Feature Flagging & Experimentation
Feature flagging is the platform engineering practice of decoupling code deployment from feature release via progressive rollouts, multivariate experiments, and real-time flag management. Career path: Practitioner (basic flags + percentage rollouts, $100-130k) → Advanced (targeting rules, experimentation integration, flag platform design, $145-185k) → Expert (custom flag services, real-time systems, blast radius management, $185-250k). Used by Netflix, Facebook, Uber to ship daily without fear. Ecosystem: LaunchDarkly, Split.io, Statsig, Unleash, Flagsmith, Optimizely, Eppo, ConfigCat, GrowthBook, PostHog.
什麼是 Feature Flagging & Experimentation
Ship code without deploying features. Progressive rollouts, A/B tests, kill switches. Decouple deployment from release. Modern continuous delivery practice. Learning Curve: Medium (tooling + system design)
💰 各地區薪資
| 地區 | 初級 | 中級 | 高級 |
|---|---|---|---|
| USA | $105,000 | $155,000 | $210,000 |
| UK | £65,000 | £95,000 | £135,000 |
| EU | €72,000 | €105,000 | €150,000 |
| CANADA | CA$115,000 | CA$165,000 | CA$225,000 |
🎓 認證
❓ 常見問題
Feature flagging vs feature flags, what's the difference?
What's the relationship between feature flagging and trunk-based development?
How do I avoid flag technical debt?
How do multivariate flags differ from A/B testing flags?
What's the OpenFeature standard and why does it matter in 2026?
How do I measure flag impact on system performance (latency, cold starts)?
What's a realistic cost for a feature flagging platform in 2026?
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