Amazon·Software Engineer·Onsite - System Design / Architecture·Senior

Senior

May 2026

Summary

Amazon system design round focused on reviewing an existing architecture rather than building something new. The whole thing was about finding what breaks, what's over-built, and how to fix it without hiring more people.

Questions Asked (1)

You're handed an existing system. Walk through how you'd identify which components are likely to fail in the next six months, where the design is over-engineered, and what improvements you'd propose without adding headcount.

System DesignTechnical Trade-offsRoot Cause Analysis

Author's notes

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Suggested Approach

Start by describing a systematic discovery process: gather data from monitoring, logs, and incident history to identify failure-prone components. Then assess over-engineering by evaluating complexity versus business value and usage. Finally, propose improvements that leverage automation, simplification, and existing resources to avoid headcount increases.

Pro tip: Frame your answer around business impact and customer experience—Amazon values decisions that improve reliability and reduce cost without sacrificing innovation. Quantify risks and improvements where possible.

1. Gather Data and Identify Failure Risks

Collect metrics, logs, incident reports, and dependency graphs to pinpoint components with high error rates, frequent outages, or scaling bottlenecks. Use historical trends to predict likely failures in the next six months.

2. Assess Over-Engineering

Evaluate components for unnecessary complexity: look for features with low usage, excessive abstraction, or redundant layers. Compare design complexity against actual business requirements and scalability needs.

3. Prioritize Improvements by Impact and Effort

Rank identified issues based on potential impact (e.g., customer impact, revenue loss) and effort to fix. Focus on high-impact, low-effort changes first, such as configuration tweaks or automation.

4. Propose Solutions Without Adding Headcount

Suggest improvements that leverage automation, simplification, and existing tools—e.g., auto-scaling, circuit breakers, removing dead code, or consolidating services. Emphasize cross-training and process improvements.

5. Define Success Metrics and Iterate

Outline how you would measure the success of improvements (e.g., reduced incident count, lower latency, cost savings) and establish a feedback loop for continuous refinement.

Key Points to Mention

Use of monitoring and observability tools (e.g., CloudWatch, X-Ray) to identify failure patterns
Root cause analysis techniques (e.g., 5 Whys, fishbone) to understand underlying issues
Trade-offs between reliability, cost, and complexity (e.g., over-engineering vs. under-engineering)
Automation and self-healing mechanisms to reduce manual intervention
Incremental improvements and prioritization based on business impact
Leveraging existing team skills and cross-functional collaboration to avoid headcount increases

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.