Stripe·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Stripe coding screen for a software engineer role. One problem, pretty focused on transaction processing logic. Felt straightforward on the surface but there are a few edge cases that'll bite you if you're not careful.

Questions Asked (1)

You're processing a stream of bank transactions with account name, timestamp, currency, and amount fields. Positive amounts are credits, negative are debits. If a transaction would push an account's balance below zero, reject it and keep the balance unchanged. After processing everything, return all accounts with non-zero balances and the list of rejected transactions in the original order.

Algorithms & Data StructuresData ModelingTechnical Trade-offs

Author's notes

The core structure is a hash map keyed by account name.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then propose a solution using a hash map to track balances and a list for rejected transactions. Walk through the algorithm step-by-step, emphasizing time/space complexity and potential optimizations.

Pro tip: Discuss how you would handle concurrency and idempotency, as these are critical in financial systems like Stripe's. Also, mention the importance of maintaining the original order of rejected transactions and considering currency handling.

1. Clarify Requirements and Edge Cases

Ask questions to confirm assumptions: Are timestamps unique? Can multiple currencies exist per account? Should we consider currency conversion? What about initial balances? How to handle duplicate transactions?

2. Design Data Structures

Choose a hash map to store account balances (keyed by account name) for O(1) lookups. Use a list to collect rejected transactions in order. Consider if additional metadata (e.g., currency) needs tracking.

3. Outline Algorithm

Iterate through transactions sequentially. For each, compute new balance. If new balance < 0, add to rejected list; else update balance. After processing, filter accounts with non-zero balances.

4. Analyze Complexity and Trade-offs

State time complexity O(n) and space O(m + r) where m is number of accounts and r is number of rejected transactions. Discuss trade-offs: e.g., using a database for persistence vs in-memory, handling large streams.

5. Discuss Extensions and Production Considerations

Mention how to handle concurrency (e.g., locking), idempotency (deduplication), currency conversion, and audit logging. Highlight potential optimizations like batch processing.

Key Points to Mention

Use a hash map for O(1) balance lookups and updates.
Maintain rejected transactions in original order using a list.
Time complexity O(n), space O(m + r).
Edge cases: initial balance, multiple currencies, duplicate transactions.
Concurrency and idempotency are crucial in financial systems.
Consider trade-offs between in-memory and persistent storage.

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