Dreaming of a high-paying data engineering job, but stuck on what you “need” to break in? You’ve probably heard a thousand things: you need a computer science degree, years of experience, a never-ending list of certificates… The list goes on. No wonder it feels confusing and overwhelming.

I’m Chris Garzon, founder of Data Engineer Academy and former data engineer at Amazon and Lyft. I went from $60K to $450K a year in five years. I’ve helped hundreds of students move from completely unrelated careers, like nursing or sales, into six-figure data roles—even at top firms. Why? Because I know most of what’s out there is wrong. It’s old advice or just plain nonsense.

Let’s bust the 10 biggest myths stopping you from your dream job. We’ll get honest about what hiring managers care about now, and how you can focus your time for better results.

Why Busting These Myths Matters

Most people believe you need to check every box before you even apply for a data job. A fancy degree, stacked experience, or a flawless technical resume. Here’s the hard truth: none of that guarantees you a shot at top pay. I’ve seen students with unconventional backgrounds blow right past CS grads, landing roles they used to think were out of reach.

Why is this happening? Because companies need real skills, business-focused thinking, and the ability to get results. Not more paper certificates or checkboxes. Industry moves fast. Sticking to old advice is costing you real opportunities and higher pay.

This post is for anyone stuck on out-of-date beliefs about data engineering. I’ll walk you through what matters and why—so you can stop guessing, quit worrying, and start moving toward your next big role.

Myth #1: You Need a Computer Science Degree to Get In

The CS Degree Myth Is Outdated

Let’s say it loudly: companies care about what you can do, not your diploma. Having a CS degree can be helpful, but it isn’t the key to a great data job. More companies want to know how you’ll solve their problems and add value, not where you went to college.

If you’re stressing over missing a CS degree, you’re wasting your energy. That outdated advice isn’t how Google, Amazon, or Meta hire anymore.

Real Backgrounds We’ve Seen Win

You might be surprised by who ends up in data engineering. Some examples:

• Consultants who are strong with clients and stakeholders
• Salespeople who know how business works and talk clearly
• Nurses and healthcare pros who can spot patterns and drive change
• Technical program managers (TPMs) who organize big projects
• Startup folks who jump in and solve problems fast

Communication skills matter a ton. Being that person who can translate data into decisions? That’s what raises your salary.

Show Real Business Results Instead

Stop chasing prestige. Focus on skills and results you can show. Build projects that solve actual business problems. Automate a real workflow. Clean up a messy data set and make it useful.

If you show how you make a company’s life easier, you become valuable, degree or no degree.

Myth #2: You Need Years of Data Experience for a Big-Paying Role

Experience Isn’t King

It’s easy to think you need a decade in the field before you can touch a big job, but it’s not true. These days, impact beats experience every time.

Employers want to know if you can save time, automate slow processes, or deliver insights that help them make money. Eight years at a bad job means less than six months doing real problem-solving.

How George Went From Non-Tech to $200K+

One student, George, had almost no technical background. He did side projects, put in extra hours learning key tools, and, most importantly, showed how he could add value to a real business.

How did George do it?

• He built side projects that solved real problems
• He documented the results and impact of those projects
• He practiced talking about his projects in practical, business terms
• He showed up to interviews ready to talk about value, not just code

Because George proved he could drive results, he landed an AI Lead role at over $200K. The takeaway? Don’t count your past years. Count the wins you can prove.

Show Your Impact—Here’s How

Do side projects. Volunteer for process improvements at your current job. Automate a workflow using Python or SQL. Bring use cases to interviews, and talk about the before-and-after impact you created.

Companies don’t want “time served.” They want proof you’ll make life easier, faster, or more profitable.

Myth #3: The Job Market Is Too Competitive or Unpredictable

Don’t Let Market Fluctuations Scare You

People use “the job market” as a catch-all excuse for waiting. When the market’s good, they stall. When it’s bad, they stall. If you keep waiting for the “perfect” climate, you’ll wait forever.

There’s always talk about too many candidates or too few jobs. The truth? Every week, students land interviews and jobs, recession or boom.

There Are Always Roles in Data

Data engineering isn’t going away. Companies keep hiring. At Data Engineer Academy, we see students getting interviews every week, even when the news says the market’s tough.

What gets results? A strong resume, real-world projects, and being ready for interviews. When you focus on those things, the market matters a lot less.

Take Action—Don’t Wait

Don’t stress about things you can’t fix. Control what’s in your hands:

• Build your skills with hands-on projects
• Polish your resume to showcase real skills and results
• Start applying—don’t wait for perfection
• Practice mock interviews to get comfortable

Getting stuck on the market is just another excuse. What matters is what you do, not what “the market” does.

Myth #4: Certifications Alone Will Get You Hired

Certifications Aren’t Enough

It looks good to have a stack of certs, but here’s the truth: certificates don’t equal job offers. A certificate shows you cared enough to finish a course, not that you’ll change a company’s bottom line.

If a certification costs $5, you can bet every applicant has it. Hiring managers know this, so it doesn’t help you stand out.

Show What You’ve Really Done

Stand out by walking into interviews ready to talk about the projects that matter:

• Why did you build that ETL pipeline?
• How did your work save money or time?
• What business decisions came from your dashboard?

Treat the interview like an acting audition—you’re showing, not telling, what you can do. Anyone can list certificates. Not everyone can prove they made a difference.

Don’t Rely on Common Certificates

Basic certificates are everywhere. Instead, focus on the skills and projects that set you apart. That’s what gets attention for high-paying roles.

Myth #5: Bootcamps Are Always Cheaper and Better

The Problem With Cookie-Cutter Bootcamps

Most bootcamps force everyone into a fixed timeline—usually 16 weeks—with the same curriculum for all. It doesn’t matter if you have 10 years of experience or three, you’re stuck on the same track.

This cookie-cutter plan just doesn’t fit real people with different needs, backgrounds, and learning speeds.

What’s Better: Personalized Support and Community

True learning happens when you get personal feedback, one-on-one coaching, and a peer group you can lean on. That’s how the top Data Engineer Academy students not just land jobs, but come back to mentor others.

Bootcamp Limitations:

• One-size-fits-all timeline
• Generic curriculum
• Lack of personal feedback

Personalized Program Benefits:

• Custom coaching and mentorship
• Real community support
• Flexible pace that matches your background

You Learn at Your Own Pace

Everyone’s journey is different. Learning should match your speed and what you need—some people just need a little push, others need time to close bigger gaps. Community and coaching make the difference.

Myth #6: You Need to Be Super Technical to Win in Data Engineering

What Most Data Engineering Roles Need

Here’s a secret: Most data engineering jobs ask for SQL and, sometimes, some basic Python. You don’t need to build the next Google search engine or know every coding language.

You need to know how to:

• Write solid SQL queries
• Use Python for automation if needed
• Understand the basics of data pipelines

Data Engineering vs. Software Engineering

There’s a big difference between writing production-level code every day and writing the SQL or automation scripts a data engineer needs.

What Data Engineers Need:

• SQL: querying and managing data
• Python: automating simple tasks
• Basic understanding of cloud tools

What Software Engineers Need:

• Deep knowledge of algorithms
• Building software from scratch
• Heavy coding every day

Focus Only On What’s Needed

Don’t overload your brain with every tech in existence. Master the basics, and build from there as jobs ask for more.

Myth #7: You Need to Know Everything and Be Perfect Before You Apply

The Market Is Your Best Teacher

Here’s where most people mess up—they don’t apply until they feel “ready.” The problem? You’ll never know your gaps until real companies give you feedback.

The market is your best teacher. Applying shows you what you need to learn.

The Power of Volume

Applying to just 10 jobs means nothing. It’s not enough data. To really understand what’s missing, you need to get out there and send more applications.

Here’s a simple approach:

1. Apply early, even before you think you’re ready
2. Apply as you build new skills and projects
3. Keep applying after you refine your resume and interview answers

This isn’t about being perfect. It’s about learning what the market wants from you, then leveling up as you go.

Keep Going, Keep Growing

The only way to build confidence, improve your pitch, and get real interviews is to keep putting yourself out there. The more you apply, the better you get.

Myth #8: Landing a Data Role Will Take Years

Why Systems Win Over Time

Some people think breaking in takes two years or more. Usually, those are the people who go it alone or enroll in two-year university programs.

If you’ve got a clear plan—or a system—and support, you move faster. Six months isn’t impossible if you do the right work at the right time.

What Does a System Look Like?

• Check your current gaps (skills, tools, interview readiness)
• Build a plan to fill each gap step by step
• Start applying while you’re still learning
• Do mock interviews that go beyond just coding: design, behavioral, and data case rounds

Many people only practice code, but companies care just as much (or more) about system design and problem-solving.

Quick Success Stories

Not everyone does it in six months, but many do. Invest the work, follow a repeatable system, and you’ll save months (or years) over going it alone.

Myth #9: Investing in Yourself Is Too Costly

Education Is an Investment, Not a Bill

Many people look at the price of education and freeze. But skills never go away. Pay for your learning, and you use that payoff every year you work.

Would you rather invest in the stock market for 10% gains—or in yourself, where the “interest” pays out in real, compounding salary growth?

ROI: Breaking it Down

If you pay $30,000 for a program and make $30,000 more your first year, you’re breakeven right away. But that doesn’t count raises, promotions, or jumping to even better jobs.

Example:

• Year 1: Make an extra $30,000
• Year 2: With raises/promotions, $50,000 extra
• Year 3: Even more, thanks to your stronger resume

You’re up $80K+ in a few years. That far outweighs what you put in upfront.

Shortcuts Cost You For Years

Going cheap now keeps you stuck. Paying to get unstuck pays you back much more. Don’t let price scare you if the outcome is a bigger, steadier paycheck.

Myth #10: “This Won’t Work for Me” – The Real Mindset Trap

Why “I Can’t” Keeps You Stuck

The biggest myth? Thinking this won’t work for you because you’re too different, too late, or too new. But it’s not about whether “it works”—it’s about whether you will make it work?

Let’s use the personal trainer example: if two people hire the same trainer and one loses weight while one gains it, it wasn’t the trainer—it was the effort.

Owning Your Results

Flip your thinking. Instead of asking, “What if it doesn’t work?” ask, “What if it does?” Even better, “How will I make it work?”

Here’s what makes the difference:

• Decide that you will make it work
• Stay consistent
• Ask for help when you need it
• Don’t quit when it’s tough

Your Mindset Is What Drives Results

Get honest about whether you’re doing what it takes. When you shift from doubter to doer, you start seeing progress—even if it’s slow at first.

What story are you telling yourself right now? If you change the story, the results change too.

Next Steps: Take Action and Choose Your Belief

All these myths are just that—myths. The top data engineers didn’t rely on degrees, experience, or luck. They chose to learn real skills, focus on results, and keep going long after most people quit. They saw investment as a path, not a cost.

Which of these myths did you believe before reading? What’s holding you back right now? Drop a comment and share your sticking point—I’d love to help you challenge that belief.

Ready to get serious about your future?

• Check out the Data Engineer Academy coursework to see how others are breaking in fast.
• Book a no-commitment call to talk about your own unique path at Data Engineer Academy: Book a Call.

You’re not stuck. Start with one action today, and you’ll be miles ahead of those waiting for the “perfect” moment.

Real stories of student success

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Student TRIPLES Salary with Data Engineer Academy

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DEA Testimonial – A Client’s Success Story at Data Engineer Academy