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Stripe·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Stripe coding round for a Software Engineer role. The problem was KYC data validation and you had to manually paste test cases into the input/output fields to run them, which was a bit clunky.

Questions Asked (1)

Q1

Implement a KYC data validation system, handling various input formats and edge cases, with test cases verified through manual I/O.

Algorithms & Data StructuresAPI & IntegrationsTechnical Trade-offs
Author's notes

The problem itself wasn't too wild but the testing setup was annoying.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and defining a clear validation schema for KYC data, including expected formats and edge cases. Then design a modular validation pipeline that normalizes inputs, applies format-specific checks, and aggregates errors. Finally, implement comprehensive test cases covering valid, invalid, and boundary inputs, and verify them through manual I/O.

Pro tip: Demonstrate a test-driven approach by writing test cases first, and emphasize the importance of clear error messages and logging for debugging and compliance. Also, mention how you would handle internationalization and data privacy concerns.

1. Clarify Requirements and Scope

Ask clarifying questions about the types of KYC data (e.g., names, addresses, IDs, dates), expected formats, and regulatory requirements. Define what constitutes valid and invalid data, and identify edge cases like missing fields, incorrect formats, and international variations.

2. Design Validation Architecture

Outline a modular system with separate validators for each data type, a normalization layer to handle different input formats, and an error aggregation mechanism. Consider using a schema validation library or custom rules, and discuss trade-offs between strict and lenient validation.

3. Implement Validation Logic

Write code for each validator, ensuring they handle edge cases such as whitespace, case sensitivity, date formats, and international characters. Use regular expressions, parsing libraries, and checksum algorithms where appropriate, and ensure errors are collected with clear messages.

4. Create and Run Test Cases

Develop a comprehensive set of test cases covering valid inputs, invalid inputs, and boundary conditions. Manually verify the output by running the tests and inspecting the results, ensuring that all edge cases are addressed and error messages are accurate.

5. Review and Iterate

Refactor code for readability and maintainability, and consider performance implications. Discuss how the solution could be extended for new data types or formats, and how it integrates with existing systems.

Key Points to Mention

  • Input normalization: handling different formats (e.g., dates, phone numbers, addresses) and trimming whitespace.
  • Edge cases: missing fields, invalid characters, international formats, and boundary values (e.g., minimum/maximum lengths).
  • Error handling: aggregating multiple errors, providing clear and actionable error messages, and logging for audit.
  • Test coverage: unit tests for each validator, integration tests for the pipeline, and manual verification of I/O.
  • Trade-offs: strict vs. lenient validation, performance vs. thoroughness, and use of third-party libraries vs. custom code.
  • Security and compliance: data privacy, encryption, and adherence to KYC regulations (e.g., GDPR, AML).

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