Interview Experience - 153 - Google | Software Development Engineer | L3
Summary
📌 Job Role: Software Development Engineer
🔢 Number of Rounds: 4
📜 Offer Status: Rejected
📍 Location: Bangalore
👤 Candidate Name: Not disclosing due to signed NDA
Interview Process
The Google Virtual Onsite interview consisted of 4 rounds, each lasting 45 minutes. There were 3 technical rounds and 1 behavioral round. All rounds were scheduled on the same day. The overall coordination was smooth, with timely communication from the recruitment team. There were no delays between rounds, and the transition from one round to another was handled efficiently.
Preparation Guide
Google doesn't just want working code; it expects production-quality, bug-free code that follows conventions, is optimal, and scalable.
Time management is critical. With only 45 minutes per round, even a few extra minutes can make a difference. Keep introductions concise—just a crisp one-liner to save time for problem-solving.
Always think out loud during the interview. This is encouraged by interviewers and helps them understand your thought process.
Be prepared to discuss multiple approaches to a problem and justify your chosen solution with time and space complexity analysis.
Interview Rounds
Round 1: Technical Round 1
Duration: 45 minutes
Difficulty Level: Medium
Experience:
The interviewer was formal and to-the-point, expecting crisp answers. The problem was to design a temperature register that receives numerous temperature readings at any given time and displays the maximum temperature recorded in the last 24 hours. It needed to scale to handle millions of readings.
I proposed using a max heap where the root node always contains the maximum temperature. Since the heap would grow indefinitely, I suggested a cleanup module that periodically deletes readings older than 24 hours. The interviewer accepted the solution and then asked for the time complexity of each module.
He also suggested that the problem could be solved using a Binary Search Tree (BST). Later, I thought it could be handled using a Deque as well, depending on the scalability requirements.
Key Learnings:
Always consider multiple data structures for optimal design.
Be ready to analyze time complexity for every component of the solution.
Sometimes alternative solutions (BST, Deque) may be more efficient for certain constraints.
Round 2: Googleyness Round
Duration: 45 minutes
Difficulty Level: Easy
Experience:
This round focused on behavioral and situational questions. The interviewer asked about past projects, deadlines, conflicts, and instances of going beyond assigned responsibilities.
Questions I recall:
What is the most difficult project you have done so far and how did you handle it?
What are your learnings from the above?
Recall an incident when you had to meet a strict deadline and how you managed it.
Have you experienced a clash of perspectives with colleagues, and how did you resolve it?
Have you ever gone beyond your set responsibilities for the benefit of the team?
The conversation felt natural, and the interviewer seemed satisfied with my responses. We concluded about seven minutes early.
Key Learnings:
Have specific, structured examples ready for common behavioral questions.
Keep answers concise yet impactful to leave room for follow-up discussions.
Round 3: Technical Round 2
Duration: 45 minutes
Difficulty Level: Hard
Experience:
The problem involved an array representing building heights. You can jump to the next building if its height is less than or equal to the current one. If taller, you need to climb using either bricks (each brick = 1 height unit) or ropes (one rope can climb any height but is single-use). The goal was to reach the farthest possible building optimally.
Example:
heights = [4, 12, 2, 7, 3, 18, 20, 3, 19], bricks = 10, ropes = 2 Output: 7
I started with a recursive solution, optimized to Dynamic Programming (DP), and finally implemented a heap-based approach in O(n log n). I covered edge cases and performed dry runs. However, I exceeded the 45-minute limit by 4 minutes. Although the interviewer allowed me to finish, I suspect the time overrun affected my evaluation.
Key Learnings:
Time management is crucial, especially when multiple optimizations are needed.
Practice progressing from brute force to DP to optimal data structures under timed conditions.
Use heaps effectively when prioritizing resource allocation problems.
Round 4: Technical Round 3
Duration: 45 minutes
Difficulty Level: Hard
Experience:
The task was to determine if a given list of inequations was solvable. For example:
[(a<b)]→ true[(a<b), (b<a)]→ false[(c<d), (p<q), (f<k)]→ true
The challenge was that variable values could be anything, and ordering based on literal or ASCII values was disallowed. Initially, the problem statement was confusing, and I spent significant time understanding it. I eventually implemented a working solution, though it was not optimal.
At the end, the interviewer mentioned that this could be solved most efficiently using graph-based approaches—specifically forest structures and topological sort.
Key Learnings:
Quickly breaking down and clarifying unclear problem statements is essential.
Graph theory knowledge, especially topological sorting, is valuable for relationship-based constraints.
Forest structures can be a powerful alternative for dependency resolution problems.
Final Thoughts
Although I did not receive an offer, this experience reinforced several important takeaways:
At Google’s level, correctness alone is not enough—solutions must be optimal, clean, and production-ready.
Time discipline can be the deciding factor, even if the problem is solved completely.
Being able to explain multiple valid approaches to a problem shows depth of understanding.
Strong foundations in data structures, algorithms, and system design are essential, but so are communication skills and adaptability.