Amazon|AerServ|Oracle: Engineering Foundation
The technical grounding that shaped how I evaluate data, risk, and leverage as a PM.
Distributed SystemsData IntegrityValidation StrategyFailure AnalysisPlatform Thinking
Challenge
- Production failures came from misunderstood system behavior — not missing tests
- Test suites passed while incidents escalated across asynchronous workflows and weak data contracts
- Data pipelines degraded silently and surfaced late through customers or operations
- Teams optimized for coverage instead of correctness
- Reason about complex systems with incomplete signals
Role
- Quality Engineer embedded in platform, API, and data-intensive systems
- Partnered with engineers and PMs to validate correctness across services and data flows
- Shifted teams from “more tests” to clearer definitions of risk and “done”
- Focused on system boundaries, assumptions, and failure modes
Approach & Decisions
test execution → failure modes → system boundaries → instrumentation
Moved from test execution to failure-mode analysis
Analyzed incidents, bugs, and regressions to identify which failure classes actually mattered.
- Prioritized high-risk paths over broad coverage
- Used real failures to guide validation strategy
Validated boundaries (not isolated components)
Targeted service boundaries and lifecycle transitions where high-impact issues emerged.
- Contract mismatches
- Async edge cases
- Environment-specific behavior
Designed validation as part of the system
Treated metrics, events, and alerts as foundational infrastructure — not a post-build checklist.
- Instrumentation to surface incorrect assumptions early
- Signals that support faster decisions
Optimized for prevention
Built confidence by catching failures earlier, reducing the cost of learning.
- Earlier detection
- Fewer production surprises
Outcomes
- Prevented critical bugs from reaching production by targeting high-risk failure modes
- Helped teams build an observability foundation (metrics, events, alerts) that enabled safer launches
- Strengthened cross-functional alignment on risk, correctness, and release readiness
Learnings
- Systems fail at boundaries where ownership, assumptions, or data contracts blur
- Automation without judgment is noise — validation must reflect real-world behavior
- Data is a diagnostic instrument (metrics, logs, events) — not vanity output
- Prevention compounds; the best work often shows up as incidents that never happen