Google

SWE II

Software EngineerL3Medium

This interview process is for a Software Engineer (SWE II, L3) position at Google. It is designed to assess a candidate's technical skills, problem-solving abilities, and cultural fit within Google's engineering environment.

Rounds

3

Timeline

~21 days

Experience

2 - 5 yrs

Salary Range

US$110000 - US$150000

Total Duration

135 min

Overall Evaluation Criteria

Technical Skills

Problem-solving approach
Algorithmic knowledge
Data structures proficiency
Code quality and efficiency
Ability to handle ambiguity
Communication skills

System Design

System design capabilities
Scalability considerations
Trade-off analysis
Understanding of distributed systems
Experience with relevant technologies

Behavioral and Cultural Fit

Collaboration and teamwork
Adaptability
Learning agility
Initiative
Alignment with Google's values

Preparation Tips

1Master fundamental data structures and algorithms.
2Practice coding problems on platforms like LeetCode, HackerRank, and AlgoExpert.
3Study system design principles and common architectural patterns.
4Review object-oriented design principles.
5Prepare for behavioral questions by reflecting on past experiences using the STAR method.
6Understand the technologies and domains relevant to the specific role.
7Research Google's engineering culture and values.

Study Plan

1

Data Structures and Algorithms

Weeks 1-2: Data Structures & Algorithms Fundamentals. Cover arrays, lists, stacks, queues, trees, graphs, hash tables, sorting, searching, recursion, DP. Analyze complexity.

Weeks 1-2: Focus on core data structures (arrays, linked lists, stacks, queues, trees, graphs, hash tables) and fundamental algorithms (sorting, searching, recursion, dynamic programming). Practice implementing these from scratch and analyzing their time and space complexity.

2

Advanced Algorithms and System Design Concepts

Weeks 3-4: Advanced Algorithms & System Design Intro. Practice LeetCode (medium/hard). Study scalability, availability, load balancing, caching, databases.

Weeks 3-4: Dive into more advanced algorithms and problem-solving techniques. Practice medium to hard LeetCode problems. Begin exploring system design concepts, including scalability, availability, and common design patterns (e.g., load balancing, caching, databases).

3

System Design and Behavioral Preparation

Weeks 5-6: System Design Practice & Behavioral Prep. Design scalable systems. Use STAR method for behavioral questions.

Weeks 5-6: Deepen system design knowledge. Work through common system design interview questions. Focus on designing scalable and reliable systems. Prepare for behavioral questions by recalling specific examples of teamwork, problem-solving, and leadership using the STAR method.

4

Mock Interviews and Final Review

Week 7: Mock Interviews & Review. Simulate interviews, get feedback, and review weak areas.

Week 7: Mock interviews. Practice with peers or use online platforms to simulate the interview environment. Get feedback on your technical explanations and problem-solving approach. Review any weak areas identified during practice.

Commonly Asked Questions

Given an array of integers, find the contiguous subarray with the largest sum.
Design a URL shortening service.
Explain the difference between a process and a thread.
Describe a situation where you had to deal with a difficult stakeholder.
How would you implement a Least Recently Used (LRU) cache?
What are the trade-offs between SQL and NoSQL databases?
Tell me about a time you failed and what you learned from it.
Design a system to count the number of unique visitors to a website in real-time.
How do you approach testing your code?
What are your strengths and weaknesses as a software engineer?

Location-Based Differences

Mountain View, CA

Interview Focus

Distributed Systems DesignScalability and PerformanceCloud ComputingProblem-solving in large-scale environments

Common Questions

Discuss a challenging project you worked on in a distributed systems environment.

How would you design a system to handle real-time traffic data for a major city?

Explain the trade-offs between different caching strategies.

Describe your experience with cloud platforms like GCP or AWS.

How do you approach debugging complex issues in a production environment?

Tips

Familiarize yourself with Google Cloud Platform (GCP) services.
Understand common distributed systems patterns and challenges.
Be prepared to discuss your experience with large-scale data processing.
Practice explaining complex technical concepts clearly and concisely.

New York, NY

Interview Focus

Machine Learning ApplicationsSystem Design with a focus on user experienceCode OptimizationMicroservices ArchitectureWeb Security

Common Questions

How would you design a recommendation system for a streaming service?

Explain the principles of machine learning and how they apply to software engineering.

Describe a time you had to optimize code for memory usage.

What are the key considerations when designing a microservices architecture?

How do you ensure the security of a web application?

Tips

Review common machine learning algorithms and their applications.
Understand best practices for designing user-centric systems.
Practice coding challenges that involve optimizing for performance and memory.
Be ready to discuss security vulnerabilities and mitigation strategies.

Process Timeline

1
Data Structures and Algorithms45m
2
System Design45m
3
Behavioral and Team Fit45m

Interview Rounds

3-step process with detailed breakdown for each round

1

Data Structures and Algorithms

Coding challenge focusing on data structures and algorithms.

Technical Interview (Coding)Medium
45 minSoftware Engineer

This round focuses on your core data structures and algorithms knowledge. You will be presented with one or two coding problems and expected to solve them on a shared editor. The interviewer will assess your problem-solving approach, coding skills, and ability to explain your solution and its complexity.

What Interviewers Look For

A systematic approach to problem-solving.Proficiency in at least one programming language.Ability to write clean, efficient, and correct code.Clear communication of logic and trade-offs.Understanding of time and space complexity.

Evaluation Criteria

Correctness of the solution
Efficiency of the solution (time and space complexity)
Clarity and organization of the code
Ability to communicate the thought process
Handling of edge cases

Questions Asked

Given a binary tree, invert the tree.

Data StructuresAlgorithmsRecursionTrees

Find the kth largest element in an unsorted array.

Data StructuresAlgorithmsSortingHeaps

Preparation Tips

1Practice coding problems regularly.
2Focus on understanding the underlying data structures and algorithms.
3Be prepared to explain your thought process step-by-step.
4Consider edge cases and test your solution thoroughly.
5Write clean, readable, and well-commented code.

Common Reasons for Rejection

Inability to solve coding problems within the given time.
Poor understanding of fundamental data structures and algorithms.
Code that is inefficient, buggy, or not well-structured.
Difficulty explaining thought process.
Lack of attention to edge cases.
2

System Design

Design a scalable software system.

Technical Interview (System Design)Medium-Hard
45 minSenior Software Engineer or Engineering Manager

This round assesses your ability to design complex software systems. You'll be given an open-ended problem (e.g., design Twitter, design a URL shortener) and expected to propose a high-level design, discuss trade-offs, and justify your choices. This often involves distributed systems concepts.

What Interviewers Look For

A structured approach to system design.Ability to break down complex problems.Knowledge of common system design patterns and technologies.Consideration of non-functional requirements (scalability, availability, latency).Effective communication of design choices.

Evaluation Criteria

Clarity and completeness of the design
Scalability and performance considerations
Robustness and fault tolerance
Understanding of trade-offs
Ability to justify design decisions

Questions Asked

Design a system like Google Maps.

System DesignScalabilityDistributed SystemsAPIs

Design a rate limiter.

System DesignAlgorithmsDistributed Systems

Preparation Tips

1Study common system design interview questions and patterns.
2Understand concepts like load balancing, caching, databases (SQL vs. NoSQL), message queues, and APIs.
3Practice designing systems for scale and reliability.
4Be prepared to discuss trade-offs between different design choices.
5Clearly articulate your design and reasoning.

Common Reasons for Rejection

Inability to design a scalable and robust system.
Lack of consideration for trade-offs and constraints.
Poor understanding of distributed systems concepts.
Failure to address potential failure points or bottlenecks.
Inability to communicate design choices effectively.
3

Behavioral and Team Fit

Assesses teamwork, problem-solving, and cultural fit.

Behavioral InterviewMedium
45 minEngineering Manager or Senior Team Member

This round focuses on your behavioral and cultural fit. You'll be asked questions about your past experiences, how you handle specific situations (e.g., conflict resolution, dealing with ambiguity, project challenges), and your motivations. The STAR method (Situation, Task, Action, Result) is highly recommended for structuring your answers.

What Interviewers Look For

Examples of collaboration and teamwork.How you handle challenges and failures.Your ability to learn and adapt.Your motivation and passion for technology.Alignment with Google's principles (e.g., 'Focus on the user and all else will follow').

Evaluation Criteria

Teamwork and collaboration skills
Problem-solving approach in past situations
Leadership potential
Adaptability and learning agility
Cultural fit with Google's values

Questions Asked

Tell me about a time you had a conflict with a teammate and how you resolved it.

BehavioralTeamworkConflict Resolution

Describe a challenging project you worked on and how you overcame obstacles.

BehavioralProblem SolvingProject Management

How do you stay updated with new technologies?

BehavioralLearningAdaptability

Preparation Tips

1Prepare specific examples from your past experiences using the STAR method.
2Reflect on Google's core values and how your experiences align with them.
3Be honest and authentic in your responses.
4Show enthusiasm and passion for the role and Google.
5Practice articulating your experiences clearly and concisely.

Common Reasons for Rejection

Lack of alignment with Google's values.
Poor communication or interpersonal skills.
Inability to provide specific examples of past experiences.
Negative attitude or lack of enthusiasm.
Dishonesty or inconsistency in responses.

Commonly Asked DSA Questions

Frequently asked coding questions at Google

View all
Two SumEASYAdd Two NumbersMEDIUMLongest Substring Without Repeating CharactersMEDIUMMedian of Two Sorted ArraysHARDString to Integer (atoi)MEDIUMInteger to RomanMEDIUMLongest Common PrefixEASY3Sum ClosestMEDIUM4SumMEDIUMValid ParenthesesEASY