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Circle·Software Engineer·Technical Phone Screen·Senior

Senior
Apr 2026

Summary

Circle SWE interview that went pretty deep on a banking system design problem. The bulk of it was extending a multi-level transaction system to support historical balance queries, which sounds straightforward until you're actually in it.

Questions Asked (1)

Q1

Extend a banking system to support querying an account's balance at a specific past timestamp. How would you design the GET_BALANCE_AT API, and what data structures would you use?

System DesignAlgorithms & Data StructuresAPI & Integrations
Author's notes

My first instinct was to just replay all transactions up to the timestamp on every call, which works but is obviously slow.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements such as consistency, latency, and data volume, then propose an append-only ledger with timestamped entries. For efficient queries, suggest a time-series data structure like a segment tree or balanced BST, and discuss trade-offs between in-memory and persistent storage.

Pro tip: Mention that you would use a combination of a write-ahead log for durability and an in-memory index for fast queries, and highlight the importance of handling concurrent writes and reads with appropriate locking or MVCC.

1. Clarify Requirements

Ask about expected query patterns, data retention, consistency needs, and performance SLAs to scope the design appropriately.

2. Design Data Model

Propose an append-only ledger where each transaction is stored with a timestamp, and consider using a time-series database or a custom index.

3. Choose Data Structures

Select a data structure that supports efficient range queries and point lookups, such as a segment tree, Fenwick tree, or balanced BST, and explain how it enables O(log n) queries.

4. Define API Contract

Specify the GET_BALANCE_AT endpoint with parameters like account ID and timestamp, and define the response format including balance and currency.

5. Address Scalability and Consistency

Discuss partitioning, replication, and consistency models (e.g., eventual vs. strong) to ensure the system scales and meets correctness requirements.

Key Points to Mention

  • Append-only ledger for immutable transaction history
  • Time-series indexing (e.g., segment tree, Fenwick tree) for efficient range queries
  • Trade-offs between in-memory and persistent storage
  • Handling concurrent writes and reads (locking, MVCC)
  • API design considerations: idempotency, pagination, error handling
  • Scalability via sharding by account ID and replication for fault tolerance

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