Why Quitting Your Data Engineering Job Can Make You $1M+

The most expensive career move in tech isn’t taking a bad job. It’s staying in the same one for too long.

If you’re a data professional earning decent money already, it’s easy to think the downside is small. Maybe you miss out on $20,000 here or $30,000 there. But when pay increases compound over 10, 20, or 40 years, the real cost gets a lot bigger than that.

That’s the whole point behind Christopher Garzon’s argument, and once you see the math, it’s hard to unsee it.

The real cost of staying put

A lot of people think “doing nothing” means staying flat. That’s not true. If you stay at your company, keep your head down, and collect normal raises, your income still goes up over time.

That is the black line in Garzon’s example. You keep getting something like a 5% raise, year after year. Your salary compounds, but it compounds on a slower path.

For plenty of people, that path still looks fine on paper. You might go from $80,000 early in your career to $200,000 or even $300,000 later on. That sounds good, and in a vacuum, it is. The problem is that it hides the opportunity cost.

An economist would call this the cost of the road you didn’t take. In plain English, it means this: if the market would have paid you more, then staying underpaid is still a financial loss, even if your paycheck keeps growing.

That matters more than most people think because careers are long. If you’re 22, 30, or even 40, you may still have decades left to work. A small difference early doesn’t stay small. It stacks. Then it compounds. Then it turns into a gap that is hard to close later.

The biggest pay cut in tech is staying underpriced for too long.

Garzon’s point is blunt. If you have 10 or 20 years left in your career, the decision to stay still versus move on purpose can be worth millions. Not because one raise is huge, but because every later raise builds on the number you accepted before.

How your pay curve changes when you start moving on purpose

The easiest way to understand the argument is to picture four career paths side by side.

The big takeaway isn’t that one path is “good” and another is “bad.” It’s that every move changes the number future moves build on.

If you get a 20% bump by switching jobs, your next raise isn’t calculated from your old salary. It’s calculated from the higher one. Then the next company often anchors off that higher number too. That is where compounding starts to get serious.

Garzon calls it a waterfall. One jump spills into the next one. Then the next one. If you change companies every few years and negotiate well each time, you’ve created a much stronger pay base for the rest of your career.

The chart in his example ends with a difference close to $500,000 per year between the slow path and the aggressive one. Stretch that over a decade and you’re looking at a $5 million gap. That’s why the “I’ll wait a bit longer” mindset isn’t harmless. Waiting slows every future compounding event.

Upskilling changes what you can ask for

Upskilling matters because companies don’t pay more out of kindness. They pay more when your value is hard to replace.

If you learn new tools, own harder projects, and increase the scope of what you can do, you give yourself more room to ask for better pay. Sometimes that conversation happens with your current manager. Sometimes it happens with the next company.

The key point is that learning needs to connect to income. Otherwise, it stays academic.

A simple version looks like this:

• You learn a skill your market rewards, such as stronger SQL, Python, cloud, system design, or data modeling.
• You apply that skill in real work, not only in a course.
• You use that proof to negotiate a raise, promotion, or better offer.

That’s why raw learning hours aren’t the metric that matters. Market value is.

If you want a grounded view of how pay changes across levels, these 2026 data engineering salary benchmarks are a useful reference point. They help show what happens when your role shifts from task execution to ownership and broader technical scope.

A lot of people stop at step one. They learn. They feel productive. Then nothing changes because they never convert the new skill into a stronger job title, better interview results, or a new offer.

Job hopping resets your base pay

This is the part big tech workers often understand early, while a lot of other professionals don’t hear about it until much later.

Internal raises are usually conservative. External offers often aren’t. When you go to another company, you’re not boxed in by the same comp structure, manager budget, or promotion cycle that held you back before.

Garzon’s example uses a simple idea: when you leave, ask for more, often around 20% more, because switching jobs has to be worth it. Do that every 3 to 4 years and the compounding starts to snowball.

That doesn’t mean changing jobs every 11 months with no story behind it. It means moving with a reason and negotiating from a position of proof. A good outside summary of that tradeoff is this piece on when job hopping is a good thing. The short version is simple: random movement looks messy, but intentional movement tied to growth usually doesn’t.

This is also where timing matters. If you’re worth more now but wait six months to explore the market, you didn’t only lose six months. You delayed the next base reset, the next raise on top of that base, and the next jump after that.

Waiting six months doesn’t cost six months. It pushes every future increase further out.

That’s why the early moves carry so much weight. The sooner you price yourself correctly, the sooner future compensation starts building on a bigger number.

Equity is where the upside gets big

Salary matters. Equity can change the whole picture.

Garzon makes a point most people ignore when they think about compensation: base pay is only one part of the package. In the right company, equity is the multiplier.

He uses Nvidia as the example. Over the last few years, Nvidia stock surged, and that kind of move can take someone with ordinary-looking salary bands and pull their total compensation far above it. In his framing, a person with only a few years of experience might not need to wait 20 years for a huge jump if stock appreciation hits fast.

Of course, equity isn’t guaranteed money in the same way base salary is. Some stock packages disappoint. Some never amount to much. But if you’re only evaluating jobs on base pay, you might be ignoring the part that creates the biggest upside.

This is one reason the gap between the slow path and the aggressive path gets so wide. One person is collecting raises on salary alone. Another person is stacking raises, promotions, better offers, and stock. Those two careers don’t end up in the same place.

Garzon’s own story follows that pattern. He describes moving from roughly $60,000 at Amazon to around $200,000 at a startup, then about $350,000 at Lyft, with total compensation rising to around $450,000 when stock moved. He also points to peers who did even better, including a friend earning around $800,000 at Nvidia.

You don’t need those exact outcomes for the lesson to matter. You only need to understand that compensation is bigger than base salary, and the market usually rewards movement faster than loyalty.

Why smart people still lose money anyway

A lot of people don’t stay stuck because they’re lazy. They stay stuck because they’re busy doing things that feel productive but don’t change their market value.

Garzon calls out a pattern that’s common in tech: people spend month after month on Coursera, Udemy, YouTube, and other learning platforms, then wonder why their pay hasn’t changed. The issue isn’t learning itself. The issue is learning without a hiring plan.

If you spend a year collecting tutorials and still can’t turn that work into stronger interview answers, a better portfolio, a sharper resume, or a better offer, the result is negative. You spent time, effort, and money, and you didn’t move.

That’s harsh, but it’s true.

The market doesn’t pay you for how many videos you watched. It pays you for skills you can prove and outcomes you can sell.

This is also why passive delay is so expensive. A lot of professionals say they’ll revisit things in six months. Then six months becomes a year. Then another promotion cycle passes. Then another market window closes. By the time they finally decide to move, they’ve lost far more than one year’s raise.

If you’re trying to figure out whether the field still has room for this kind of upside, the answer is yes. The broader data engineering career outlook and salary trends still point to strong demand and strong pay in the US market.

The regret doesn’t usually come from taking a smart swing and missing. It comes from realizing you stayed in the cheap seat for too long.

Treat your career like an investment

One of the strongest ideas in Garzon’s message is that your career isn’t only a paycheck. It’s an asset.

That sounds obvious until you think about how most people behave. They budget their rent, save for retirement, and watch their monthly cash flow, but they don’t manage their career with the same seriousness. They don’t ask whether their role is compounding well. They don’t ask whether their current company is giving them real upside. They don’t ask whether the open market values them more than their employer does.

The people who learn this earlier tend to build far more wealth, not only because they earn more today, but because they put themselves on a stronger curve for the next decade.

This is part of why the pattern seems like a secret outside big tech. In places like Silicon Valley, people see co-workers leave and come back with 30%, 40%, or 50% increases. They see equity change lives. They see that the market often pays faster than internal promotion ladders do. In more traditional environments, many professionals never see that playbook up close.

The point isn’t to quit recklessly. The point is to stop treating your current job like the only place your value can be priced.

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