Airbnb·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Airbnb software engineer interview with a meaty coding problem around refund allocation logic. The question had enough edge cases to keep you busy for a while, and the complexity analysis at the end felt like a separate mini-interview on its own.

Questions Asked (1)

Implement a refund allocation function that takes a list of payments (each with a unique ID, payment method from a fixed set, an ISO-8601 date, and an amount paid), a list of existing refunds linked to those payments, and a new refund request amount. Return a list of allocations specifying how much to refund from each payment, following strict rules: exhaust one payment fully before moving to the next, prioritize methods in a fixed order (store credit first, then credit card, then PayPal), within the same method refund the most recent payments first, cap each payment's refundable amount at what's left after prior refunds, and if the request exceeds what's available return both the partial allocations and the shortfall. Also explain your data structures, grouping and sorting strategy, and analyze time complexity.

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

This one took me a minute to even parse correctly.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the rules and edge cases, then outline a step-by-step algorithm that groups payments by method, sorts within each group by date descending, and allocates the refund while tracking remaining amounts. Finally, analyze the time complexity and discuss potential optimizations or trade-offs.

Pro tip: Demonstrate maturity by proactively discussing how to handle floating-point precision (e.g., using integers for cents) and by mentioning that the algorithm should be idempotent and safe for concurrent refunds.

1. Clarify Requirements and Edge Cases

Ask questions to confirm the method priority order, date sorting (most recent first), and how to handle partial refunds and shortfalls. Also clarify if payments can be refunded multiple times and if refunds are linked to specific payments.

2. Design Data Structures

Choose appropriate data structures: a map from payment ID to remaining refundable amount, and a map from method to a list of payments (or a priority queue) sorted by date descending. This allows efficient lookup and allocation.

3. Outline Allocation Algorithm

Iterate through methods in priority order. For each method, iterate through payments from most recent to oldest, allocating as much as possible from each until the refund amount is exhausted. Track allocations and any remaining shortfall.

4. Analyze Time and Space Complexity

State that the time complexity is O(P log P) due to sorting payments within each method, where P is the number of payments. Space complexity is O(P) for storing remaining amounts and allocations.

5. Discuss Trade-offs and Optimizations

Mention potential optimizations like using a heap for each method to avoid full sorting if only a few refunds are needed, or precomputing remaining amounts. Also discuss handling of concurrent refunds and idempotency.

Key Points to Mention

Grouping payments by method and sorting within each group by date descending to satisfy the 'most recent first' rule.
Using a map to track remaining refundable amount per payment, initialized from payment amount minus sum of existing refunds.
Iterating methods in the fixed priority order: store credit, credit card, PayPal.
Exhausting one payment fully before moving to the next within the same method.
Returning both partial allocations and the shortfall if the refund request exceeds available amount.
Time complexity analysis: O(P log P) due to sorting, and space complexity O(P).

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