Stripe·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Stripe coding screen for a software engineer role, one problem but it had a sorting requirement buried in it that I completely missed the first time through.

Questions Asked (1)

Given two CSV files each with a header row, merge rows from both files where the values in a specified field column match exactly. Output each merged row as all columns from the first file followed by all columns from the second. The output must be sorted by an 'order' field present in the header.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I got the join logic working fine, matched on the field name, combined columns, seemed good.

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AI HintsAI Generated

Suggested Approach

Clarify the requirements first, especially around duplicate keys, memory constraints, and the exact sorting behavior. Then propose a hash join approach: load the smaller file into a hash map keyed by the join column, stream the larger file to find matches, and sort the results by the 'order' field. Discuss trade-offs between in-memory and external sorting for large files.

Pro tip: Mention that you would validate the header rows and handle edge cases like missing join keys or duplicate matches, showing attention to data quality. Also, proactively discuss how you would scale the solution if the files don't fit in memory, demonstrating systems thinking.

1. Clarify requirements and constraints

Ask about file sizes, memory limits, duplicate keys, and whether the join column is guaranteed to exist in both headers. Confirm the exact output format and sorting order (ascending/descending).

2. Choose an algorithm and data structures

Propose a hash join: build a hash map from the smaller file keyed by the join column, then probe with the larger file. Alternatively, if files are sorted, use a merge join. Discuss time/space complexity.

3. Handle edge cases and data quality

Address missing values, duplicate keys (cartesian product), and header mismatches. Decide whether to skip, error, or log such cases.

4. Implement sorting and output

Collect merged rows, sort by the 'order' field (stable sort if needed), and write to output with the combined header. Consider external sorting if data is too large.

5. Discuss scalability and trade-offs

Compare in-memory vs. external sorting, hash join vs. sort-merge join, and single-threaded vs. parallel processing. Mention memory usage and I/O considerations.

Key Points to Mention

Hash join vs. sort-merge join and when to use each
Time and space complexity (O(n+m) for hash join, O(n log n) for sorting)
Handling duplicate keys and ensuring correct cartesian product
Memory constraints and external sorting algorithms (e.g., merge sort with chunks)
Data validation: header consistency, missing join keys, and type mismatches
Stable sorting to preserve order of equal 'order' values

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