SoFi·Software Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

SoFi software engineer interview that was basically one long design problem about a key-value store. More depth than I expected for what sounded like a simple data structure question at first glance.

Questions Asked (2)

Design and implement an in-memory key-value store with add(key, value), get(key), remove(key), and getLast() operations, where getLast() returns the most recently added entry that still exists. get() should not affect what counts as 'last'. All operations should run in O(1) average time. Cover edge cases, justify your data structure choices, provide pseudocode, and analyze complexity.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I went straight to a hashmap for the key-value part and a stack or queue for tracking order, which was wrong because remove() in the middle breaks everything.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then propose a combination of a hash map for O(1) key access and a doubly linked list to maintain insertion order. Explain how getLast() can be O(1) by tracking the tail of the list, and ensure remove updates both structures. Walk through pseudocode and analyze time/space complexity.

Pro tip: Mention that get() should not affect the order, so it's a pure lookup without list manipulation. Also, highlight that using a doubly linked list allows O(1) removal when given a node reference, which is crucial for remove(key).

1. Clarify Requirements and Edge Cases

Ask about expected behavior for duplicate keys, removing non-existent keys, and getLast() on an empty store. Confirm that get() should not change the 'last' entry.

2. Choose Data Structures

Propose a hash map (dictionary) for O(1) key lookup and a doubly linked list to maintain insertion order. Explain that the hash map stores key -> node, and the list nodes store key-value pairs.

3. Design Operations

Detail how add inserts at the tail (most recent), get retrieves from the map without list changes, remove deletes from both map and list, and getLast returns the tail's value.

4. Provide Pseudocode

Write clear pseudocode for each operation, including edge cases like empty list or missing key. Show how to update pointers in the doubly linked list.

5. Analyze Complexity and Trade-offs

State that all operations are O(1) average time due to hash map and constant-time list operations. Discuss space complexity O(n) and potential trade-offs (e.g., memory overhead of pointers).

Key Points to Mention

Use a hash map for O(1) key access and a doubly linked list for O(1) insertion/removal and order tracking.
getLast() returns the tail of the doubly linked list, which is the most recently added entry still present.
get() does not modify the list order, so it remains O(1) and does not affect 'last'.
remove(key) must delete the node from the list and the entry from the map, updating adjacent pointers.
Handle edge cases: empty store, removing non-existent key, duplicate keys (update value and move to tail).
All operations are O(1) average time; space is O(n). Mention that worst-case for hash map is O(n) but average is O(1).

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

How would you handle concurrent adds and removes in this key-value store, and what locking strategy would you use?

System DesignTechnical Trade-offs

Author's notes

Thrown in at the end, felt like a bonus round.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: expected read/write ratio, consistency needs, and scale. Then propose a locking strategy that balances correctness and performance, such as fine-grained locking with read-write locks or lock striping, and discuss trade-offs like contention and deadlock risk. Finally, mention alternatives like optimistic concurrency control or lock-free data structures if appropriate.

Pro tip: Demonstrate awareness of real-world constraints by mentioning that the choice depends on the specific use case—e.g., SoFi's financial systems might prioritize strong consistency over raw throughput. Also, proactively discuss how you'd test and monitor the locking strategy under load.

1. Clarify requirements and constraints

Ask about read/write ratio, latency requirements, consistency guarantees, and scale. This shows you don't jump to solutions without understanding the problem.

2. Identify concurrency challenges

Explain the risks of concurrent adds and removes: race conditions, lost updates, deadlocks, and inconsistent reads. Mention that naive global locking hurts scalability.

3. Propose a locking strategy

Describe a specific approach, such as fine-grained locking per key or bucket, read-write locks for read-heavy workloads, or lock striping to reduce contention. Justify why it fits the requirements.

4. Discuss trade-offs and alternatives

Compare your chosen strategy with alternatives like optimistic concurrency control (e.g., CAS), MVCC, or lock-free structures. Highlight trade-offs in complexity, performance, and consistency.

5. Address edge cases and testing

Mention how you'd handle deadlocks (e.g., lock ordering), ensure atomicity, and test under concurrent load. This shows production readiness.

Key Points to Mention

Read-write locks vs. mutexes: read-write locks allow concurrent reads, improving throughput for read-heavy workloads.
Lock striping: partitioning the key space into stripes, each with its own lock, reduces contention while keeping memory overhead low.
Optimistic concurrency control: using version numbers or CAS operations to avoid locks, but may require retries under high contention.
Deadlock prevention: establish a global lock ordering or use timeouts to avoid deadlocks when multiple locks are acquired.
Performance considerations: measure contention and adjust granularity; consider lock-free data structures like ConcurrentHashMap for certain operations.
Consistency guarantees: discuss whether the store needs linearizability or if eventual consistency is acceptable, as it affects locking choices.

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