Preparing for a Google SWE interview is overwhelming mostly because of how much there is to cover: data structures and algorithms, coding, system design, behavioral questions, and often a discussion of your past projects. Finding material isn't the hard part. The hard part is deciding what to study first when you only have so many weeks.
What works better than grinding hundreds of random problems is building your plan around the skills the interview actually tests. Here's how I'd organize it, with the focus on coding and system design.
Know What You're Preparing For
Start by listing the areas you'll be tested on. For a typical Google SWE loop, that's:
Data structures and algorithms
Coding
System design
Behavioral questions
Project experience and technical discussion
The exact format varies by role and level, so what your recruiter tells you should be your starting point.
Of these, coding and system design need the most deliberate practice, and they need different kinds. Coding is about solving a problem correctly while the clock is running. System design is about explaining how you'd build something, weighing trade-offs, and getting your decisions across clearly. Studying them the same way won't work well.
Coding: Fundamentals, Then Patterns, Then Timed Practice
I'd split coding prep into three stages, in that order.
Review the fundamentals
Refresh the structures and algorithms that come up most often:
Arrays and strings
Hash tables
Linked lists
Stacks and queues
Trees and graphs
Heaps
Binary search and sorting
DFS and BFS
Dynamic programming and greedy algorithms
You don't need weeks of theory here. You just need the basics to feel automatic, so that when a graph problem shows up you can pick between DFS and BFS and write it out without burning several minutes trying to remember how.
Learn the patterns
Once the basics are back, start grouping problems by pattern instead of counting how many you've solved. A rough map looks like this:
Problem type Typical techniques
Arrays Prefix sum, two pointers
Subarrays Sliding window
Trees DFS, BFS
Graphs DFS, BFS, topological sort
Sorted data Binary search
Top K Heap
Optimization Dynamic programming
Intervals Sorting plus greedy
The point isn't to memorize solutions. It's to get good at seeing the structure of a problem you've never met. When you hit a new one, ask yourself three things: what's the simplest brute-force approach, why is it too slow, and which data structure or algorithm removes that bottleneck? That habit will serve you far better than a bank of memorized answers.
Stick to Mediums before chasing Hards
Spend most of your time on Medium problems, and work through each one the same way:
Read the constraints carefully.
Come up with a brute-force solution.
Find the bottleneck.
Work out a faster approach.
Write the code without peeking at the solution.
Test edge cases.
State the time and space complexity.
If you're stuck after a reasonable stretch, don't stare at it for an hour. Read the explanation, make sure you understand why it works, close it, and then implement it again from scratch. That last step matters more than it sounds, because reading a solution easily gives you the feeling of understanding without the ability to reproduce it.
Add timed practice
Once the fundamentals feel solid, start working under a clock. This is where interview prep stops resembling ordinary LeetCode practice. In a real interview you have to understand the problem, clarify assumptions, explain your plan, write the code, test it, and handle follow-ups, all in one sitting.
Do the occasional mock session where you solve one or two problems with no notes. Afterward, look at more than whether you got the answer. Did you spot the pattern quickly? Did you sink too much time into a weak first idea? Was your explanation clear? Did you test enough edge cases? Notes like these say more about your readiness than a total problem count ever will.
System Design
You can't prepare for system design by memorizing architectures and replaying them. Learn a framework you can apply to a question you haven't seen. This is the one I'd use:
Requirements → Scale → API and data model → High-level design → Deep dive → Trade-offs
Clarify requirements first
Work out what the system actually has to do. If you're asked to design a messaging app, you might ask whether it's one-to-one or group chat, whether delivery needs to be real-time, whether message history is kept, whether there are read receipts or notifications, and how many users to expect. You don't need to ask everything, only the questions that would change your design.
Estimate the scale
Get comfortable with rough numbers: daily active users, requests per second, storage needs, read/write ratio, and how fast traffic might grow. Precision doesn't matter. What you're deciding is whether a simple setup will do or whether you need to start thinking about caching, sharding, replication, or queues.
Sketch the high-level design
Lay out the major pieces: load balancer, API servers, database, cache, message queue, object storage, search or notification services, whichever apply. Don't include a component just because it appears in every system design diagram. Each one should be there because it solves a specific problem.
Study real examples
Rather than memorizing dozens of questions, pick a handful of representative systems:
URL shortener
Messaging system
File storage
News feed
Rate limiter
Notification system
Distributed storage
For each, concentrate on why the design works. If there's a cache, what problem is it solving? If there's a queue, why does that step need to be asynchronous? If you chose a particular database, what access pattern makes it a good fit? Answering those questions is what lets you carry the ideas over to a new problem.
A 4-Week Plan
If you have about a month, this is how I'd lay it out.
Week 1: coding fundamentals. Cover arrays, strings, hash tables, linked lists, stacks and queues, trees, and binary search. Don't worry about finishing everything. The real aim this week is to find out where you're still slow or shaky.
Week 2: advanced patterns. Move on to graphs, DFS/BFS, heaps, sliding window, two pointers, intervals, dynamic programming, and greedy algorithms. Start adding timed practice here.
Week 3: system design. Work on clarifying requirements, capacity estimation, API design, data modeling, high-level architecture, caching, database choices, queues, replication and partitioning, and trade-offs. Practice explaining your designs out loud rather than only drawing them.
Week 4: mocks and review. This isn't the week to learn a pile of new topics. Mix timed coding sessions, system design mocks, behavioral prep, and project discussions, and go back over the problems you missed earlier. If you keep making the same mistake, fix that instead of adding more problems to your list.
Coding vs. System Design: How to Split Your Time
There's no ratio that works for everyone. It depends on the role, the interview format, and where you're weakest right now. If coding is your weak spot, put more hours there. If Mediums feel comfortable but you lose the thread in design discussions, shift toward system design.
An easy way to decide is to jot down your weak spots after each session:
Area Current issue Next step
Coding Slow on graph problems Practice DFS/BFS
Coding Missing edge cases Add a 10-minute review
System design Vague requirements Practice clarifying questions
System design Too many components Focus on trade-offs
Communication Scattered explanations Practice thinking aloud
That gives you a plan based on what's actually going wrong rather than on guesses.
Don't Neglect Communication
A common mistake is treating the interview like a coding contest. Your interviewer isn't only reading your final code; they're following your reasoning. So practice saying it out loud. For example: "The brute-force approach is O(n²), so I want to cut down the repeated work." Then say which data structure or algorithm you'll use and why.
The same goes for system design. "I'll use Redis here" tells the interviewer very little. Compare that with: "Reads far outnumber writes, so I'd cache the frequently accessed data to take load off the database." The second version shows the thinking behind the choice, which is what they want to see.
If You Only Have Two Weeks
Don't try to cram a three-month plan into fourteen days. Cut it down to what matters.
For coding, review the common data structures, practice the common patterns, stay on Medium problems, do timed sessions, and go over your mistakes. For system design, learn one reusable framework, study a few representative systems, practice capacity estimation and explaining trade-offs, and do at least a few mock sessions. You're aiming to be consistent, not to know everything.
Wrapping Up
A good Google SWE interview preparation plan isn't about ticking off an enormous topic list. It's about building the skills you'll use in the room. For coding, that means recognizing patterns, writing correct code, and solving problems under time pressure. For system design, it means requirements, scale, architecture, and trade-offs rather than memorized diagrams. And as the interview gets closer, practice in conditions that feel as much like the real thing as you can manage.
If you'd like a wider look at the process, including the interview stages and the other technical areas, the Google SWE Interview Guide on InterviewShow is a good next read.
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