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

SeniorPrefer not to say

Apr 2026Remote

Summary

System design interview at Headway for a software engineer role. Two questions, both focused on backend scaling under load. Pretty technical throughout, no behavioral stuff at all.

Questions Asked (2)

Your backend service is timing out under high request volume. How do you diagnose what's wrong and what do you do about it, both right now and longer term?

System DesignRoot Cause AnalysisTechnical Trade-offs

Author's notes

I started with the obvious stuff, check metrics, look at CPU and memory, see if it's the DB choking or the app layer.

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AI HintsAI Generated

Suggested Approach

Start by acknowledging the immediate need to stabilize the service, then walk through a structured diagnostic process covering metrics, logs, and traces to identify the bottleneck. Finally, discuss both short-term mitigations and long-term architectural improvements, emphasizing trade-offs and prevention.

Pro tip: Show that you prioritize user impact by first checking if you can quickly mitigate (e.g., scale out, enable rate limiting) while gathering data for root cause. This demonstrates calm under pressure and a bias for action.

1. Stabilize the service

Implement immediate mitigations like scaling horizontally, enabling rate limiting, or shedding load to restore functionality and reduce user impact.

2. Gather data and diagnose

Use monitoring tools (metrics, logs, traces) to identify the bottleneck: check CPU, memory, I/O, database queries, external dependencies, and error rates.

3. Identify root cause

Analyze the data to pinpoint the underlying issue, such as a slow database query, inefficient code, resource contention, or a downstream dependency failure.

4. Implement short-term fixes

Apply targeted fixes like optimizing queries, adding caching, increasing timeouts, or rolling back a recent change to resolve the immediate problem.

5. Plan long-term improvements

Propose architectural changes like horizontal scaling, circuit breakers, load testing, and better observability to prevent recurrence and handle future growth.

Key Points to Mention

Use of monitoring and observability tools (e.g., Prometheus, Grafana, Datadog, ELK stack) to diagnose issues.
Common bottlenecks: database connection pools, slow queries, thread pool exhaustion, memory leaks, and external API latency.
Short-term mitigations: auto-scaling, rate limiting, circuit breakers, and graceful degradation.
Long-term solutions: caching strategies, asynchronous processing, database sharding, and load testing.
Trade-offs: consistency vs. availability, cost vs. performance, and complexity vs. maintainability.
Prevention: implementing SLOs, alerting, chaos engineering, and capacity planning.

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

Given a set of assumptions about users, request rates, and data volume, how would you estimate the server capacity you actually need?

System DesignTechnical Trade-offs

Author's notes

This one I liked more.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the assumptions and translating them into concrete workload metrics like QPS and data size. Then walk through a structured estimation process, from single-server capacity to required server count, incorporating redundancy and headroom. Finally, discuss trade-offs and validation strategies.

Pro tip: Always state your assumptions explicitly and show how changes in assumptions affect the result; this demonstrates that you understand the sensitivity of capacity planning. Also, mention that you'd validate estimates with load testing and monitoring before finalizing.

1. Clarify Assumptions and Requirements

Ask clarifying questions to understand user base, request patterns, data volume, growth projections, and performance SLAs. Confirm whether estimates should be for peak or average load.

2. Convert to Workload Metrics

Translate assumptions into quantifiable metrics: requests per second (RPS), concurrent connections, data storage and transfer rates, and compute requirements per request.

3. Estimate Single-Server Capacity

Determine the capacity of a typical server by benchmarking or using known performance characteristics (e.g., CPU, memory, I/O limits). Calculate how many requests one server can handle.

4. Calculate Required Servers and Add Overhead

Divide total workload by single-server capacity to get baseline server count. Add redundancy (N+1, N+2) and headroom (e.g., 30-50%) for spikes and failures.

5. Validate and Iterate

Propose validation through load testing, monitoring, and gradual rollout. Discuss how to adjust estimates as real traffic data becomes available.

Key Points to Mention

Back-of-the-envelope calculations (e.g., QPS, storage per user, bandwidth)
Peak vs. average load and diurnal patterns
Redundancy and fault tolerance (N+1, multi-AZ)
Headroom for growth and unexpected spikes
Cost implications and trade-offs (vertical vs. horizontal scaling)
Load testing and monitoring for validation

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