Technical interview experience 1
Candidate Profile: B.Tech CS, 8.5 CGPA, 2 internships, ACM ICPC participant, open source contributor
Round 1 - Resume Screening (1-2 weeks)
- Applied through Google Careers portal (off-campus)
- Strong resume with competitive programming achievements (ACM ICPC regional qualifier)
- Multiple internships at product companies
- Active GitHub profile with open source contributions
- Tip: Google values competitive programming, internships, and open source contributions. Highlight these prominently in your resume.
Round 2 - Online Assessment (90 minutes)
- Platform: Google’s internal platform
- Coding Problem 1: Find the longest palindromic substring in a string (medium difficulty)
- Used dynamic programming approach with O(n²) time complexity
- Handled edge cases (single character, empty string)
- Coding Problem 2: Design a data structure for efficient range sum queries (hard difficulty)
- Implemented segment tree with O(log n) query time
- Discussed time/space complexity trade-offs
- MCQs: 18 questions on CS fundamentals
- Time complexity analysis, graph algorithms (Dijkstra, BFS/DFS)
- System design basics (scalability, load balancing, caching)
- Database concepts (ACID properties, indexing)
- Result: Cleared both coding problems with optimal solutions, scored well in MCQs, advanced to interviews
- Tip: Both coding problems must be solved optimally (O(n) or O(n log n)) to advance. Practice Google-tagged LeetCode problems.
Round 3 - Technical Interview 1 (60 minutes)
- Interviewer: Senior Software Engineer (L5)
- Format: Google Docs for coding (no IDE, no compiler)
- Question 1: Implement a rate limiter using sliding window algorithm
- Discussed multiple approaches (fixed window, sliding window, token bucket)
- Implemented sliding window with optimal time complexity
- Handled edge cases and concurrency considerations
- Question 2: Design a distributed cache system (basic system design)
- Discussed requirements (capacity, eviction policy, consistency)
- Designed architecture with load balancer, cache servers, database
- Discussed trade-offs (consistency vs availability, cache invalidation)
- Discussion: Detailed discussion into my internship project on distributed systems
- Explained architecture, scalability challenges, and solutions
- Discussed technologies used (Redis, Kafka, microservices)
- Result: Strong performance, excellent problem-solving approach, advanced to next round
- Tip: Practice coding on Google Docs. Google evaluates problem-solving approach, not just correct code. Think out loud.
Round 4 - Technical Interview 2 (60 minutes)
- Interviewer: Staff Engineer (L6)
- Question 1: Find the median of two sorted arrays (hard, O(log(min(m,n))) solution)
- Discussed multiple approaches (merge and find, binary search)
- Implemented optimal binary search solution
- Handled edge cases (empty arrays, single element arrays)
- Question 2: Design a URL shortener system (system design)
- Discussed requirements (scale, character set, collision handling)
- Designed with hash function, database, cache layer
- Discussed scalability (billions of URLs), database sharding, CDN
- Project Discussion: Detailed discussion on my open source contributions
- Explained technical challenges and solutions
- Discussed impact and learnings
- Result: Excellent problem-solving approach, strong system design fundamentals, advanced to behavioral round
- Tip: Google values system design basics even for L3 roles. Practice designing popular systems (URL shortener, chat app, distributed cache).
Round 5 - Behavioral Interview / Googleyness (45 minutes)
- Interviewer: Engineering Manager
- Questions Asked:
- “Tell me about a time you led a technical project”
- “Describe a situation where you had to learn a new technology quickly”
- “How do you handle ambiguity in technical problems?”
- “Why do you want to join Google?”
- “Tell me about a time you failed and what you learned”
- My Approach:
- Used STAR method (Situation, Task, Action, Result) for all answers
- Discussed my open source project where I led a team of 3 contributors
- Explained how I learned React in 2 weeks for an internship project
- Emphasized alignment with Google’s values (innovation, impact, learning)
- Shared a failure story about a hackathon project and lessons learned
- Googleyness Assessment:
- Demonstrated leadership through open source project
- Showed learning agility with quick technology adoption
- Handled ambiguity by breaking down complex problems
- Aligned with Google’s mission and values
- Result: Strong cultural fit assessment, advanced to final committee review
- Tip: Research Google’s culture and values. Prepare 8-10 STAR stories covering leadership, problem-solving, learning, and failures.
Round 6 - Final Committee Review (1 week)
- All interview feedback compiled and reviewed by hiring committee
- Background verification completed
- Reference checks conducted
- Final Result: Selected for Software Engineer L3 role
- Package: ₹35 LPA (total compensation: base ₹22 LPA + stock + bonus)
- Location: Google Hyderabad office
Key Takeaways: Practice coding on Google Docs, focus on optimal solutions (O(n) or O(n log n)), prepare for system design basics, use STAR method for behavioral questions, and demonstrate Googleyness (leadership, learning agility, cultural fit). Practice with Google placement papers to understand the question patterns.