Stripe·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Stripe coding round focused on HTTP and JSON library usage, basically a test of whether you can read docs and wire things together under pressure. Not algorithmically hard but the devil is in the details.

Questions Asked (1)

Given a JSON file describing a sequence of HTTP requests (method, URL, headers, body, and expected response), make each call, parse the response, and verify it matches the expected output. Report any mismatches.

API & IntegrationsTechnical Trade-offs

Author's notes

The core trick here isn't the logic, it's knowing your HTTP and JSON libraries well enough to not fumble around.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline a modular design that separates HTTP execution, response validation, and reporting. Emphasize robustness through error handling, retries, and idempotency, and discuss how you would test and scale the solution.

Pro tip: Mention idempotency keys and rate-limit handling early—Stripe cares deeply about safe retries and respecting API limits. Also, propose a dry-run mode to validate the JSON schema before making any calls.

1. Clarify Requirements and Constraints

Ask about the JSON schema, expected response matching (exact vs. partial), authentication, rate limits, and error handling expectations. Confirm whether requests should be made sequentially or can be parallelized.

2. Design Modular Architecture

Propose separate components: a parser/validator for the JSON input, an HTTP client with retry and timeout logic, a response comparator, and a reporter. This separation improves testability and maintainability.

3. Implement Robust HTTP Execution

Use a resilient HTTP client with configurable timeouts, exponential backoff for retries, and idempotency keys for non-idempotent methods. Handle rate limits (429) by respecting Retry-After headers.

4. Validate and Compare Responses

Parse responses (JSON/XML) and compare against expected output using a deep equality check or a JSON schema validator. Support partial matching and ignore volatile fields like timestamps if needed.

5. Report and Log Mismatches

Collect mismatches with details (request, expected, actual) and output a clear report. Log all interactions for debugging, and consider exit codes for CI integration.

Key Points to Mention

Idempotency and safe retries for non-idempotent HTTP methods (e.g., POST) using idempotency keys.
Rate limiting and backoff strategies (exponential backoff with jitter, respecting Retry-After).
Response comparison techniques: deep equality, JSON schema validation, and handling of dynamic fields.
Error handling: network failures, timeouts, malformed responses, and partial failures.
Testing strategy: unit tests with mocked HTTP calls, integration tests against a sandbox, and contract testing.
Scalability and performance: parallel execution with concurrency limits, connection pooling, and streaming for large files.

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