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Google·Software Engineer·Onsite - Coding / Algorithms·Senior

SeniorPrefer not to say
Jun 2026

Summary

Google SWE coding round, one problem, classic hard-ish LeetCode territory. They wanted the full O(n log n) treatment and weren't satisfied with just getting a working answer.

Questions Asked (1)

Q1

Given an integer array, return a new array where each element is the count of numbers to the right of the original element that are smaller than it.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

I knew this problem but blanked on which data structure to lead with.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and constraints, then discuss both brute-force and optimized solutions. Focus on explaining a Fenwick tree or merge sort based approach that achieves O(n log n) time, and analyze trade-offs between time and space.

Pro tip: Mention that this is the classic 'count of smaller numbers after self' problem and that Google interviewers often expect you to derive the Fenwick tree solution from scratch, so practice explaining the intuition behind the data structure.

1. Clarify the problem

Ask about input size, range of integers, and whether duplicates count as smaller. Confirm that the output should be an array of the same length.

2. Discuss brute-force approach

Explain a simple O(n^2) solution using nested loops to establish a baseline and show understanding of the problem.

3. Propose optimized solution

Describe an O(n log n) approach using a Fenwick tree (BIT) or merge sort, explaining how to process elements from right to left and query counts efficiently.

4. Analyze trade-offs

Compare time and space complexity of different approaches, and discuss when a simpler solution might be acceptable based on constraints.

5. Code and test

Write clean code for the chosen approach, handle edge cases, and walk through a small example to verify correctness.

Key Points to Mention

  • Time complexity: O(n^2) vs O(n log n)
  • Space complexity: O(n) for the output and auxiliary data structures
  • Fenwick tree (Binary Indexed Tree) for prefix sums
  • Coordinate compression to handle large integer ranges
  • Merge sort with index tracking as an alternative
  • Handling duplicates correctly (strictly smaller)

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