Pinterest·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Pinterest SWE interview that came down to the classic shared expenses / balance settlement problem. Nothing too wild, but the implementation details have more edge cases than you'd expect.

Questions Asked (1)

Design and implement a system to track shared expenses among a group of people and compute the minimum set of transactions needed to settle all balances.

Algorithms & Data StructuresSystem DesignData Modeling

Author's notes

I started with the naive approach, just tracking who owes who after each expense, and it worked fine for the basic case.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and defining the data model for users, groups, and expenses. Then, compute net balances per user and design an algorithm to minimize the number of transactions to settle all debts, discussing trade-offs between optimality and simplicity.

Pro tip: Acknowledge that finding the absolute minimum number of transactions is NP-hard (related to the partition problem), so in practice, a greedy approach that matches largest creditors with largest debtors is often used, and mention that this is a common trade-off in real systems.

1. Clarify Requirements and Scope

Ask about functional and non-functional requirements: number of users, frequency of expenses, need for real-time updates, persistence, and whether the settlement algorithm must be optimal or just efficient.

2. Design Data Model

Define entities: User, Group, Expense (with payer, amount, participants, split method), and Balance. Consider how to store and update balances efficiently.

3. Compute Net Balances

For each user, calculate the net amount they owe or are owed by summing all expenses they paid and their shares. This reduces the problem to settling net balances.

4. Minimize Transactions

Use a greedy algorithm: repeatedly match the largest creditor with the largest debtor, settling the smaller amount, until all balances are zero. Discuss that this yields a minimal or near-minimal number of transactions.

5. Discuss Scalability and Trade-offs

Address how the solution scales with many users and expenses, potential optimizations (e.g., using heaps), and trade-offs between optimality and performance. Mention alternative approaches like graph-based or linear programming if needed.

Key Points to Mention

Net balance calculation: sum of amounts paid minus sum of shares owed.
Greedy algorithm: sort creditors and debtors, match largest amounts to minimize transactions.
Complexity: O(n log n) for sorting, where n is number of users with non-zero balance.
NP-hardness of optimal settlement: exact minimization is NP-hard, so greedy is a practical approximation.
Data model considerations: support for different split types (equal, exact, percentage) and multiple currencies.
Scalability: use of efficient data structures (heaps) and potential for batch processing.

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