"Aldy, do you think QA Engineer roles will still exist in the next 3–5 years?"
That was the question my friends at work asked me one morning.
I replied, "Of course! We have a unique skill set. Everyone can test, but not everyone has a tester's mindset."
Yet, behind that confident tone, I couldn't help asking myself, "Should I re-skill or prepare to switch careers?"
Curious about other perspectives, I posted a poll on LinkedIn. As expected, most respondents felt QA roles would remain stable, but about 19% believed switching might be a smart move.
Even Ibrahim Arief, a prominent Indonesian tech leader, endorsed a 'hard pivot' to software engineering, echoing concerns highlighted in my LinkedIn poll.
QA roles will survive, but only for those who adapt to the changing landscape. This article explores how acquiring knowledge in four areas of software development, data transport, data storage, application architecture, and code quality, will help you thrive as a QA engineer.
But first, let's look at how the QA field itself has evolved with time.
How QA Has Evolved: From Manual Testing to Automation
In the early days of quality engineering, almost everything was tested manually. QAs would wait for developers to complete their work before executing test cases and running through tickets.
Over time, companies recognized that manual approaches couldn't scale and began investing in automation to reduce repetitive tasks and free up time for exploratory testing.
I was particularly curious about how FAANG companies approached quality engineering, so I researched them. I was shocked to discover traditional QA engineer roles no longer exist there!
These companies have fundamentally transformed their quality management approach, with developers assuming testing responsibilities through automation frameworks that reduce the need for dedicated QA personnel.
Why this shift?
It's simple. It's more efficient when devs also own testing and quality. And it's logical since it drives devs to produce high-quality code.
With AI, low-code, and no-code tools making test creation easier, there's less need for purely dedicated testers.
Why Technical Knowledge Matters in QA
This drive for efficiency and developer ownership of quality naturally leads to the question: where does QA fit in, and what skills are now most valuable?
The encouraging reality is that while shift-left testing (testing as early as possible) is changing everything, QA isn't dying, it's evolving into a more technical role. Microsoft's approach demonstrates this transformation.
Many teams have merged developers and testers, catching issues at the code or API level instead of waiting for full UI E2E tests.
However, it's important to note that not all companies follow the same path. Companies like Apple and Amazon still have dedicated QA teams. Yet, these aren't traditional QA roles as you might think. They're more technical than ever before!
To stay relevant, we need to work closer with developers and speak their language. This means understanding how to test at a lower level, examining functions, API contracts, and system interactions, rather than relying too heavily on black-box testing approaches.
Let's explore the four core technical skills that will help you thrive at a time where AI seems to be taking over the QA profession.
Four Core Technical Areas to Master
Let's talk about four fundamental skills that'll help you "stand out" as a QA in today's tech world. To make it easier to understand, we'll use a restaurant analogy!
1. Know How Data Is Transported
Imagine working in a restaurant. The chef prepares meals in the kitchen, but you need waiters to deliver those dishes to customers. **In software, the waiters are the systems that transport data from back-end to front-end, the data transport layer.
I still see many QAs who can operate tools like Postman but don't grasp the concept of how systems communicate, whether through REST APIs, GraphQL, or message queues. Understanding data transport is crucial! You need to know which service sends data where, and how it's formatted.
With this knowledge, you can spot issues in API contracts before they cause problems in the UI. You can ask, "Why is this endpoint returning inconsistent date formats?" or "What happens if this service times out?"
2. Know How Data Is Stored and Retrieved
Every restaurant has cabinets and shelves for organizing utensils and ingredients. If they're disorganized, cooking becomes painfully slow. It's the same with databases.
You should understand the basics of SQL vs. NoSQL, indexing, and normalization. Without this foundational knowledge, you can't tell if a developer's new query is inefficient or if there's a better database design that could improve performance.
For example, when testing a feature that seems slow, database knowledge will help you determine if the issue is with inefficient queries rather than front-end code. You might suggest, "Hey, this query is pulling the entire table when we only need three columns. Let's optimize it!"
3. Know How Applications Work Under the Hood (Architecture Basics)
Understanding how applications are structured makes you a more effective QA engineer.
When you know how the different parts of a system connect and communicate, you can spot potential problems before they reach users. It's like being able to inspect the entire restaurant operation instead of just tasting the final dish. You see the complete picture.
This knowledge directly improves your testing in several ways:
- You can design more effective test cases that target component boundaries
- You can identify which parts of the system need the most testing
- You'll know where data transformations happen and where errors might occur
- You can predict how changes in one area might affect another
For example, when a feature isn't working correctly, you might recognize it's because two components are sharing data in an inconsistent way.
Or you might realize that a slow page load is happening because of how the app is structured to fetch data. This deeper understanding helps you communicate issues more clearly and suggest more effective solutions.
4. Know How to Read and Understand Code
Every good QA should be able to determine what 'good' and 'bad' code looks like.
You don't need to build entire features. Still, you should be able to review pull requests, check unit test coverage, and question suspicious logic.
It's like having basic cooking knowledge. If you see ice cubes on a steak, you immediately know something's wrong! Similarly, if you see code without error handling or proper input validation, you can flag it before it causes problems.
For example, you might notice a function that assumes data will always arrive in a specific format (but what if it doesn't?), or catch that a feature implements validation rules differently than existing components, creating an inconsistent user experience.
Now that we've covered all four technical skills, you might wonder, "How can I build these core skills?"
The best way is to spend some time in development, even if it's just small projects. Try coding a simple personal website or crafting an automation test framework from scratch. This hands-on approach "forces" you to grapple with data transport, storage, code-level logic, and overall system architecture.
The more you build, the sharper your technical instincts and your QA sensibilities become.
Evolving with the Changing QA Landscape
The QA world is indeed changing, but it's far from dead.
Yes, AI and no-code platforms can handle many repetitive tasks, and shift-left means devs are now testing at the code level. Still, QAs bring a unique mindset, critical thinking, risk assessment, and holistic approach that pure dev roles often lack.
What's crucial is that we evolve with the times by mastering the four technical skills outlined above:
- Understanding how data is transported between systems
- Knowing how data is stored and retrieved efficiently
- Comprehending application architecture and system interactions
- Being able to read and evaluate code quality
Strengthening these technical abilities and actively collaborating with developers will make you an essential contributor in any tech team. We're still the gatekeepers of quality — but we can't stay at the gate. We need to move upstream, partnering with devs earlier in the process, guiding strategy, and catching issues early. As Eric Hoffer said, "In a time of drastic change, it is the learners who inherit the future."
References
MagicPod is a no-code AI-driven test automation platform for testing mobile and web applications designed to speed up release cycles. Unlike traditional "record & playback" tools, MagicPod uses an AI self-healing mechanism. This means your test scripts are automatically updated when the application's UI changes, significantly reducing maintenance overhead and helping teams focus on development.
