My Google and Amazon Interview Journey: 2-3 Months of Focused Preparation
My journey preparing for and interviewing at Google and Amazon - lessons learned, resources used, and tips for success
On May 2, I got an interview email from Google. I felt excited, but also pressured(phati padhi thi i thought its just prank call lol).
Instead of jumping between random resources, I made one clear decision:
Give 2-3 months of focused effort and trust a repeatable system.
What changed everything
I stopped optimizing for "number of problems solved" and started optimizing for "quality of understanding." That single shift changed my prep.
What you will get from this post
- The exact prep structure I followed with a full-time job
- Where I lost time in interviews and what that taught me
- A practical framework you can reuse for your own prep
Starting point
I had solved around 75 easy LeetCode problems. Basics were okay, but my depth in trees, graphs, and optimization was limited.
The Shift: From Random Practice to Structured Learning
During this phase, I reached out to Ravi Lamkoti, whose guidance helped me create a proper roadmap instead of solving blindly.
Resources that gave me the most clarity
- Striver's SDE Sheet & Playlists — Progression from basics to advanced patterns
- Google Preparation Sheet — Expected interview style and problem difficulty
Daily routine
- Morning (~8 AM): solve 2 focused problems
- Evening (after work): solve 3-4 medium or hard attempts
- Late night: review editorials and optimize solutions
- Weekends: deep sessions (10-15 problems/day)
Weekly review system
- Topics covered + weak areas
- Patterns I still could not solve quickly
- Revisit list (every 2 weeks)
- Mock interviews completed
Interview Day: What Happened
The interview itself was smooth and conversational. The interviewer encouraged me to articulate my thought process clearly and reason through the problem.
The questions, however, were not direct LeetCode-style prompts. Instead, they were framed as real-world scenarios. The expected approach was:
- Understand. Deeply understand the problem requirements
- Define. Clearly define the expected output
- Design. Design a clean approach or class structure
- Brute-force. Start with a brute-force solution
- Optimize. Gradually optimize step by step
I successfully implemented a correct brute-force solution and explained my reasoning well. However, I couldn't fully arrive at the optimal solution within the given time.
HR feedback was positive on understanding and communication. The gap was optimization instinct — knowing when to stop validating and start optimizing.
What helped me move forward
I stopped framing the result as pass/fail. I treated it as signal. The signal made my next training steps obvious.
Key Lessons and Takeaways
This journey reinforced several principles that I continue to follow:
- Read every problem multiple times. Most mistakes come from misinterpreting the question.
- Think deeply about edge cases. Trying to break your own solution often reveals the correct approach.
- Revisit problems consistently. I created a dedicated Discord channel to track problems and revisit them every two weeks.
- Follow a clear progression. Blind 75 → Striver's Sheet → Deep dives into recursion, DP, trees, and graphs. Random practice creates movement — structured practice creates growth.
- Use paper for recursion and trees. Draw the recursion tree first, then debug in the editor. It builds real instinct.
- Prioritize depth over speed. Depth beats volume. Always.
- Consistency is a skill. Even on low-energy days, review beats skipping.
- Complexity reflects understanding. If time and space complexity are clear, your solution quality improves.
Amazon Interview Experience
Behavioral rounds matter as much as coding rounds.
I underprepared behavioral rounds compared to DSA, and that cost me.
What I now prioritize:
- STAR method fluency. Structure every answer with Situation, Task, Action, Result.
- 2–3 strong stories with measurable impact. Concrete numbers and outcomes matter more than vague descriptions.
- Clear decision-making under ambiguity. Show how you navigate trade-offs when there is no obvious right answer.
Coding opens doors. Behavioral depth helps close loops.
If I Restarted Today
Week 1 reset plan
- Day 1-2: map weak areas and define strict scope
- Day 3-4: solve + review with written takeaways
- Day 5: run one mock interview and document mistakes
- Day 6-7: revisit incorrect problems and optimize
I may not have converted every opportunity yet. But the growth from that phase is compounding, and this journey is still on.