Stripe·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Stripe coding screen for a software engineer role, the problem was a file processing task involving a left outer join across two CSVs. Pretty implementation-heavy, less algorithmic than I expected.

Questions Asked (1)

Given two files (a customer file and a processor file), iterate over the customer file and produce an output row for every customer record. If no matching record exists in the processor file, still emit the customer row but leave the processor columns blank while preserving all comma delimiters. Essentially implement a left outer join over flat files.

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

The join logic itself wasn't the hard part.

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AI HintsAI Generated

Suggested Approach

Clarify the file formats, sizes, and whether the processor file can fit in memory. Then propose a two-pass approach: load the processor file into a hash map keyed by the join key, then stream the customer file, looking up each record and emitting the joined row or a row with empty processor columns. Discuss trade-offs like memory usage, sorting, and external merge join for large files.

Pro tip: Mention that you would handle edge cases like duplicate keys in the processor file (e.g., by keeping the first match or aggregating) and ensure proper CSV escaping for fields containing commas or quotes.

1. Clarify requirements and constraints

Ask about file sizes, memory limits, key uniqueness, and output format expectations. Confirm whether the processor file can fit in memory or if an external sort-merge join is needed.

2. Choose join strategy

If the processor file is small, use a hash map for O(1) lookups. If both files are large, propose sorting both files by key and performing a merge join, or using an external hash join with partitioning.

3. Implement the join logic

Stream the customer file line by line, parse each record, look up the key in the processor data, and construct the output row. If no match, emit the customer fields followed by empty placeholders for processor columns, preserving delimiters.

4. Handle edge cases and data quality

Address duplicate keys, missing keys, malformed lines, and CSV escaping. Decide on behavior for multiple matches (e.g., emit multiple rows or take first).

5. Discuss performance and scalability

Analyze time and space complexity, memory usage, and I/O. Mention optimizations like buffered reading/writing, parallel processing, or using a database if files are huge.

Key Points to Mention

Hash map vs. sort-merge join trade-offs (memory vs. time, disk I/O)
Streaming processing to handle large files without loading everything into memory
Preserving CSV structure: correct number of delimiters, quoting fields with commas
Handling duplicate keys in the processor file (e.g., first match, last match, or emit multiple rows)
Error handling for malformed records or missing join keys
Complexity analysis: O(n + m) time with hash join, O(n log n + m log m) with sort-merge

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