Goldman Sachs·Product Manager·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026

Summary

Goldman Sachs PM interview that threw a classic string compression algorithm question at me, which felt a bit out of left field for a product role but apparently they do this. No behavioral fluff, just jumped straight into the technical stuff.

Questions Asked (1)

Design a string compression algorithm that replaces consecutive repeating characters with the character and its count (e.g. 'aaabbc' becomes 'a3b2c1'). Walk through both compression and decompression, handle edge cases like single-character runs or inputs where compression actually makes the string longer, and give a full time and space complexity analysis.

Algorithms & Data StructuresTechnical Trade-offsSystem Design

Author's notes

I started with the happy path, two pointers scanning through the string, count runs, append char plus count.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and assumptions (e.g., character set, case sensitivity). Then walk through the compression and decompression algorithms step-by-step, using a concrete example. Finally, analyze time and space complexity, and discuss edge cases and trade-offs, especially from a product perspective.

Pro tip: Emphasize that compression should only be used if it reduces size; otherwise, return the original string. This shows product thinking about practical utility and avoiding unnecessary complexity.

1. Clarify Requirements and Assumptions

Ask about the character set (ASCII vs Unicode), case sensitivity, and whether the input can be empty. Confirm that the output should be a new string and that we only compress if it shortens the string.

2. Design Compression Algorithm

Iterate through the string, counting consecutive identical characters. Append the character and its count to a result string. If the compressed string is not shorter than the original, return the original.

3. Design Decompression Algorithm

Parse the compressed string by reading a character followed by its count (which may be multiple digits). Repeat the character count times and append to the result.

4. Analyze Complexity and Edge Cases

Time complexity is O(n) for both compression and decompression, where n is the length of the input string. Space complexity is O(n) for the output. Discuss edge cases: empty string, single character, all unique characters, runs longer than 9 (multi-digit counts), and compression making the string longer.

5. Discuss Trade-offs and Product Considerations

Consider when compression is beneficial (e.g., strings with many repeats) and when it's not. Mention alternative approaches like in-place compression if memory is constrained, and the importance of choosing the right algorithm based on use case.

Key Points to Mention

Time complexity: O(n) for both compression and decompression, as each character is processed once.
Space complexity: O(n) for the output string; can be O(1) extra space if modifying in-place (but not typical for strings).
Edge cases: empty string, single-character runs, multi-digit counts (e.g., 'aaaaaaaaaaaa' -> 'a12'), and compression not reducing size.
Decompression must handle multi-digit counts correctly (e.g., 'a12' -> 12 'a's).
Trade-off: compression only useful if it reduces size; otherwise return original.
Product perspective: consider use cases like data transmission or storage where compression matters, and the importance of clear requirements.

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