Data Professionals Are F*ing Delusional
Why Python, SQL, and a few Airflow DAGs won’t save your job search anymore.
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I’ve had the same conversation three times this month. One friend just launched a startup. One is job hunting. One has no idea what the hell he’s doing next. Different situations, same theme: it’s f*ing hard to find a job right now.
And every time we talk, I hear the same quiet fear hiding under the surface.
I know Python. I know SQL. I build Airflow pipelines. Why isn’t anyone hiring?
Here’s the part nobody wants to say out loud: most data professionals have the narrowest skillset in tech. They don’t see it, because everyone around them is equally narrow. When your entire world is DAGs, you start to believe DAGs are the world.
I didn’t notice the gap until I stepped back.
I spent years before data engineering writing code for physical devices, building reusable JavaScript components, designing REST APIs, messing with infrastructure, learning whatever language I found fun. Breadth wasn’t optional. It was the job.
Then I entered data and realized, it’s like walking into a room where everyone trained for one event and thinks they’re ready for the Olympics.
Python + SQL + Airflow used to be enough.
Now it’s a career starter kit, not a career.
The Pipeline-Only Mindset Is a Trap
If you’re honest for a second, your job probably lives inside a tiny technical box. You spend your days wiring up pipelines someone else requested, using tools someone else picked, to deliver definitions someone else wrote. You call it engineering, but most days you’re operating machinery.
Maybe that worked a few years ago. Companies were hiring fast, nobody knew what good looked like, and if you could build a DAG without lighting up PagerDuty, people called you “senior.” The market wasn’t evaluating talent; it was filling seats.
Today the world is different. Teams are smaller, budgets are tighter, and hiring managers want someone who can think past the next task. When the market shifted, a lot of data people got exposed. Not because they’re bad, but because the skill stack is narrow and the job stopped hiding it.
Here’s the uncomfortable part:
When you only build pipelines, you trick yourself into believing you’re progressing.
New operators, new connectors, new cloud services with the same concepts behind them. But that isn’t growth. It’s maintenance.
You don’t escape the pipeline box by getting faster at building pipelines.
What Software Engineers Understand That Data Engineers Don’t
If you spent your entire career in data, you might not realize how small your technical world is. Software engineers don’t get to hide behind one tool or one part of the stack. They can’t survive that way. And neither can you.
You’re competing with people who’ve been forced to understand how everything fits together, not just the one slice they happen to own.
Breadth Isn’t Optional
Ask a software engineer what they do and you won’t hear, “I’m the PHP backend person”. They’d get laughed out of the room. They jump between languages, frameworks, APIs, frontend quirks, infrastructure problems, and whatever else the job throws at them.
They don’t do it for fun. They do it because it’s survival.
Meanwhile you might feel “senior” because you know Airflow, dbt, or Snowflake. But that’s the bare minimum now. If your entire skill set fits inside one pipeline tool and one SQL dialect, you’re playing the smallest possible game.
Breadth isn’t some extra credit assignment. It’s the difference between getting hired and getting ignored.
Systems Thinking Over Tasks
Software engineers think in systems. They trace problems across the whole flow: user action, frontend behavior, API calls, backend logic, database writes, all the way down to infrastructure.
They can explain how their work fits into the bigger picture.
Most data professionals don’t do this. You might be stuck in task mode: pull data from A to B, clean column X, create table Z so dashboard Q doesn’t break. But if I ask you who uses the data, how it drives a decision, or what fails downstream, can you answer?
If you can’t explain the system around your work, you’re not an engineer. You’re a task executor.
And companies don’t hire task executors anymore.
The Harshest Gap: Zero Business Understanding
Here’s the part almost nobody in data wants to admit: most of the pain you feel in your career has nothing to do with tools or tech. It comes from not understanding the business you work for.
I’m not talking about being a “strategic thinker” or reading company OKRs. I mean the basics:
Who uses the data you bring in?
What decisions does it change?
What dollars or risks sit behind those decisions?
If you can’t answer those questions, you’re operating blind.
And this blindness shows up everywhere. You build pipelines without knowing why they matter. You model tables without knowing who depends on them. You fix bugs without knowing what breaks downstream. Analysts and data scientists often don’t know where the data comes from, and a lot of data engineers don’t know where it goes.
That’s the real gap. Not Python. Not SQL. Not dbt.
Context.
Because here’s the truth you already feel but don’t say out loud:
Your work has value only when it moves the business.
When you don’t understand the business, you don’t know your value. If you don’t know value, so you get passed over for raises, for promotions, for roles where ownership matters.
And it’s not because you’re bad. It’s because you’re disconnected.
Companies want engineers who think beyond their keyboard. People who understand the flow from idea to impact. People who can explain not just how something works, but why it needs to exist in the first place.
If your job feels fragile right now, this is probably why.
Why the Job Market Punishes This Narrow Skill Stack
If your job search feels impossible right now, it’s not because the market is broken. It’s because the market finally got honest.
Companies aren’t hiring “pipeline people” anymore. They don’t want someone who can build an Airflow DAG but can’t explain a data model, or someone who can write SQL but can’t reason about system design, or someone who can transform tables but can’t tell you who actually uses the output.
They want engineers who can design, build, test, deploy, and explain. Not people who do one slice of the work and call it a day.
And here’s the part nobody tells you: You’re not competing with other data engineers. You’re competing with full-stack software engineers who learned data on the side.
Those folks know how to debug systems, ship features, understand context, and own outcomes. They walk into interviews and talk about trade-offs, not tools. They can build an end-to-end product, not just a scheduled job.
Hiring managers love them because they reduce risk. They can be pointed at a messy problem and trusted to figure it out.
If your skill set is narrow, you can’t win that fight. Not because you’re worse, but because you’ve boxed yourself into the smallest part of the technical spectrum.
The market isn’t punishing you. It’s rewarding the people who understand the whole picture.
The Exception
There’s one group that gets to ignore everything I just said.
The only time extreme specialization pays off is when you’re a contractor who knows one technology so well that companies treat you like a surgeon. You know, the person they call when things are on fire and nobody else can fix it.
If you’re the person who can cut Snowflake costs by 70%, or scale a Kafka cluster that’s drowning under load, or untangle a dbt project that’s grown into a swamp, you don’t need breadth.
Your depth is the value. Companies will pay you obscene money for a very specific problem with a very specific pain point.
But here’s the truth most people don’t want to face: this path is rare.
You don’t become “the Snowflake cost killer” because you ran a few queries. You get there after years of context, experiments, failures, and scars. You’ve seen more edge cases than most teams even know exist. You understand the business impact behind the technical mess.
Most people aren’t specialists. They’re just narrow. And being narrow is not the same as being a world-class expert.
If you want this exception to apply to you one day, you need breadth first. You need to understand the entire system, not just the corner you enjoy. Then, if you choose to, you can go deep enough to become the person companies call when nothing else works.
But until you’re the person who gets called, you’re not the exception. You’re the rule.
What “Full-Stack Data Professional” Actually Means
Before you roll your eyes, I’m not talking about learning every tool under the sun or becoming some mythical “unicorn”.
I mean something much simpler: understanding the entire path your data takes, from the moment it’s created to the moment someone uses it to make a decision.
A full-stack data professional isn’t defined by tools. They’re defined by awareness.
Understanding the Entire Data Flow
You don’t need to be an expert in everything. You just need to know how the pieces connect:
How data is generated in the source system
How it’s ingested
How it’s modeled
How it’s transformed
How it’s stored
How it’s served
How it’s observed
How it breaks
And most importantly: how someone actually uses it.
You don’t have to build every layer yourself. But you should understand every layer well enough to talk about it.
Owning the Why, Not Just the How
If your answer to “Why does this pipeline exist?” is “Because someone asked for it”, that’s the problem. The people who stand out aren’t the ones who build the most. They’re the ones who understand why it matters.
Full-stack data professionals don’t wait for Jira tickets. They solve business problems.
They ask “What decision does this drive?” and “What metric does this change?” and “Who is depending on this?” long before they write a line of code.
Being Comfortable Across Tools, Not Married to One
You don’t need to master five orchestrators, three warehouses, and sixteen ingestion frameworks. But you do need to understand trade-offs.
Why choose Snowflake over Postgres?
Why choose Stream vs Batch?
Why choose dbt Core vs dbt Cloud?
Full-stack doesn’t mean everything. It means enough to make good decisions. And that’s what companies hire for: people who can think, not tool operators.
Final Thoughts
The biggest delusion in data isn’t thinking pipelines are enough. It’s believing you’re only allowed to understand your small corner of the system while everyone else gets to see the whole map.
Somewhere along the way, data professionals convinced themselves they’re supposed to stay in their lane. “I just ingest the data”, “I just build the models”, “I just maintain the DAGs”.
And after a few years of that, you start to believe you’re only capable of the thing you’ve been doing.
But that’s not true.
You’re not blocked by intelligence. You’re blocked by permission.
Nobody in your career is going to walk up to you and say, “Hey, you’re ready to understand the whole system now”.
You have to claim that on your own. You have to decide your value isn’t defined by the narrowest part of your job description. You have to give yourself permission to be more than the person who wires things together.
Because the moment you start seeing the whole flow, not just the slice you touch, everything changes. You stop feeling replaceable. You stop feeling behind. You stop feeling like the market is out to get you.
You become the person who understands the business, the systems, and the decisions.
And once you reach that level, job markets don’t scare you anymore. They need you.
Thanks for reading,
Yordan
PS: If you want to become indispensable, ask for more money without feeling like an imposter, and build a career that doesn’t collapse every time the market shifts, this is where I can help.