Most of the time, scrolling on LinkedIn or Twitter meets you with several success stories like
“Just landed my offer at Google/Microsoft/Amazon!”
And the first thing that comes to your mind:
What exactly did they study?
What skills are these companies really looking for?
Where should I even start?
One of the most common questions we hear from aspiring tech professionals and freelancers looking to land full-time roles is “What do I actually need to learn to stand out at companies like Google, Microsoft, or Amazon?”
And the honest answer is that there’s no single “golden” curriculum, but there is a clear set of skills and areas that consistently get valued.
Keep reading to find out how to make a clear path for yourself.
Why Study Paths Matter for Top Tech Companies
It’s tempting to think, “If I just get really good at LeetCode, I’ll land the job.”
But that’s only part of the puzzle.
According to LinkedIn’s Emerging Jobs Report, hiring managers at top companies are increasingly looking for well-rounded candidates who bring:
- Strong coding fundamentals
- Practical system-building skills
- Communication and collaboration abilities
- Business awareness – understanding how tech drives value
Certifications help, but depth of skill matters more.
Degrees help, but projects and problem-solving matter more.
Core Areas to Study
Here are some topics per category to study if you want to land interviews and succeed at Google, Microsoft, or Amazon:
Data Structures & Algorithms
You can’t skip this, especially for Google and Amazon.
Both companies put a heavy emphasis on your ability to solve problems efficiently.
Study topics:
- Arrays & Strings
- Linked Lists
- Stacks & Queues
- Hash Maps & Hash Sets
- Trees & Graphs (very important!)
- Dynamic Programming
- Recursion & Backtracking
- Sorting & Searching
Resources:
Tip: For Amazon and Google, focus on optimising for time & space complexity in interviews.
Systems Design
Microsoft, Amazon, and Google love candidates who can design scalable systems, even for junior-mid roles now.
Study topics:
- Load Balancing
- Caching
- Database Design (SQL + NoSQL)
- Microservices
- CAP theorem
- Event-driven architectures
- Data consistency and availability
- High availability & fault tolerance
Resources:
- System Design Primer (GitHub free guide – gold standard)
- Grokking the System Design Interview
Tip: Freelancers working with startups can get real-world experience with system design → mention this in interviews!
Coding Fundamentals
Top tech companies expect you to know a mainstream language deeply (Java, Python, C++, Go, TypeScript)
Core language concepts:
- Memory management
- Object-oriented programming
- Functional programming basics
- Multithreading/concurrency
Resources:
- Language-specific books & official docs (ex: Effective Java, Python Cookbook)
- Open-source projects in your target language
- Exercism.io for hands-on language practice
Tip: Don’t jump between 5 languages. Pick 1–2 and go deep.
Cloud & Distributed Systems (Bonus: Critical for Freelancers too!)
More and more interview loops now ask about cloud architecture and working with distributed systems.
Study topics:
- AWS, Azure, or GCP basics
- Containerization (Docker)
- Kubernetes basics
- Serverless computing
- CI/CD pipelines
Certifications that help:
AWS Certified Solutions Architect
Google Professional Cloud Architect
Data Engineering & Big Data (Increasingly Important)
Amazon and Google especially value data-savvy engineers, even in general SWE roles.
Study topics:
- ETL pipelines
- Data modeling
- Distributed data systems (Hadoop, Spark)
- SQL optimization
- Data visualisation basics
Resources:
Soft Skills & Behavioural Prep
One of the most underrated “study” areas is your ability to communicate and collaborate.
Study topics:
- STAR method for behavioural questions
- Conflict resolution
- Working in cross-functional teams
- Giving and receiving feedback
- Leadership principles (especially critical for Amazon!)
Resources:
- Amazon Leadership Principles → study deeply
- Google’s focus on collaboration and humility → practice storytelling around teamwork
- Grokking the Behavioural Interview
How to Structure Your Learning
One mistake many candidates make is trying to study everything at once.
Instead, think of your learning path in layers:
| Layer | Focus | Time Allocation |
|---|---|---|
| Foundation | Coding fundamentals, DSA | 40% |
| System Thinking | System design, cloud | 25% |
| Domain Specialization | Data engineering, ML, security, etc. | 15% |
| Behavioral & Soft Skills | Interview prep, leadership | 10% |
| Freelance & Portfolio Projects | Real-world experience | 10% |
Adjust percentages based on your experience level and target role.
Conclusion
If you’re serious about landing a job at Google, Microsoft, or Amazon, study smart, not just hard.
Prioritize:
- Strong coding fundamentals + algorithms
- Scalable system design
- Practical cloud skills
- Data engineering basics
- Soft skills + leadership
And if you’re freelancing, leverage every project as a learning opportunity and portfolio booster.
The tech hiring process rewards those who can apply knowledge to real problems, not just memorise algorithms.
So as you study:
- Build projects
- Share your learning in public (LinkedIn, GitHub)
- Stay curious and consistent
Your next interview invite could be one study session away.
