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Stripe·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Stripe coding screen for a software engineer role, one question about extending a CSV join utility. Pretty focused problem, not a lot of back and forth.

Questions Asked (1)

Q1

You're given a function that joins two CSV-like datasets on a key field, defaulting to a left join. Extend it so that a boolean flag controls whether unmatched rows are skipped entirely or kept with empty values for the missing columns.

API & IntegrationsAlgorithms & Data StructuresTechnical Trade-offs
Author's notes

The left join part was fine, I had that logic pretty quick.

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AI HintsAI Generated

Suggested Approach

First, clarify the function's current behavior and the exact semantics of the boolean flag (e.g., true means skip unmatched rows). Then, outline a clean implementation that separates the join logic from the filtering step, ensuring backward compatibility by defaulting to the existing left join behavior. Finally, discuss edge cases and trade-offs such as performance and memory usage.

Pro tip: Mention that you would add the flag as an optional parameter with a default value that preserves the current left join behavior, and consider using an enum instead of a boolean for future extensibility (e.g., to support inner, right, or full joins).

1. Clarify requirements and current behavior

Ask questions to confirm the function's current join behavior, the meaning of the boolean flag, and whether the flag should default to preserving existing behavior. Ensure you understand what 'empty values' means for missing columns (e.g., null, empty string).

2. Design the API change

Decide how to add the flag: as an optional parameter with a default value that maintains backward compatibility. Consider naming and type (boolean vs. enum) for clarity and future extension.

3. Outline the implementation

Describe how you would modify the join logic: perform the left join as before, then if the flag indicates skipping unmatched rows, filter out rows where the key from the right dataset is missing. Alternatively, conditionally include unmatched rows during the join.

4. Handle edge cases and data types

Discuss how to handle missing values (null vs. empty string), duplicate keys, and type mismatches. Ensure the flag works consistently regardless of data types.

5. Discuss trade-offs and testing

Mention performance implications (e.g., filtering after join vs. during join) and memory usage. Suggest unit tests for both flag states and edge cases.

Key Points to Mention

  • Backward compatibility: default flag value should preserve existing left join behavior.
  • Clear semantics: define what the boolean flag represents (e.g., true = skip unmatched rows).
  • Implementation approach: filter after join or conditionally include unmatched rows.
  • Edge cases: handling nulls, empty strings, duplicate keys, and type mismatches.
  • Performance considerations: filtering after join may be less efficient than during join.
  • Testing: unit tests for both flag states and edge cases.

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