Stripe·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Stripe coding round for a software engineer role. The whole thing was one big table-filtering problem that kept getting extended with follow-ups, each one layered on top of the last. Less about algorithms, more about whether your code could absorb new requirements without a rewrite.

Questions Asked (1)

You're given a table of records (many rows, multiple attribute columns per row). Write a function that filters or transforms the table based on a stated requirement and returns the matching rows. Then be ready to handle an open-ended series of follow-ups that each build on the previous result, like adding filters, deriving new columns, grouping, aggregating, or sorting.

Algorithms & Data StructuresTechnical Trade-offsAdaptability & Ambiguity

Author's notes

The thing that trips people up here isn't the first step, it's that the follow-ups never stop and each one assumes your last output is the input.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the exact filtering/transformation requirement and the expected output format, then implement a clean, readable solution using standard data manipulation patterns (e.g., list comprehensions, SQL-like operations, or pandas). Structure your code so that each follow-up can be handled by composing small, reusable functions rather than rewriting logic, and narrate your trade-offs as you go.

Pro tip: Treat the follow-ups as a conversation about extensibility: explicitly call out where you'd refactor (e.g., extracting a predicate, using a pipeline) and why, showing you optimize for maintainability over clever one-liners.

1. Clarify the requirement and constraints

Ask about input format (list of dicts, CSV, DataFrame), output expectations, edge cases (empty table, missing values), and performance constraints (memory, streaming vs. in-memory).

2. Design a composable solution

Sketch a modular approach: separate filtering, transformation, grouping, and sorting into small functions that can be chained or combined, making follow-ups easy to implement.

3. Implement the initial filter/transform

Write clean, idiomatic code for the first requirement, using appropriate data structures and avoiding premature optimization; explain your choices briefly.

4. Handle follow-ups incrementally

For each new requirement, extend your existing code by composing new functions or adding parameters, and discuss how you'd test and validate each addition.

5. Discuss trade-offs and scalability

Reflect on time/space complexity, readability vs. performance, and how your solution would scale to larger datasets or more complex operations.

Key Points to Mention

Modularity and composability: breaking down operations into reusable functions (e.g., filter, map, reduce) to handle evolving requirements.
Data structure choice: when to use lists, dictionaries, sets, or specialized libraries (pandas, SQL) based on access patterns and size.
Edge cases: empty input, null values, duplicate keys, and type mismatches.
Performance considerations: time/space complexity, lazy evaluation, streaming vs. batch processing.
Testing strategy: unit tests for each transformation and integration tests for chained operations.
Trade-offs: readability vs. conciseness, in-memory vs. out-of-core, and the cost of premature optimization.

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