The ground is shifting under our feet
Software engineers are facing something we haven't seen before. I looked around recently and observed great uncertainty around us. Our reality is changing, we are facing more challenges. Firstly, AI started changing the rules. It's evolving so fast that nobody really knows what to expect anymore, today's reality is not the same as 3 years ago. We cannot be sure how the future will look like and how it will influence our daily work and needed skills.
Secondly, there have been huge layoffs also among the biggest IT companies (layoffs in Amazon or Google for instance). I have recently witnessed my colleagues getting laid off from their IT jobs, some of them after 10 years at the same company. Business decisions. The times when we had a certain job security are long over. Now we need to be prepared to quickly adapt to changes, we don't know what the future will bring.
My response: start preparing now
In response to all these uncertainties I want to start preparing myself for what's coming. How do I stay relevant? What skills actually matter now? How do I prepare for interviews when the market can shift overnight?
I built a study companion to start preparing
As a starting point I put together a list of topics every software engineer should be familiar with — a standard checklist for interviews.
You will find this in this GitHub project.
Mandiaa
/
Software-Engineer-Interview-Study-Plan
Interview knowledge base for advanced backend java software engineers with cross-cutting DevOps skills. Covers Java, Kafka, Spring Boot, DDD, CQRS, Event Sourcing, PostgreSQL, Kubernetes, and more. Created with help of Claude AI. Structured Q&A, coding exercises & 12-week study plan.
Software Engineer with DevOps skills — Interview Knowledge Base
Preparation for Software Engineering with DevOps/Platform Engineering skills roles Core focus: Kafka · Multithreading · Hexagonal Architecture · Microservices · PostgreSQL · DDD · CQRS · Event Sourcing Core Java · Spring Boot · JPA · Microservices · Algorithms · System Design · DevOps
A comprehensive preparation guide for landing your next software engineering role.
Covers technical depth, behavioral mastery, AI awareness, and how to run your own interview.
🎯 Frequency Legend
| Tag | Meaning |
|---|---|
| 🔴 Must Know | Asked in virtually every senior interview — no excuses |
| 🟠 Very Likely | Asked in most senior interviews — high priority |
| 🟡 Likely | Asked at many product companies — good to know deeply |
| 🟢 Niche | Asked at FAANG / specialist roles / if on your CV |
📁 Planned Repository Structure
I propose the topics mentioned in the following study plan. They could be structured…
This repository is designed to be used interactively with Claude Code for study sessions, generating daily quizzes, blind LeetCode challenges, live coding mocks, architecture challenges, system design mocks, DevOps mocks, behavioral mocks, and gap analysis.
It covers the same ground I went through a few years back when I was job hunting.
It's biased by my preferences and experience. I'm a backend engineer with 10+ years in Java, and recently I've been working closely with DevOps tools as well. I find it satisfying working close to the infrastructure layer. I don't want to forget or omit these skills because I find them very useful and in demand. Having a DevOps mindset on top of backend experience is what I think sets me apart. But you could use this as a starting point and adapt it to your own profile.
How does this map to a real interview process?
This project covers the typical backend engineer interview process, which often looks like this:
- Initial assessment of the candidate's knowledge, in the form of a conversation or more often a written test
- One or two live coding exercises, might check algorithmic knowledge with typical LeetCode problems, but some companies check usage of technologies used in their company and problems that are closer to their domain.
- Some companies could give you a take-home task
- More abstract level interview verifying your knowledge of architecture, design patterns etc.
- Non technical step with behavioral questions - seems easy, but it helps to know what interviewers actually expect.
Interviews vs. real work
Taking this process into account, have many of you ever wondered if this is what you do every day as a software engineer? This reality is slightly different to what we do on a regular basis.
The companies expect us to benefit from AI agents during working hours to support us on different tasks from generation of code, through debugging up to log analysis. The more the better. I have read recently that Meta had a leaderboard in number of tokens generated by their workers. So our job is to benefit from AI to speed up our work 10x, while still judging the AI output critically, making sure we will be able to find any mistakes it produces. The interview process hasn't caught up with how we actually work today.
Don't get me wrong, I understand this approach, I also believe that to understand something it is valuable to build it ourselves without AI support. It tests that we can think on our own. However, in my opinion this approach is quite outdated and it will have to evolve at some point to fit the modern conditions.
The skills we use during the job will involve less and less coding. Now we employ AI agents to do that for us. And our job will be more to think, to do tasks on a higher level, to plan, design and to monitor if the AI is performing well and that what AI produces actually meets our expectations. Until then, the rules haven't changed: master your stack, handle the pressure, produce code on demand.
AI awareness as an interview topic
AI is a hot topic in company interviews. So, to prepare for the future, I made sure the project covers AI awareness: practical tools for daily work and questions about using AI to boost productivity. I am interested in your experiences, are AI tool questions showing up in your interviews yet? I'm genuinely curious how often this topic comes up and whether this varies by company size. Is it mandatory to be able to name the hottest AI tools, even better having hands on experience with them? Drop a comment if you've seen this come up in interviews.
Wish me luck
I'm excited to start this blogging (and learning) journey. Please, if you find this project helpful give me some feedback ;) I'd love to hear your tips on improving the study plan — what would you add or change?
For now I will concentrate on learning following this path. But expect to hear from me in the future, when I'll describe how useful it was for me.
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