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

JuniorPending
Apr 2026

Summary

Went through two technical onsite rounds at Google for an early career campus role. First round left me second-guessing myself, second felt much smoother. Still waiting to hear back and genuinely not sure which way this goes.

Questions Asked (2)

Q1

Solve a coding problem by identifying the appropriate data structure and implementing it correctly.

Algorithms & Data Structures
Author's notes

I picked the right data structure and got the code mostly working on my own, which felt good.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and constraints, then discuss potential data structures and their trade-offs before coding. Implement the chosen solution cleanly, test with examples, and analyze time and space complexity.

Pro tip: Verbalize your thought process and consider edge cases early; Google values how you approach problems, not just the final code. If stuck, think aloud about simpler versions or related problems to show problem-solving skills.

1. Understand the Problem

Ask clarifying questions to confirm input/output formats, constraints, and edge cases. Restate the problem in your own words to ensure alignment.

2. Explore Data Structures

Brainstorm suitable data structures (e.g., arrays, hash maps, trees, graphs) and discuss their trade-offs in terms of time and space complexity.

3. Design and Implement

Choose the optimal data structure, outline the algorithm, and write clean, modular code. Explain your reasoning as you code.

4. Test and Validate

Walk through your code with sample inputs, including edge cases. Debug and fix any issues.

5. Analyze Complexity

State the time and space complexity of your solution and discuss potential optimizations or alternative approaches.

Key Points to Mention

  • Time and space complexity analysis of the chosen data structure and algorithm
  • Trade-offs between different data structures (e.g., hash map vs. tree for lookup)
  • Edge cases such as empty inputs, large inputs, and duplicates
  • Code readability and modularity
  • Testing methodology and validation with examples
  • Potential optimizations or alternative solutions

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

Q2

Solve a coding problem involving a non-trivial loop construct, explaining your approach as you go.

Algorithms & Data Structures
Author's notes

This one went way better.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem and edge cases, then outline a brute-force approach before optimizing. While coding, verbalize each step, especially the loop logic, and test with small examples to catch off-by-one errors.

Pro tip: Google values clean, efficient code and clear communication. Write pseudocode first to structure your thoughts, then translate to real code while explaining your reasoning.

1. Understand and Clarify

Ask clarifying questions to confirm input/output, constraints, and edge cases. Restate the problem in your own words to ensure alignment.

2. Discuss Approaches

Propose a brute-force solution and analyze its time/space complexity. Then suggest an optimized approach, explaining the trade-offs.

3. Code with Explanation

Write clean code while narrating your thought process, focusing on the loop construct and its invariants. Use meaningful variable names and modularize if helpful.

4. Test and Debug

Walk through your code with a small example, including edge cases. Identify and fix any bugs, especially those related to loop boundaries.

5. Analyze and Optimize

State the final time and space complexity. Discuss potential optimizations or alternative solutions if time permits.

Key Points to Mention

  • Time and space complexity analysis of the loop and overall algorithm
  • Edge cases such as empty input, single element, or maximum size
  • Loop invariants and termination conditions
  • Trade-offs between different approaches (e.g., iterative vs. recursive)
  • Code readability and maintainability
  • Testing methodology and debugging techniques

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