Data Engineering AMA: Real Questions, Real Advice for Breaking into Data Careers

Thinking about a career in data? Not sure if you should go for data engineering, data science, or something else entirely? If you’ve got more questions than answers, you’re in the right place.

Every week, I participate in a live Ask Me Anything session with data-curious professionals and career changers who want direct answers. People join us from diverse backgrounds: some are IT veterans, some are recent graduates, and some are looking to make a significant change. Everyone gets a chance to ask what’s on their mind—no matter how basic or complicated.

Below, you’ll find key takeaways and honest advice pulled straight from one of our recent AMA sessions. We’ll cover common roadblocks, salary talk, and how to go from “curious” to “hired.”

What’s an AMA Session, and Why Do We Host These?

A weekly Ask Me Anything (AMA) is exactly what it sounds like. You show up, jump in the chat, and ask whatever’s on your mind. We tackle everything—no scripts, no filters.

Here’s why these calls matter:

• You get answers straight from the CEO and coaches at Data Engineer Academy.
• They’re live, and they’re open. If you want the real dirt on salaries, job prospects, or what skills you need, just ask.
• We set aside 45 minutes so everyone gets a shot. People drop questions in chat, and we work through them in order.

But there’s more to it. The AMA is a key piece in our process with potential students. We don’t just accept everyone interested. When a prospect makes it past the first screen, the AMA helps them figure out if we’re a true fit for them and vice versa. Around 70% of people who show interest get told, “This isn’t quite right for you.” Harsh, but honest.

If you’re thinking, “I want a shot at one of these calls,” you can book a call to join the next AMA. No pressure. Show up, ask, and see if it’s relevant for you.

Career Transition Challenges: Real Talk on Technical Project Management

Some folks join the AMA with decades of experience. Like Rich, who spent 37 years in IT, the last 15 managing big projects and migration efforts. He wanted to break into a technical project manager (TPM) role—ideally without getting bogged down in stuff he’d never use.

He faced something a lot of career-changers do: he knew tech, he knew the business, but he hadn’t learned the newest pieces like Python or recent cloud tools. Here’s how we tackle questions like his.

What Matters for TPMs in Data?

There isn’t just one kind of TPM job. Some TPMs focus on data teams, others run software projects or infrastructure. For data-focused TPMs, the most important skills include:

• SQL: You don’t have to build advanced systems, but you should understand joins and how to pull reports.
• Python: Basics are helpful, not required. It lets you “speak the language” with developers.
• Cloud (AWS or Azure): Real-world familiarity. If you’ve done cloud migrations or taken vendor courses, you’re ahead.

Even if you’re not going to code every day, you need to be fluent enough to lead a team, make technical decisions, and understand the workflow.

Shortcuts for Experienced Pros

Thirty years in tech? You’re not going to start all over. We personalize study plans so you spend just enough time on each area to get the job done. Most seasoned folks need a couple of months to get familiar with what’s new, not years.

Key skills for emerging data TPMs:

• SQL basics (reporting, error troubleshooting)
• Cloud fundamentals (especially migrations)
• Understanding system design for data
• Enough Python to communicate, but don’t sweat advanced problems

Realistic Job Search: It’s a Numbers Game

Let’s be honest: right now, finding a job in tech is tough. It’s not about being perfect for every job; it’s about getting in front of enough opportunities. We spread a wide net, often hundreds of applications, to land the interviews that count. That’s why having a team apply for you saves time and hassle.

Coaching, One-On-One Help, and Who It’s For

Want to work with the CEO directly? The Diamond coaching package is built for advanced folks who want that personal touch—monthly calls, targeted prep for TPM scenarios, and hands-on help passing those “harder” leadership rounds. If you know your stuff but want a boost over the finish line, this is it.

Picking Your Path: Data Engineer, Data Scientist, or Data Analyst?

What if your background isn’t pure computer science—or even close? Amin, for example, came from biomedical engineering. He’d done data work, but worried he’d need a PhD to land as a data scientist.

Here’s the reality check for full transitioners (those making a big switch):

• Data science jobs are in demand, but companies want advanced credentials—often PhDs or industry-specific deep experience.
• Data analyst and data engineer roles are much more open to people switching over from other fields, even if they’re learning on the job.
• Many folks start in analyst or “business intelligence” (BIE) roles, then make the jump to data engineering or data science after a year or two in the field.

Yes, you can learn both data science and data engineering basics together. But when it’s time to hit the job market, playing to your strengths is smarter: target analyst and engineering roles where your transition actually has a higher chance of success.

What does this mean in the real world? Let’s say you get hired as a data analyst at Amazon. You’ll have chances to touch both data science and data engineering projects while getting real pay, not student rates.

If you’re coming from a field where salaries are capped, don’t chase data science just because it sounds cool. You’ll get further by breaking into analyst or engineer roles, then growing your skillset.

We tell folks to shoot us a private message or DM with their salary goals. That way, you get targeted advice for your specific situation.

Owning Your Doubt: What Keeps People from Succeeding?

Most people who show up to the AMA have some fear: “What if I don’t succeed?” or “Is this legit?” Here’s the deal—it’s not about doubting the program; it’s about doubting yourself.

Imagine you hire a personal trainer. If you don’t show up, you won’t get fit. It’s the same here. We give people the tools, support, mock interviews, and job application help they need. But if someone “ghosts” us—stops showing up, doesn’t follow the process—their odds go down.

Quick Facts

• 80% of our participants either land jobs or keep progressing with us.
• 20% drop or disappear. Usually, that’s from not showing up or not following through.

When you join, you’re not just paying for content. You’re becoming a partner—we work together until you reach your goal. We’re in your corner until you land the job.

Salary Talk and Interview Skills: The Truth About Getting Hired

You want to know actual numbers? Skip the hype and do your research. Look up threads on Reddit or Blind to see what people earn.

Typical base salary for a new data engineer: $130,000 to $160,000 a year, straight out of school or after a successful transition. Add in stock grants or bonuses, and the number can grow fast.

What’s Really in a Paycheck?

• Base salary: The number you see on your offer letter.
• Stock/equity: Many tech companies offer shares that grow over time.
• Bonuses: Yearly or performance-based.

Often, people underestimate how much stock matters. Down the road, equity can boost your total pay well beyond your base—the difference between a $170K and a $300K package.

Behavioral Interviews Matter

Many think you just need to pass coding interviews. The truth is, about a third to half of all rounds are behavioral. You have to show you can work with others, communicate, and solve problems as a team.

If you’re a strong coder but not great at telling your story, you need practice. That’s where group calls, mock interviews, and targeted coaching make a difference.

You won’t get hired just for skills. You get hired because the company believes you’re the right fit.

Data Engineer, ML Engineer, or Data Scientist? Know the Difference

It can get confusing. Here’s how to sort it out:

• Data Engineer: Builds data pipelines and infrastructure. Example: moving billions of records every day for reporting or apps.
• Machine Learning Engineer: Takes ML models and makes them usable at scale. Example: setting up systems that serve recommendations to millions of users.
• Data Scientist: Finds insights and builds basic models for business decisions. Often needs deep statistical chops or advanced degrees.

If you like the idea of building the “roads and bridges” of data, go data engineering. If you want to design and run algorithms for massive datasets, think ML engineering (though we don’t offer a direct ML engineering program right now). If your passion is digging into numbers and solving puzzles, data science or analytics may suit you better—but be realistic about entry requirements.

If you’re already in a company with internal data teams, see if you can move over—some companies have resources or budgets for training, and learning on the job saves you time and money.

How Long Will It Take Me to Land a Data Job?

One of the top questions: “How long before I can get hired?” Here’s the honest answer: it depends on you.

If you’re a full-time student, starting from scratch, or doing a total career change, you can move quickly by putting in steady effort. Start small. Try 30 minutes or an hour a day until the habit sticks, then slowly ramp up. The biggest mistake newbies make? Overcommitting, getting tired, and dropping off.

If you already know SQL, you can be job-ready in about three months at four to five hours a day. If you’re learning from zero, give it three to six months. Learning “just enough” Python and cloud tools—no need to do everything—gets the job done.

Don’t chase hot topics like AI just to say you did. AI is cool, but it’s not what will land you the job interviews (yet). Nail the basics first, and you’ll be prepared for the real interview questions.

Thinking of Switching from Product or Business Analysis?

If you’ve been in product management or business analysis for years, you might feel stuck. You know the business side, but maybe you want to do something more technical or hands-on.

We get these questions a lot. Here’s how we break it down:

• If you don’t know SQL or Python, start there. Learn the basics, try some analytics or reporting projects.
• Don’t commit to an expensive, full program until you know you actually like it. Try our entry-level package first or work through free YouTube courses to see if it fits.
• Once you pick it up, you can move toward product analyst, marketing analyst, or data analyst roles—all open doors for someone with your background.

Don’t let decision paralysis hold you back. The worst thing you can do is do nothing and let time pass. Try small, see if you like it, and then step up if you want more.

Consulting, Contracting, and Value—Not Just Hourly Work

Some folks want to consult or contract, not take a “regular” job. Yes, we cover that too. Whether you want freelance gigs, corp-to-corp projects, or run multiple contracts, we help with:

• Finding the right job boards or recruiter channels for contract work
• Advice on avoiding hourly gigs (where companies can start micromanaging your time)
• Negotiating value-based pricing so you get paid for results, not just hours worked

Just remember, direct employment often brings perks like stock and equity options that can mean huge gains over time. Weigh your options, do your research.

Final Thoughts: Information is Power, but Action Gets Results

You can ask every question under the sun, but at the end of the day, getting hired comes down to action. Show up. Do the basics well. Practice telling your story. Apply widely. Let experts help you.

If you want to be on the next live Ask Me Anything session, book a call to join us. Already know you want to dive into learning? Explore our full range of course options.

And if you still have questions, drop them in the comments or send us a DM. Someone’s always there to help.

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