Requirements
- Backend platform engineering: You've built backend systems or platforms that other engineers depend on, and understand what changes when your software becomes part of somebody else's critical path.
- Experimentation exposure at scale: You've built, operated or worked deeply with experimentation, A/B testing, feature-management or progressive-rollout systems in a large engineering environment.
- Technical leadership: You've taken ambiguous engineering problems, formed a technical direction and helped other engineers move towards an outcome.
- Driving adoption: You've built internal platforms or capabilities and understand that the technology only succeeds when other teams trust and use it.
- Technical knowledge: Backend and distributed systems: Strong software engineering fundamentals, with experience designing reliable backend systems and reasoning about scalability, availability, consistency and failure modes.
- Data systems: Enough familiarity with data engineering, analytics engineering or large-scale data processing to understand how experimentation data is generated, moved and consumed.
- Experimentation concepts: Familiarity with experiment assignment, targeting, variants, feature flags, metrics, guardrails and progressive rollout.
Nice to Have
- You've built this before: Direct experience designing or building experimentation, A/B testing, feature-management or rollout infrastructure — either inside a large technology company or for a platform vendor.
- Data crossover: A background spanning software engineering and data science, data engineering or analytics engineering.
- Feature flags: Experience building feature flagging, user targeting, progressive rollout or automated guardrail systems.
- Statistical intuition: You don't need to be a data scientist, but being comfortable enough with experimentation and statistics to work closely with one will be valuable.
