Meta·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026Remote

Summary

Meta SWE interview with a pretty meaty coding/design hybrid question around a payment tracking system. One question but it had a lot of layers and I felt like I was peeling an onion the whole time.

Questions Asked (1)

Design and implement a function that returns the top N accounts by total outgoing payments, combining both immediate and scheduled payments. The function should support efficient online updates after every payment, and you should explain tie-breaking behavior, complexity, and how account deletions or merges would affect the ranking.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I got the basic heap idea out pretty fast but then they kept pulling on threads.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: define 'top N', tie-breaking rules, and update frequency. Then propose a data structure like a balanced BST or heap with lazy deletion to maintain rankings efficiently, and discuss how to handle deletions/merges. Finally, analyze time/space complexity and trade-offs.

Pro tip: Demonstrate awareness of real-world constraints by mentioning that scheduled payments may need to be processed as they occur, and that account merges require careful handling of aggregated totals and ranking updates.

1. Clarify Requirements and Assumptions

Ask about N's typical size, update frequency, tie-breaking rules, and whether deletions/merges are common. Confirm that 'total outgoing payments' includes both immediate and scheduled payments that have been executed.

2. Choose Data Structures

Propose a data structure that supports efficient updates and top-N queries, such as a balanced binary search tree (e.g., order-statistic tree) or a combination of a hash map for account totals and a heap for ranking. Discuss lazy deletion for handling deletions/merges.

3. Design Operations

Detail how to implement update (add payment), getTopN, delete account, and merge accounts. For updates, adjust the account's total and update its position in the ranking structure. For merges, combine totals and remove the merged account.

4. Analyze Complexity and Trade-offs

Provide time and space complexity for each operation. Compare alternatives (e.g., heap vs. BST) and justify your choice based on expected workload. Mention that getTopN is O(N) or O(N log N) depending on structure.

5. Address Edge Cases and Scalability

Discuss tie-breaking (e.g., by account ID), handling of scheduled payments (e.g., process when due), and scalability concerns like sharding or distributed updates if the system grows.

Key Points to Mention

Tie-breaking: define a deterministic rule, e.g., lexicographic order of account IDs.
Complexity: update O(log M) with BST, getTopN O(N log M) or O(N) with heap, where M is number of accounts.
Deletions: use lazy deletion or remove from structure; ensure ranking remains correct.
Merges: combine totals, remove one account, and update ranking; consider cascading effects.
Scheduled payments: treat as updates when they occur; may need a scheduler or event queue.
Scalability: consider distributed systems, sharding by account ID, and eventual consistency for top-N.

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