Stripe·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Stripe SWE interview with a coding problem centered on building the validation layer of a fraud-detection pipeline. The problem looked like a clean CSV parsing task on the surface but had enough edge cases baked in that you had to think carefully about what a 'real' implementation would need.

Questions Asked (1)

Write a function that reads a transaction CSV file row by row and validates that each row has all seven required fields present and non-empty, returning a structured result per row that includes the row number, a VALID or INVALID status, and the names of any missing fields. Handle edge cases like an empty file, a header row, rows with the wrong number of columns, and quoted fields with embedded commas.

Algorithms & Data StructuresAPI & IntegrationsTechnical Trade-offs

Author's notes

The core logic came together pretty fast but I almost wrote a naive split-on-comma approach before catching myself.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then outline a solution that uses a proper CSV parser to handle quoted fields, validates each row against the seven required fields, and returns a structured result per row. Discuss trade-offs like streaming vs. loading entire file, error handling, and how to handle the header row.

Pro tip: Mention that you would use a battle-tested CSV library (e.g., Python's csv module) rather than splitting on commas, because quoted fields with embedded commas are a common source of bugs. Also, explicitly state how you handle the header row and empty file to show attention to detail.

1. Clarify requirements and edge cases

Ask about the exact seven field names, whether the header row is always present, and how to handle malformed rows (e.g., wrong number of columns). Confirm the expected output format.

2. Choose a CSV parsing strategy

Decide to use a robust CSV parser that correctly handles quoted fields with embedded commas. Discuss whether to stream the file row by row or load it entirely, considering memory constraints.

3. Design the validation logic

For each data row, check that all seven required fields are present and non-empty. If the row has fewer columns, treat missing columns as missing fields; if more, decide whether to ignore extras or mark invalid.

4. Define the structured result

Return a list of objects (or dictionaries) with row number (1-indexed for data rows), status (VALID/INVALID), and a list of missing field names. Ensure row numbers account for the header row.

5. Handle edge cases explicitly

Address empty file (return empty list), header-only file (no data rows), and rows with wrong number of columns. Explain how you detect and report these cases.

Key Points to Mention

Use a proper CSV parser to handle quoted fields with embedded commas, not naive string splitting.
Validate that each of the seven required fields is present and non-empty (e.g., not None or empty string).
Handle the header row by skipping it and adjusting row numbers accordingly.
For rows with fewer columns, map missing columns to missing field names; for extra columns, decide whether to ignore or flag as invalid.
Return a structured result per row, such as a list of dictionaries with row_number, status, and missing_fields.
Consider streaming the file for large files to avoid memory issues, and discuss error handling for malformed CSV.

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