SDE & AI @Amazon | Simplifying software engineering, system design, and generative AI | Building Hungry Minds: the newsletter to stay ahead in software 👇

Tell me about the last time you felt growing as a software engineer. Really.

𝙽𝚞𝚕𝚕𝙿𝚘𝚒𝚗𝚝𝚎𝚛𝙴𝚡𝚌𝚎𝚙𝚝𝚒𝚘𝚗? Here's why:

We all face information overload as software engineers. 
There's too much content and too little time.
You have to stay relevant, but it's hard.

Right now, you're either working or taking a break.

You are hoping to get a dopamine shot from scrolling your feed.

You can find:

💡 A practical tip to implement
📚 An inspirational quote
😆 Something funny

And then what?

→ Scroll past it? 
→ Forward it blindly to a friend?
→ Forget about it in the bookmarks?

The harsh truth: nothing.

What you consume passively doesn't make you learn.

How do I know?
It was the same for me for years.

How do you stay updated with AI, stay relevant in the job market, and continuously grow?

That's the problem I set out to solve with Hungry Minds - a free weekly newsletter to help software engineers learn and stay updated, without the noise.

I'm doing that by:

1. Writing to you daily on LinkedIn to 40K+ followers and over 5M impressions last year
2. Compressing 1 week of software deep dives and trends in 1 free newsletter issue
3. Sharing my thoughts and learnings being promoted to SDE II in 1.5y at Amazon

If that sounds interesting, check out Hungry Minds or DM me! I'm always down for a chat.

Save 50+ hours of your week here: 𝗵𝘁𝘁𝗽𝘀://𝗵𝘂𝗻𝗴𝗿𝘆𝗺𝗶𝗻𝗱𝘀.𝗱𝗲𝘃 (6K+ engineers from startups to big tech are reading already)

DISCLAIMER: Opinions expressed are solely my own and do not express the views or opinions of my employer.

My career background:

I'm currently an L5 software engineer at Amazon in Madrid, working on core Search CX and Personalization. 
In 2022/2023, I delivered 2 successful billion-scale projects with multiple teams across different time zones.

I have worked with multiple code languages (although this doesn't make a difference), and with tech stack in the lines of AWS CDK, React applications, scheduling systems, backends with GraphQL, and simple AI applications.

My education background:

- I finished a Master's in Machine Learning and AI with l'Ecole Polytechnique and IPP
- I completed a Master's in Computer Science and Data Science with ESILV Paris
- I got a Master's in business development and marketing with EMLV Paris

Alexandre Zajac

𝗖𝗼𝗱𝗲 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗡𝗢𝗧 𝗔𝗯𝗼𝘂𝘁:


→ One senior gate-keeping all the knowledge. 

Code documentation is ALL about:

→ Sharing knowledge so the whole team knows how the system works.

1-person docs = single point of failure.

Whole-team docs = redundancy + knowledge multiplication.

Remember:
You NEED documentation to manage a system through people and time.


How do you manage docs effectively?


P.S: Documente my personal interview cheatsheet: https://lnkd.in/d_eCGEJR

SDE & AI @Amazon | Building Hungry Minds to 1M+ | Daily Posts on Software Engineering, System Design, and AI ⚡

No alternative text description for this image

10+1 System Design Patterns Every Engineer Should Know in 2026:


___
btw, my personal interview cheatsheet: https://lnkd.in/d_eCGEJR
___


0.💡Load Balancer: 

Distributes incoming traffic across multiple servers to ensure no single server gets overwhelmed.

➡️ Useful when: You're scaling web applications or ensuring high availability.

1.💡CDN (Content Delivery Network):

Caches content at edge locations worldwide, reducing latency by serving users from the nearest node.

➡️ Useful when: You're serving static assets globally or streaming media at scale.

2.💡Message Queue:

Decouples services by letting them communicate asynchronously through a queue (like RabbitMQ or Kafka).

➡️ Useful when: You need reliable communication between microservices or handling traffic spikes.

3.💡Rate Limiter:

Controls the number of requests a user or service can make within a time window.

➡️ Useful when: You're protecting APIs from abuse or managing resource allocation.

4.💡Circuit Breaker:

Prevents cascading failures by stopping requests to a failing service and allowing it time to recover.

➡️ Useful when: You're building resilient microservices that depend on external APIs.

5.💡Database Sharding:

Splits a database into smaller pieces (shards) distributed across multiple servers.

➡️ Useful when: Your database is too large for a single server or you need horizontal scaling.

6.💡Caching Layer:

Stores frequently accessed data in memory (Redis, Memcached) to reduce database load.

➡️ Useful when: You're optimizing read-heavy applications or reducing latency.

7.💡API Gateway:

A single entry point that handles routing, authentication, and rate limiting for all your microservices.

➡️ Useful when: You're managing multiple services and need centralized control.

8.💡Reverse Proxy:

Sits in front of servers and forwards client requests, adding security, caching, and load balancing.

➡️ Useful when: You need SSL termination, compression, or hiding server architecture.

9.💡Event-Driven Architecture:

Services react to events rather than direct calls, enabling loose coupling and real-time processing.

➡️ Useful when: Building real-time analytics, notifications, or audit logging systems.

10.💡CQRS:

Separates read and write operations into different models, optimizing each for its specific purpose.

➡️ Useful when: You have complex domains with different read/write performance needs.


What did I miss?

Solving system design problems is HARD.

1 mega-template to solve any problem:


Approaching system design problems can be harder than coding problems.

For one reason: ambiguity.

13 concepts to use when solving a system design problem:


0. Load Balancer:

A load balancer efficiently distributes incoming network traffic across multiple servers to ensure no single server bears too much load.

1. API Gateway:

An API Gateway acts as a single entry point for all client requests, providing a unified interface to various microservices.

2. Cache:

Caching stores recently accessed data for speedy retrieval, reducing the load on databases and improving response times.

3. CDN:

A Content Delivery Network (CDN) stores static content closer to users to minimize latency and speed up content delivery.

4. Distributed File Storage:

Distributed file storage ensures data is redundantly stored across multiple locations for fault tolerance and easy access.

5. Search Index:

Using a search index like Elasticsearch allows for scalable and fast search capabilities across large datasets.

6. Notification Service:

A notification service handles the delivery of messages or alerts to users across different platforms and devices.

7. Data Processing Systems:

Data processing systems like Hadoop and Spark analyze large volumes of data, enabling scalable and complex computational operations.

8. Distributed Scheduler: 

A distributed scheduler orchestrates and manages the execution of distributed tasks across various computing resources.

9. Metadata Server:

The metadata server stores information about the structure of data, enabling efficient access and management.

10. Block Server:

Block servers manage the storage blocks in distributed file systems, which helps in data replication and retrieval.

11. Distributed Logging:

Distributed logging collects logs from various services in a centralized manner for monitoring and troubleshooting.

12. Replication and Partitioning:

Replication ensures data availability and durability, while partitioning helps in scaling databases by distributing data across different nodes.


Reference (aff.):

🔥 Grokking the system design interview: 
https://lnkd.in/dcMM8sZM


What did I miss?


~~~

👉🏻 Join 50,001+ software engineers getting curated system design deep dives, trends, and tools (it's free):
➔ https://lnkd.in/dkJiiBnf

~~~

If you found this valuable:

👨🏼‍💻 Follow Alexandre Zajac
🔖 Bookmark this post for later
♻️ Repost to help someone in your network

You are what you eat.

10 brain foods to grow as an engineer:


0. Why Clock Synchronization Makes Engineers Lose Sleep:
► https://lnkd.in/gkDbWARS

1. How ChatGPT Actually Stays on Track (by Neo):
► https://lnkd.in/ggNMHDuV

2. How to Build Internal AI Agents That Actually Work:
► https://lnkd.in/gkJKyQnm

3. Scaling LLMs to Larger Codebases:
► https://lnkd.in/gWwfibJQ

4. Connection Pooling Finally Explained in 6 Minutes (by Nikki):
► https://lnkd.in/gkx6eNSG

5. 5 Engineering Dogmas It's Time to Retire (by Anton):
► https://lnkd.in/gYTdTmap

6. The System That Eliminates Interruptions for 10x Engineers (by Francisco):
► https://lnkd.in/g65_i6jf

7. Why Logging Sucks (And Everyone Knows It):
► https://loggingsucks.com/

8. Internal Dev Tooling at Meta (from Gergely):
► https://lnkd.in/g_A_BxFd

9. The Future of Software Development Is Software Developers:
► https://lnkd.in/gvzpK2CM


I am packaging the other deep dives and trends in today's issue of Hungry Minds.

Join 50,001+ software engineers from big tech to startups here: https://lnkd.in/dkJiiBnf


What are you reading this week?


#softwareengineering #systemdesign #programming

Freelance (Self employed)

Machine Learning Engineer

Accenture

• Designed and implemented robust data pipelines, ETL processes, and data integration solutions to efficiently collect,
transform, and load data from various sources.
• Collaborated with cross-functional teams to design and develop data pipelines and ETL processes.
• Utilized Tableau to create interactive data visualizations and dashboards for effective data communication.
• Seamlessly integrated Tableau into data pipelines to enable data-informed decision-making.
• Kept up-to-date with emerging data technologies and trends to continuously improve data engineering processes and stay
at the forefront of the industry.

Associate Software Engineer

Associate Software Engineer | DBMS, Tableau, SQL, PowerBI, Python, Machine learning

As an Associate Software Engineer at Accenture, I designed and implemented robust data pipelines, ETL processes, and data integration solutions to efficiently collect, transform, and load data from various sources. I collaborate with cross-functional teams to deliver high-quality data products that enable data-informed decision-making. I also utilize Tableau to create interactive data visualizations and dashboards for effective data communication. 

I graduated from Acharya Institutes with a Bachelor's degree in Computer Science, where I honed my technical skills and developed a passion for data engineering. I am proficient in languages like Python, Javascript, and C++, and technologies such as Big Data, Data science, and machine learning. I have also earned a Data Science Masters certification from PhysicsWallah. I am eager to learn new skills and apply my knowledge to solve real-world problems.

taplio

The Battle

Alexandre Zajac

Ahmad Bader

Followers

Posts

Engagements

Likes

Performance ScoreAlexandre Zajac's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Alexandre Zajac

Ahmad Bader

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Alexandre Zajac's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreAlexandre Zajac's LinkedIn Performance