * Product and data intuition. You care whether what you shipped actually moved the needle, and you're comfortable working with event data, SQL and data warehouses such as BigQuery. * Technical ownership. You've delivered complex projects with minimal supervision, broken them into short feedback loops (analyze, design, ship an MVP, learn, iterate), and helped teammates work more effectively. * Operational responsibility. You've been on call, supported software in production, and know how to make systems observable and debuggable. * Pragmatic AI usage. You use AI coding tools thoughtfully, validate their output, and share practices that improve the team's speed and quality. * Clear communication. You can explain technical and statistical tradeoffs to engineers, analysts and product managers in a fully remote, asynchronous environment. Bonus Points For: * Hands-on experience with feature flags and OpenFeature. * A working understanding of experiment statistics: Bayesian or frequentist testing, sample sizing, variance reduction, and common pitfalls such as peeking and sample ratio mismatch. * Experience with Kubernetes concepts: custom resources, operators, admission webhooks, GitOps. * Experience building Grafana plugins or apps, or with the Grafana App SDK or Scenes. * Familiarity with frontend observability and product analytics tooling, such as Grafana Faro, RUM, session replay, RudderStack or Segment. * Open source contribution or maintainership. * Experience as a user of Grafana, Prometheus, Loki or Tempo, whether at work or on a homelab. ## Description * Frontend: a TypeScript and React app plugin using @grafana/ui, plus a custom datasource plugin that powers experiment statistics panels. * Backend: Go, built with the Grafana App SDK. Experiments are Kubernetes-style custom resources defined in CUE, with an operator, admission webhooks, and support for declarative, GitOps-style management. * Feature flags: integration with OpenFeature and the OpenFeature Remote Evaluation Protocol (OFREP), Grafana's multi-tenant feature toggle service, and an embedded GO Feature Flag evaluator. * Statistics engine: a Bayesian analysis engine (TypeScript, with a Go port) that produces a direction-aware verdict per metric, probability to be best, credible intervals, expected loss and sample-size recommendations. * Data: experiment exposure and outcome events flow from product instrumentation through RudderStack into BigQuery, or from Grafana Faro into Loki, and are queried live through Grafana datasources. * AI: experiment authoring assisted by Grafana Assistant, which discovers relevant data and drafts the experiment configuration for a human to review. What You'll be doing: * Evolve the experimentation app from internal tool to product. Full ownership of features end to end across the React frontend and Go backend, from problem framing and design through rollout, measurement and iteration. * Build the experimentation experience. Develop intuitive workflows for defining hypotheses, configuring experiments and metrics, managing their lifecycle, and exploring results. Make these experiences useful for product managers and analysts as well as engineers. * Make instrumentation easier to adopt and trust. Evolve the experimentation SDK and its OpenFeature and Faro integrations. Help teams connect variant assignment, actual exposure, and outcome events with consistent identity and event contracts. * Connect experiments to the rest of Grafana's user-experience stack. Integrate with Grafana Faro and Frontend Observability so flag exposures and outcomes are captured automatically, and link experiment results to Session Replay and critical user journeys so teams can see why a variant won, not only that it won. * Harden the statistics and analysis path. Work with our data analysts to extend the statistics engine, and make analysis scalable, cost-aware and correct. Examples include moving computation server-side, scheduled analysis, alerting on experiment health, and guardrails against misleading results. * Use AI where it genuinely helps. Extend the Grafana Assistant integration for authoring, explaining and reporting on experiments. * Raise the engineering bar. Mentor teammates, including analysts who contribute code, through code review, pairing and design docs. Write the architecture decision records (ADRs), docs and demos that help other teams adopt experimentation. * Own what you ship. Take part in on-call for the services you build, and support the internal teams that run experiments on them. We invest heavily in developer productivity. You can use modern AI coding assistants as part of your daily workflow (your choice of tools, within security guidelines), backed by a company-funded usage budget so you can iterate quickly without unnecessary friction. We encourage pragmatic AI-assisted development: faster prototyping, test generation, refactors, documentation, and incident follow-ups, always with strong code review and quality standards. You'll also have access to the latest frontier models from OpenAI, Anthropic, and Google.