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Circle·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Apr 2026Remote

Summary

Circle SWE interview had me building out a banking system in stages, each level stacking on top of the last. Pretty well-designed problem structure, I'll give them that.

Questions Asked (4)

Q1

Implement basic banking account operations: creating accounts (rejecting duplicates), depositing funds, and processing payments while tracking each account's cumulative outgoing total.

Algorithms & Data StructuresData Modeling
Author's notes

The duplicate check tripped me up for a second.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and edge cases, then design a data model that efficiently supports account creation, deposits, and payments while tracking cumulative outgoing totals. Implement the solution using appropriate data structures, ensuring operations are correct and efficient, and discuss trade-offs.

Pro tip: Emphasize idempotency and error handling for payment operations, as financial systems require robustness against duplicate requests and failures. Also, consider thread-safety if the system is concurrent.

1. Clarify Requirements and Edge Cases

Ask questions to understand constraints: Are account IDs unique? What happens on insufficient funds? Should deposits be idempotent? Are there concurrency concerns?

2. Design Data Model

Choose a data structure (e.g., hash map) to store accounts, mapping account ID to account object containing balance and cumulative outgoing total. Consider using a database for persistence if needed.

3. Implement Core Operations

Write methods for createAccount (check duplicate), deposit (update balance), and processPayment (check balance, update balance and outgoing total). Ensure atomicity if concurrent.

4. Handle Errors and Edge Cases

Implement validation for negative amounts, non-existent accounts, insufficient funds, and duplicate account creation. Return appropriate errors or exceptions.

5. Test and Optimize

Write unit tests covering normal and edge cases. Discuss time/space complexity and potential optimizations (e.g., locking, transactions).

Key Points to Mention

  • Use a hash map for O(1) average-time account lookups.
  • Track cumulative outgoing total separately from balance to avoid recomputation.
  • Ensure payment operations are atomic to prevent race conditions.
  • Validate inputs (e.g., positive amounts, existing accounts) and handle errors gracefully.
  • Consider idempotency for payment requests to avoid double-charging.
  • Discuss trade-offs between in-memory and persistent storage.

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

Q2

Given the account operations above, implement a TOP_SPENDERS query that returns the top N accounts by total outgoing payments, with ties broken alphabetically by account ID.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

Sorting on demand vs maintaining a sorted structure live.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the schema and requirements, then outline a two-phase approach: aggregate outgoing payments per account, then sort by total descending and account ID ascending, and finally limit to N. Discuss trade-offs between in-memory sorting and using a heap for large datasets, and mention indexing strategies for efficiency.

Pro tip: Explicitly state your assumptions about the data (e.g., payment amounts are positive, account IDs are strings) and mention that you would validate them with the interviewer before writing code. This shows attention to detail and collaborative problem-solving.

1. Clarify requirements and schema

Ask about the table structure, data types, and edge cases (e.g., null amounts, negative payments, accounts with no outgoing payments). Confirm the definition of 'outgoing payment' and whether N is a parameter.

2. Design the aggregation

Group by account ID and sum the outgoing payment amounts. Consider using a hash map or SQL GROUP BY, and discuss handling large datasets with partitioning or streaming.

3. Implement sorting and tie-breaking

Sort the aggregated results by total descending, and for ties, by account ID ascending. Explain that a stable sort or a custom comparator can achieve this.

4. Optimize for top N

If N is small relative to the number of accounts, use a min-heap of size N to avoid sorting all accounts. Discuss time and space complexity trade-offs.

5. Discuss scalability and indexing

Mention that an index on (account_id, amount) or a materialized view can speed up aggregation. For distributed systems, consider partitioning by account ID and using map-reduce style aggregation.

Key Points to Mention

  • Time and space complexity: O(A log A) for sorting all accounts vs O(A log N) with a heap, where A is number of accounts.
  • Tie-breaking: ensure deterministic ordering by account ID when totals are equal.
  • Handling large datasets: streaming aggregation, partitioning, or using a database with appropriate indexes.
  • Edge cases: accounts with zero outgoing payments, negative amounts (refunds), and N larger than the number of accounts.
  • SQL vs. procedural code: show awareness of both and when to use each (e.g., SQL for simplicity, code for complex logic).
  • Testing: suggest unit tests for tie-breaking, empty results, and performance benchmarks.

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

Q3

Extend the system with a transfer mechanism where funds are deducted from the source immediately but only credited to the target upon explicit acceptance, and transfers expire if not accepted within 24 hours.

System DesignData ModelingTechnical Trade-offs
Author's notes

This is where things got interesting and also where I slowed down noticeably.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints, then propose a data model that tracks transfer states and balances. Discuss the trade-offs of immediate deduction vs. delayed credit, and outline the system components for handling acceptance, expiration, and consistency.

Pro tip: Emphasize idempotency and exactly-once processing to prevent double-spending or duplicate credits, and discuss how to handle race conditions between acceptance and expiration.

1. Clarify Requirements

Ask questions to understand the scope: Is this for a single currency? What are the consistency guarantees? How should failures be handled? What are the notification requirements?

2. Design Data Model

Propose a schema for transfers with states (PENDING, ACCEPTED, EXPIRED, CANCELLED) and timestamps. Include fields for source, target, amount, and expiration time.

3. Outline System Components

Describe services: Transfer Service to initiate and manage transfers, Balance Service to handle deductions/credits, and a Scheduler for expiration. Consider using a message queue for asynchronous processing.

4. Handle Concurrency and Consistency

Discuss locking mechanisms (e.g., optimistic or pessimistic) to prevent race conditions. Ensure atomic operations for deduction and credit, and use idempotency keys to avoid duplicates.

5. Address Expiration and Notifications

Explain how to implement a timeout mechanism (e.g., scheduled jobs, TTL in database) and how to notify users of pending transfers and expirations.

Key Points to Mention

  • Idempotency of transfer operations to prevent double-spending
  • Use of a state machine to manage transfer lifecycle
  • Consistency models (ACID vs. BASE) and their trade-offs
  • Handling of race conditions between acceptance and expiration
  • Scalability considerations for the expiration scheduler
  • Auditability and logging for financial transactions

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

Q4

Implement ACCEPT_TRANSFER: validate that the accepting account is the intended target, check the transfer hasn't expired, and credit the funds if valid.

Algorithms & Data StructuresAPI & Integrations
Author's notes

Straightforward once the transfer registry was solid.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and assumptions, such as the data model for transfers and accounts, and the expected behavior for invalid cases. Then outline a step-by-step algorithm that validates the accepting account, checks expiration, and updates balances atomically. Finally, discuss edge cases, error handling, and how to ensure idempotency and concurrency safety.

Pro tip: Emphasize idempotency and atomicity: use a unique transfer ID and database transactions with row-level locks to prevent double-crediting and race conditions. This shows you think about production reliability, not just the happy path.

1. Clarify requirements and assumptions

Ask about the data model (e.g., transfer has targetAccountId, expiration timestamp, status), expected error responses, and whether the operation must be idempotent. Confirm if the accepting account is provided as input and how to identify it.

2. Design validation logic

Outline checks: verify the accepting account matches the transfer's intended target, ensure the transfer hasn't expired (compare current time with expiration), and confirm the transfer is in a valid state (e.g., not already accepted or cancelled).

3. Implement atomic credit operation

Describe using a database transaction to atomically update the transfer status and credit the account balance. Use row-level locking or optimistic concurrency control to prevent race conditions.

4. Handle errors and edge cases

Define error responses for invalid account, expired transfer, already accepted transfer, and insufficient funds (if applicable). Discuss idempotency: if the same accept request is retried, return the same result without double-crediting.

5. Discuss testing and monitoring

Mention unit tests for each validation branch, integration tests for concurrency, and logging/metrics for failed attempts. Suggest adding alerts for unusual patterns like repeated expired transfer attempts.

Key Points to Mention

  • Idempotency: use a unique transfer ID and ensure repeated accept calls don't double-credit.
  • Atomicity: perform validation and balance update in a single database transaction.
  • Concurrency control: use row-level locks or optimistic locking to prevent race conditions.
  • Expiration check: compare current timestamp with transfer's expiration, considering time zones.
  • Error handling: return appropriate HTTP status codes (e.g., 400, 403, 409, 410) and clear error messages.
  • Security: ensure the accepting account is authorized to accept the transfer (e.g., authenticated user owns the account).

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