Performance and reliability improvements
Continuously improved performance, quality, and operational ownership on a high-traffic travel comparison and booking platform.
Context
Booking web experiences—including WebView contexts—needed measurable performance and reliability work without regressing core booking behaviour.
Technical challenge
Find real bottlenecks across SSR, cache, prefetch, and divergent web vs WebView behaviour; prove improvements with automation instead of only manual QA.
Constraints
Shared codepaths, release risk on high-traffic flows, and limited ability to publish exact internal metrics externally.
Ownership
Owned performance and reliability improvements on the affected paths, including test automation that reduced manual verification load.
Decisions
Prioritized profiling-backed changes and targeted caching/prefetch over blanket rewrites. Invested in Playwright coverage for booking-critical journeys instead of expanding manual regression only.
Approach
Strengthened tests and automation, addressed SSR/cache/prefetch hotspots, and aligned web/WebView behaviour where state mismatched.
Failure modes
Stale cache, app/web state mismatch, flaky E2E, and performance regressions that only appear under production-like conditions.
Validation
Automated E2E on critical journeys, monitoring after rollout, and before/after confidence from reduced manual test burden (order-of-magnitude operational improvement without confidential absolute numbers).
Outcome
Improved performance/reliability posture and cut a large share of manual regression effort through automation—raising deployment confidence on booking paths.
Reflection
I would capture a lightweight public performance budget earlier so regressions are easier to discuss without relying on private dashboards alone.