Senior Full Stack Engineer, Experimentation - Product Analytics

About Grafana Labs

Grafana Labs is the company behind Grafana Cloud, the fully managed observability platform trusted by more than 10,000 organizations to ensure reliability, resolve incidents faster, and optimize telemetry at scale. Built on open source and open standards and designed for interoperability across any stack, Grafana Cloud brings AI to observability and observability to AI, giving teams unified visibility so they can see, understand, and act on all their data.

We are a 100% remote company with team members across 40+ countries, backed by leading investors including Lightspeed Venture Partners, Sequoia Capital, GIC, Coatue, J.P. Morgan, CapitalG, and Lead Edge Capital.

The Opportunity

Grafana Labs is building a native experimentation platform: a Grafana app that lets teams define A/B tests, connect them to feature flags and product telemetry, and get trustworthy, statistically sound answers about what their changes actually did.

Having started as a hackathon project, it is now used internally by Grafana engineering and product teams. The next step is to turn it into a first-class product available to Grafana Cloud customers alongside Frontend Observability, Session Replay, and user-experience capabilities.

You'll join the Product Analytics team, a group of technical product analysts who help teams across Grafana understand product usage and run experiments. As the team's dedicated software engineer, you'll own the engineering of the experimentation app and partner closely with data scientists, data engineers, software engineers, and product stakeholders. This is a hands-on engineering role shipping production code.

About the Product Stack

• Frontend: TypeScript and React app plugin using @grafana/ui, plus a custom datasource plugin powering 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 GitOps-style management support.
• 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: Bayesian analysis engine (TypeScript, with a Go port) producing direction-aware verdicts 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, queried live through Grafana datasources.
• AI: Experiment authoring assisted by Grafana Assistant.

What You'll Be Doing

• Evolve the experimentation app from internal tool to product: Take full ownership of features end to end across the React frontend and Go backend, from problem framing through rollout and measurement.
• Build the experimentation experience: Develop intuitive workflows for defining hypotheses, configuring experiments/metrics, managing their lifecycle, and exploring results.
• Simplify instrumentation: Evolve the experimentation SDK and its OpenFeature and Faro integrations to help teams connect variant assignment, exposure, and outcome events.
• Integrate with Grafana's UX stack: Connect with Grafana Faro and Frontend Observability so flag exposures and outcomes are captured automatically, linking results to Session Replay and critical user journeys.
• Harden the statistics and analysis path: Work with data analysts to extend the statistics engine, moving computation server-side, enabling scheduled analysis, health alerting, and statistical guardrails.
• Leverage AI thoughtfully: Extend Grafana Assistant integration for authoring, explaining, and reporting on experiments.
• Raise the engineering bar: Mentor teammates, write ADRs, design docs, and demos to drive internal adoption.
• Operational ownership: Participate in on-call rotations for the services you build.

Qualifications

• Full-stack engineering experience: Proven track record building and operating production web applications with TypeScript, React, and backend services in Go (or similar language with willingness to learn Go).
• Experimentation platform experience: Prior experience building an in-house platform or integrating existing experimentation/A/B testing tooling (e.g., Statsig, Eppo, GrowthBook, Optimizely, LaunchDarkly).
• Product and data intuition: Comfortable working with event data, SQL, and data warehouses such as BigQuery.
• Technical ownership: Ability to deliver complex projects asynchronously with short feedback loops and minimal supervision.
• Operational responsibility: On-call experience and a deep understanding of software observability and debuggability.
• Pragmatic AI adoption: Thoughtful use of AI coding assistants to improve development speed and quality.
• Clear communication: Ability to explain complex technical and statistical tradeoffs asynchronously in a remote environment.

Bonus Points

• Hands-on experience with feature flags and OpenFeature.
• Working understanding of experiment statistics (Bayesian/frequentist testing, sample sizing, variance reduction, peeking, sample ratio mismatch).
• Experience with Kubernetes concepts (custom resources, operators, webhooks, GitOps).
• Experience building Grafana plugins, apps, Scenes, or using the Grafana App SDK.
• Familiarity with frontend observability and product analytics tooling (Grafana Faro, RUM, RudderStack, Segment).
• Open source contributions or maintainership.
• Experience as a user of Grafana, Prometheus, Loki, or Tempo.

Compensation & Benefits

• Base Salary Range: £94,000 - £118,000 GBP (UK base range; regional variations apply for Ireland, Sweden, Spain, and Germany).
• Equity: Restricted Stock Units (RSUs) included with all roles.
• Annual Leave: 30 days per annum (including 3 reserved Grafana Shutdown Days).
• Developer Productivity: Company-funded usage budget for modern AI coding tools and access to frontier models (OpenAI, Anthropic, Google).
• Remote Culture: 100% remote working model with in-person onboarding options.