HubSpot·Software Engineer·Onsite - System Design / Architecture·Senior

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Jun 2026Remote

Summary

HubSpot system design round for a software engineering role, and they came in with a pretty involved in-memory banking service problem that had way more moving parts than I expected for a single session.

Questions Asked (1)

Design an in-memory banking service supporting account creation, deposits, transfers, scheduled payments, a top-K accounts by total outgoing view, and account merging. Walk through data structures, method signatures, time/space complexities, and edge cases.

System DesignAlgorithms & Data StructuresData Modeling

Author's notes

This thing had six sub-operations and I kind of panicked when I saw the full list.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints (e.g., single-threaded vs concurrent, persistence, scale). Then design a core Account class with balance and transaction history, and choose data structures for each operation: hash map for account lookup, balanced BST or heap for top-K, and a scheduler for payments. Walk through method signatures, complexities, and edge cases like insufficient funds, duplicate accounts, and merge conflicts.

Pro tip: Explicitly discuss trade-offs between different data structures (e.g., heap vs balanced BST for top-K) and mention how you'd handle concurrency if needed, showing you think beyond the happy path.

1. Clarify Requirements and Constraints

Ask about expected scale, concurrency, persistence, and whether operations need to be atomic. Confirm the definition of 'total outgoing' and how scheduled payments affect it.

2. Design Core Data Model

Define an Account class with ID, balance, and transaction history. Use a hash map (accountId -> Account) for O(1) lookup. Discuss how to track outgoing totals efficiently.

3. Implement Operations and Choose Data Structures

For deposits/transfers, update balances and outgoing totals. For top-K, consider a min-heap of size K or a balanced BST (e.g., TreeMap) for O(log n) updates. For scheduled payments, use a priority queue keyed by execution time.

4. Analyze Time and Space Complexity

State complexities for each operation: account creation O(1), deposit/transfer O(1) or O(log n) if updating top-K structure, top-K query O(K log n) or O(1) if maintained, scheduled payment O(log n) insertion.

5. Handle Edge Cases and Merging

Address insufficient funds, invalid accounts, duplicate IDs, and concurrent access. For merging, combine balances and transaction histories, update the top-K structure, and handle scheduled payments from both accounts.

Key Points to Mention

Use a hash map for O(1) account lookup and a separate structure (min-heap or balanced BST) for top-K outgoing accounts.
Maintain a running total of outgoing amounts per account to avoid recomputing from transaction history.
For scheduled payments, use a priority queue (min-heap by execution time) and process due payments lazily or with a background thread.
When merging accounts, transfer all balances and scheduled payments, update the top-K structure, and mark the merged account as inactive.
Discuss concurrency: use locks per account or a global lock, and consider atomic operations for transfers.
Mention trade-offs: heap gives O(1) top-K query but O(log n) updates; balanced BST gives O(log n) for both but more overhead.

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