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Stripe·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Stripe coding screen for a software engineer role, pretty focused on a data validation problem around KYC fields. Nothing flashy, but the edge case discussion was where things got interesting.

Questions Asked (1)

Q1

Given a list of business account records, each containing 6 KYC fields, output VERIFIED for records where all 6 fields are present and non-empty, and NOT_VERIFIED for any record that fails that check. How do you handle edge cases around what counts as 'present'?

Algorithms & Data StructuresTechnical Trade-offsAdaptability & Ambiguity
Author's notes

The core logic is dead simple, loop through records and check the fields.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the definition of 'present' and 'non-empty' for each KYC field, considering data types and business rules. Then outline a robust algorithm that handles edge cases like whitespace, null, undefined, and type mismatches, and discuss trade-offs between strictness and practicality. Finally, emphasize the importance of validation and testing to ensure correctness.

Pro tip: Mention that in a real-world system like Stripe, you'd likely have a centralized validation utility to ensure consistency across services, and you'd log edge cases for monitoring and iterative improvement.

1. Clarify requirements and edge cases

Ask clarifying questions to define what 'present' and 'non-empty' mean for each field, including handling of whitespace, null, undefined, empty strings, and type coercion.

2. Design the validation logic

Propose a function that iterates over the 6 fields, checking each against the defined criteria, and returns VERIFIED only if all pass.

3. Handle edge cases explicitly

Detail how to treat edge cases: trim whitespace, reject null/undefined, consider empty arrays/objects as empty, and decide on type-specific checks (e.g., numbers must be > 0).

4. Discuss trade-offs and scalability

Talk about strict vs. lenient validation, performance for large lists, and potential need for configuration or field-specific rules.

5. Test and validate

Mention writing unit tests for edge cases and using sample data to verify the logic before deployment.

Key Points to Mention

  • Definition of 'present': not null, not undefined, and not an empty string after trimming whitespace.
  • Handling of different data types: strings, numbers, booleans, and ensuring they are not empty or default values.
  • Use of a helper function to avoid code duplication and ensure consistency across fields.
  • Consideration of business rules: some fields may have specific formats (e.g., email, date) that require additional validation.
  • Performance implications: for large datasets, optimize by short-circuiting on first missing field.
  • Importance of logging and monitoring for edge cases in production to refine validation rules.

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