Stripe·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Stripe coding round for a software engineering role, basically one big CSV join problem that kept growing. They layer on requirements as you go, so what looks like a simple string manipulation exercise turns into a fairly involved data processing question by the end.

Questions Asked (1)

Implement a function that joins two CSV-style datasets on a given field name, handling unmatched rows, multiple matches on one side, and an optional flag to skip rows with no match.

Algorithms & Data StructuresAPI & IntegrationsTechnical Trade-offs

Author's notes

This thing starts simple and then they just keep adding.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: input format (list of dicts or CSV strings?), output format, handling of unmatched rows (include with nulls or skip), and multiple matches (cartesian product). Then outline an algorithm using hash maps for efficiency, and discuss trade-offs like memory vs. time, and edge cases like duplicate keys or missing fields.

Pro tip: Mention that you'd build an index on the smaller dataset to optimize memory and performance, and discuss how to handle large datasets that don't fit in memory (e.g., external sort-merge join). This shows practical engineering maturity.

1. Clarify requirements and edge cases

Ask about input/output formats, behavior for unmatched rows (include with nulls or skip), multiple matches (cartesian product), and whether the join key is unique on either side. Also confirm if the function should be in-memory or handle large datasets.

2. Choose data structures and algorithm

Propose using a hash map to index one dataset by the join key, then iterate over the other dataset to find matches. Discuss time complexity O(n+m) and space complexity O(n) where n is the size of the indexed dataset.

3. Handle multiple matches and unmatched rows

For multiple matches, produce a cartesian product of matching rows. For unmatched rows, if the skip flag is true, omit them; otherwise, include with nulls for the missing side's fields.

4. Implement and test with edge cases

Write clean code with helper functions for indexing and joining. Test with cases: no matches, all matches, multiple matches, missing join key, and empty datasets.

5. Discuss trade-offs and optimizations

Talk about memory vs. time trade-offs, choosing which dataset to index (smaller one), and potential optimizations like sorting for external joins or using database indexes if applicable.

Key Points to Mention

Hash map indexing for O(1) lookups on the join key
Cartesian product for multiple matches on one side
Handling unmatched rows with nulls or skipping based on flag
Time and space complexity analysis (O(n+m) time, O(n) space)
Edge cases: missing join key, empty datasets, duplicate keys
Trade-offs: in-memory vs. external join for large datasets

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