Tech Professionals Are Getting Fired: Here’s How to Be the One Who Doesn’t

Tech layoffs have exposed something a lot of people didn’t want to admit: some roles aren’t bouncing back the way they used to.

Christopher Garzon’s point is blunt. If you’re in tech and you’re still asking whether your current job is “safe,” you’re asking the wrong question. The better question is what your role looks like five, 10, or 20 years from now, and whether the business can afford to lose it.

The tech market changed, and some roles are shrinking

A lot of people still talk about layoffs like they’re a short dip. Garzon argues the opposite. Companies found ways to get more output from fewer people, and many of them aren’t going back.

That’s why waiting for the market to “come back” can be a bad bet. In a lot of teams, the jobs that disappeared aren’t being refilled. The work is getting pushed onto smaller teams, handled with AI, sent offshore, or dropped altogether.

Which roles are getting squeezed first

Some of the pressure is showing up in predictable places:

• QA roles are getting reduced because more testing can be automated, and more of it is getting pushed onto engineers.
• Front-end roles are getting squeezed at smaller companies that would rather use low-code or no-code tools.
• Back-end roles are facing more price pressure from offshore hiring.
• Product managers are seeing smaller teams under them, which usually means fewer openings and more competition.

Garzon shares the story of a software engineer with eight years of experience making more than $200,000 a year. After three rounds of layoffs, that engineer’s team went from 12 people to three. The work didn’t disappear. The company simply expected the top performers to do more of it, with AI and longer hours.

That person didn’t get a raise. It wasn’t a promotion. It was a warning.

Stop asking, “Is my job safe?” Ask, “What does this role look like in 5, 10, or 20 years?”

That’s the shift. Short-term survival matters, but long-term direction matters more.

Why data engineering sits closer to the money

A company can cut a testing team and still keep operating for a while. It can shrink a front-end team and get by with templates or lighter releases. What it can’t do for long is break the systems that move clean, usable data across the business.

That’s the case for data engineering. It sits close to revenue, reporting, operations, forecasting, and decision-making. When the pipelines break, other departments feel it fast.

Here are a few places where that dependence shows up right away:

1. Marketing needs clean pipeline data to know where budget is working.
2. Finance needs reliable data to close the books every month.
3. Executives need trusted numbers before making decisions.
4. AI teams need clean datasets before any model can be trained well.

When those systems fail, the problem doesn’t stay in the data team. It hits the rest of the business.

If your role disappeared tomorrow, would the business stop making money?

That’s the question Garzon wants people to ask about their own job. If the answer is yes, or even “maybe,” you’re in a stronger position. If the answer is no, then you need to move closer to work the business can’t afford to break.

He also makes a point that catches a lot of people off guard: companies are cutting software engineering roles, then posting data engineering roles right after. In his view, some of those companies could retrain internal engineers in two to four months and save money. Many still aren’t doing it. They’re going straight to the market instead.

That gap is where the opportunity is.

Your current tech skills already map to data engineering

For most people in QA, software engineering, IT, or analytics, this isn’t a full restart. It’s a redirect.

You’re not throwing away your career. You’re taking the parts that already make sense, then stacking a smaller set of missing skills on top.

QA, automation, and IT work translate well

A QA engineer already thinks in terms of validation, failure cases, repeatability, and data quality. That’s a big part of data engineering.

Automated test frameworks map well to automated data checks. Test databases aren’t that far from the databases a data engineer works with, they’re usually smaller and used in a different setting. API testing maps to API-based data extraction. Script writing maps to pieces of pipeline development.

One example from the video makes this pretty clear. An automation engineer with five years of Python experience was already writing scripts, working with APIs, and maintaining test databases. He thought his background might be too far removed. Garzon showed him the overlap: data validation, ingestion, scripting, and database work were already there. Four months later, that engineer moved from an $80,000 QA role to a data engineering job that nearly doubled his payIf you want a deeper breakdown of that bridge, this guide to moving into a data engineering role connects software, analytics, and ML backgrounds to the work more directly.

Software engineering maps even faster than most people think

Software engineers usually have an even shorter jump.

Back-end work already touches databases, APIs, production systems, debugging, and scale. That’s a big piece of the job. Data engineers use a different tool mix and think more about data movement, warehouse design, and reliability, but the base is familiar.

The self-audit here is simple. Pull up your resume and look at every bullet. Ask whether it already includes API work, data ingestion, database management, SQL, Python scripts, data modeling, validation logic, or pipeline-style tasks. A lot of people are surprised by how much is already there.

If you want something more structured, the free career transition roadmap for tech professionals lays out the projects, resume angles, and study order Garzon talks about.

A four-month plan is more realistic than most people think

One of the strongest points in the video is that you probably don’t need four years, a master’s degree, or a pile of certificates. You need a focused plan and a way to perform in interviews.

Garzon frames this as about a four-month push for people who already have a technical background, with some people stretching parts of it a bit longer depending on pace. The larger point is what matters: think in months, not years.

Here’s the rough order he gives.

The interview piece matters more than people think. Lots of people study for months, then freeze when they need to explain projects, answer behavioral questions, or talk through system design. College doesn’t teach that well. Most certifications don’t either.

Garzon shares a story about a QA engineer whose team went from eight people to three. She started learning before the second layoff wave hit. By month one, she was learning SQL. By month two, she had personal projects, ETL work, database practice, and warehouse experience underway. By month three, she was rewriting her resume and applying. When the next layoff came, she was ready. She landed a higher-paying data role and still got severance from the job she was leaving.

That’s the whole point. The best time to do this is while you still have income and options.

If you’re worried that you need formal credentials first, this piece on getting a data engineering job without a degree makes the same case in plain English.

Data engineering has a better moat than most tech roles

Garzon’s supply-and-demand claim is simple: for every five data engineering openings, there’s about one qualified candidate. Whether you take that as a market snapshot or a directional point, the message is the same. There is still a gap between demand and people who can do the work.

That gap won’t stay open forever. Software engineers, ML people, analysts, and other technical workers can learn these skills too. But right now, companies still struggle to find people who understand both engineering discipline and data systems.

That combination matters because data engineering isn’t taught well in a lot of bootcamps, and many schools still don’t teach it in a way that matches the job.

Why companies struggle to offshore it

QA can often be documented, handed off, and run in parallel. Data engineering is harder to treat that way because it’s tied to the business heartbeat.

If a revenue pipeline breaks at 2:00 a.m., someone has to see it, understand it, and fix it fast. Time zone gaps get expensive. Communication gaps make it worse. When reporting is blocked, finance is stuck, or product data stops flowing, the damage spreads.

Garzon says he’s seen companies try to offshore this kind of work and fail. The issue isn’t that offshore engineers can’t do good work. The issue is that core business data systems often need tight feedback loops, clear ownership, and people who can respond when things go sideways.

Why engineering rigor matters here

This is also why QA and software backgrounds stand out. QA people think about edge cases, bad inputs, and failures. Software engineers think about scale, architecture, and production bugs. Analysts often know the business side well, but they may not bring the same engineering habits.

Put those two sides together and you get a profile companies want: someone who can build data systems and keep them stable.

If you want a broader high-level view of how the two career paths compare, this data engineer vs. software engineer comparison gives a useful baseline.

The resume rewrite can change who calls you back

A lot of people don’t have a skill problem first. They have a framing problem.

Hiring managers don’t know what you meant to say. They read what’s on the page. If your resume sounds like QA only, or generic software work, they won’t fill in the blanks for you.

Before and after language that reads better

The work can stay the same. The wording changes.

• “Maintained automated test suites” becomes “Designed and maintained automated data quality frameworks validating 50 million records daily.”
• “API testing for payment processing systems” becomes “Built Python-based data extraction pipelines integrating payment API endpoints.”
• “Logging and monitoring” can be described in terms of pipeline monitoring and data observability when that’s what the work supported.

That isn’t dishonest. It’s the same work, described through the lens of the role you’re trying to get.

Garzon gives one student example where this shift changed everything. After rewriting her resume in data engineering language, three companies reached out within two weeks. She updated her LinkedIn next, and more inbound interest followed.

This weekend, rewrite three resume bullets through a data engineering lens, then apply to two entry-level or mid-level data engineering jobs to test the response.

Don’t wait until you feel ready. Use the application process to show you what’s missing.

Waiting gets expensive fast

The cost of doing nothing isn’t zero. That’s one of the hardest truths in this whole conversation.

If your role is shrinking, your market value can shrink with it. You don’t stay in the same place by waiting. You move backward while the market gets tighter around you.

Garzon uses a simple example. Say there are 10 openings and 100 people who can do the job. That’s 10 applicants per opening. Six months later, maybe there are only five openings and 200 people chasing them. Now it’s 40 applicants per opening. Your odds didn’t get a little worse. They got much worse.

Then salaries start slipping too. More supply, less demand, less pay.

He shares one story of a person who came from QA and went through three layoffs in four years. Roles that once paid around $150,000 turned into a contract job paying $70,000, with no second offer and no real room to expand. Same person. Same effort. Worse boat.

That’s the image to keep in your head. The boat matters. Rowing harder in a shrinking market doesn’t fix the market.

Fear is the other hidden cost. People worry about changing roles, joining a startup, taking on more responsibility, or falling short in interviews. That’s real. But Garzon’s point is worth sitting with: how expensive is it to let that fear keep making the decision for you?

Your current employer has no reason to tell you to prepare for your next move. They paid to hire you. They’d rather keep you where you are, right up until they don’t.

Final thoughts

The safest career move usually doesn’t feel urgent when you still have a paycheck. That’s why so many people wait too long.

If you’re in QA, software engineering, IT, or a nearby tech role, moving into data engineering is often a redirect, not a reset. The missing pieces are learnable. The resume can be fixed. The timing matters most.

Training to land a high-paying data engineering role is a practical next step if you want help turning your current experience into interviews before the market gets tighter.

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