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

SeniorPrefer not to say

Jun 2026Remote

Summary

Instacart system design round for a software engineering role. One big open-ended incident investigation question that basically ate the whole session. Felt like a senior-level bar even if the title didn't say so.

Questions Asked (1)

You're on call for a microservices banking platform and users start reporting intermittent failures and high latency on transfers at a specific time. Walk through your full troubleshooting process: what you check first, how you form and test hypotheses, how you narrow down whether the problem is in the client, network, a service, the database, or an external dependency, what mitigations you'd apply safely in the short term, and how you'd verify the fix and run a postmortem.

Root Cause AnalysisSystem DesignTechnical Trade-offs

Author's notes

This one sprawled in every direction and I kept second-guessing my own structure.

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AI HintsAI Generated

Suggested Approach

Start by acknowledging the incident and stating that you'd first assess impact and stabilize the system, then systematically narrow down the root cause using observability data and hypothesis testing. Emphasize a structured, data-driven approach that covers client, network, service, database, and external dependencies, and ends with safe mitigations, verification, and a blameless postmortem.

Pro tip: Mention that you'd check for recent deployments or configuration changes first, as they are the most common cause of sudden intermittent failures. Also, highlight the importance of time-boxing each hypothesis test to avoid rabbit holes.

1. Assess Impact and Stabilize

Determine the scope and severity: which users, regions, and transfer types are affected. If possible, apply immediate safe mitigations like rate limiting, circuit breakers, or rolling back recent changes to reduce user impact while investigating.

2. Gather Data and Form Hypotheses

Use monitoring dashboards, logs, traces, and metrics to identify patterns (e.g., time correlation, error types, latency percentiles). Form hypotheses about potential causes across the stack: client, network, service, database, or external dependency.

3. Test Hypotheses and Narrow Down

Systematically test each hypothesis by querying specific metrics, reproducing in a staging environment, or using canary tests. Isolate the component by checking dependencies: e.g., if database latency spikes, check slow queries; if external API errors, check third-party status.

4. Apply Safe Mitigations and Verify

Once root cause is identified, apply the least invasive fix (e.g., increase timeouts, add retries with backoff, scale resources, fix a bug). Verify the fix by monitoring key metrics and ensuring error rates and latency return to normal.

5. Conduct Postmortem and Prevent Recurrence

Schedule a blameless postmortem to document timeline, root cause, impact, and lessons learned. Identify action items to prevent recurrence, such as improved monitoring, automated rollbacks, or architectural changes.

Key Points to Mention

Use of observability tools (metrics, logs, traces) to correlate the issue with specific times or events.
Check for recent deployments, configuration changes, or infrastructure changes as likely triggers.
Differentiate between client-side, network, service, database, and external dependency issues by examining error patterns and latency distributions.
Apply safe short-term mitigations like feature flags, circuit breakers, or rollbacks to reduce user impact.
Verify the fix by monitoring key performance indicators (KPIs) and ensuring no regression.
Conduct a blameless postmortem with actionable follow-ups to improve system resilience.

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