Circle·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Circle SWE interview, got a banking system design question that was more algorithmic than I expected. Pretty niche problem around tracking outgoing spend across accounts.

Questions Asked (1)

Implement a `topSpenders(timestamp, k)` function for a banking system that returns the top k accounts ranked by total outgoing spending. Outgoing transfers and payments count; incoming transfers and deposits do not. Sort descending by spend, break ties by account ID lexicographically. Return strings formatted like 'alice(1200)'. If fewer than k accounts have spent anything, just return all of them.

Algorithms & Data StructuresSystem Design

Author's notes

The tie-breaking tripped me up more than the core logic.

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AI HintsAI Generated

Suggested Approach

Clarify the event model and constraints first, then design a streaming aggregation that maintains per-account outgoing spend and supports efficient top-k queries. Use a hash map for account totals and a min-heap or balanced tree for top-k, handling ties by account ID and formatting output as specified.

Pro tip: Explicitly discuss how you'd handle late-arriving events and out-of-order timestamps, since banking systems often require windowing and correctness guarantees. Also, mention that you'd confirm whether the function is called repeatedly or once, as that affects whether to precompute or query on demand.

1. Clarify requirements and constraints

Ask about the event schema (fields for sender, receiver, amount, type), timestamp semantics (event time vs processing time), and whether the function is called once or repeatedly. Confirm tie-breaking rules and output format.

2. Design data structures for aggregation

Use a hash map to track total outgoing spend per account. For top-k, consider a min-heap of size k or a balanced BST if frequent updates and queries are needed. Discuss trade-offs.

3. Process events and maintain top-k

Iterate through events, filter outgoing transfers/payments, update the hash map, and adjust the top-k structure. Handle ties by comparing account IDs lexicographically.

4. Format and return results

Extract top-k accounts, sort descending by spend and ascending by account ID for ties, then format each as 'account(spend)'. If fewer than k accounts have spent, return all.

5. Analyze complexity and edge cases

Discuss time and space complexity, and address edge cases like zero spend, duplicate events, and large k. Mention scalability considerations for high-volume streams.

Key Points to Mention

Event schema and semantics: distinguishing outgoing vs incoming based on sender/receiver and transaction type.
Data structures: hash map for aggregation, min-heap or balanced tree for top-k with tie-breaking.
Time and space complexity: O(n log k) with heap, O(n) space, and potential optimizations.
Tie-breaking: lexicographic order by account ID when spends are equal.
Output formatting: exact string format 'account(spend)' and handling fewer than k accounts.
Scalability and streaming: handling out-of-order events, windowing, and incremental updates.

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