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Lowe's·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jun 2026

Summary

Lowe's technical phone screen for a software engineer role, one question but they really wanted you to go deep on it. Less of a 'write the code' situation and more of a 'explain everything about this problem' situation, which I wasn't totally prepared for.

Questions Asked (1)

Q1

Given a string, find the length and an example of the longest substring with no repeated characters. Walk through your algorithm, argue why it's correct, analyze time and space complexity, and cover edge cases like empty strings, all-identical characters, and Unicode.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

Sliding window, fine, I know that.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then present a sliding window algorithm using a hash map to track character indices. Walk through a concrete example to illustrate, argue correctness via invariants, and analyze time and space complexity.

Pro tip: Mention that the algorithm naturally handles Unicode if you treat characters as code points (e.g., using runes in Go or code points in Python) and discuss the trade-off between using a fixed-size array for ASCII versus a hash map for general Unicode.

1. Clarify requirements and edge cases

Ask whether the string can be empty, contain all identical characters, or include Unicode. Confirm that we need both the length and an example substring.

2. Outline the sliding window approach

Explain that we maintain a window [left, right) and expand right, updating the left boundary when a duplicate is found using a map from character to last index.

3. Walk through an example

Choose a string like 'abcabcbb' and trace the algorithm step by step, showing how the window and max length update.

4. Argue correctness

State the invariant: the window always contains no repeated characters, and we record the maximum length seen. Prove that any longer substring would have been considered.

5. Analyze complexity and edge cases

Time O(n) with a single pass, space O(min(n, m)) where m is the alphabet size. Discuss empty string (return 0), all identical (return 1), and Unicode handling.

Key Points to Mention

  • Sliding window technique with two pointers
  • Hash map to store last seen index of each character
  • Time complexity O(n) and space complexity O(min(n, m))
  • Correctness proof via loop invariant
  • Edge cases: empty string, all identical characters, Unicode
  • Trade-off between fixed-size array (ASCII) and hash map (Unicode)

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