Design an Internal Tool for Experimentation Management

To design a self-service Experimentation Management platform, I would start by prioritizing the friction points PMs currently face: waiting on engineering for randomization logic and analyzing results manually. The solution should be a unified dashboard integrated with our internal data lake. First, the UI must allow PMs to define hypotheses, select target audiences via simple filters, and set confidence thresholds without writing code. Behind the scenes, we need a deterministic hashing service to ensure a user stays in the same variant across all sessions, which is critical for consistency. We would implement an asynchronous job queue to handle the heavy lifting of assigning variants and logging events to prevent blocking the main application. For analysis, the system should automatically calculate p-values and power levels, flagging results only when statistical significance is reached to avoid false positives. A key differentiator for a Stripe-like environment is safety; we would include a mandatory 'kill switch' API that allows engineers to instantly disable a feature rollout globally if error rates spike. Finally, the tool must log every decision for auditability. This approach shifts the burden from engineering to product teams while maintaining the high standards of reliability and accuracy required in financial technology.