Lead Bank·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Coding round at Lead Bank for a software engineer role. One problem with a twist layered on top, which made it more interesting than a straight leetcode grind.

Questions Asked (1)

Implement a time-based key-value store that supports setting values with timestamps and retrieving the most recent value at or before a given timestamp. Then extend it to support returning a full snapshot of the store at a specific timestamp.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The base problem I'd seen before so binary search on the timestamps per key came to me pretty fast.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: operations (set, get, snapshot), timestamp semantics, and constraints. Then design a data structure that stores per-key timestamped values, using binary search for efficient retrieval. For snapshot, discuss trade-offs between copying on write vs. on read, and propose a versioned or copy-on-write approach.

Pro tip: Mention that timestamps are monotonically increasing for set operations, which allows appending to per-key lists and using binary search. Also, for snapshot, consider using a persistent data structure or versioning to avoid O(n) copies.

1. Clarify Requirements and Constraints

Ask about expected operations, timestamp uniqueness, concurrency, and memory limits. Confirm whether timestamps are strictly increasing for set calls and if snapshot should be a deep copy or a view.

2. Design Core Data Structure

Propose a hash map from key to a list of (timestamp, value) pairs, with timestamps sorted. For get, use binary search to find the latest timestamp <= given time.

3. Extend for Snapshot

Discuss approaches: (a) copy all current key-value pairs at snapshot time (O(n) space/time), (b) maintain a versioned store with copy-on-write, or (c) use a persistent balanced tree. Compare trade-offs in time/space complexity.

4. Analyze Complexity and Trade-offs

For each operation, state time and space complexity. For snapshot, compare eager vs. lazy copying, and discuss impact on read/write performance and memory.

5. Consider Optimizations and Edge Cases

Mention optimizations like pruning old timestamps if only recent queries matter, handling missing keys, and concurrency control (e.g., locking or MVCC).

Key Points to Mention

Use of binary search for O(log n) retrieval per key.
Timestamp monotonicity assumption and its implications.
Trade-offs between copying on write vs. on read for snapshots.
Persistent data structures (e.g., persistent treap) for efficient snapshots.
Handling of missing keys and timestamps before the earliest entry.
Concurrency considerations and potential use of MVCC.

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