Top #5. Reasons to Learn Data Engineering in 2025

You’ve probably heard the term “Data Engineering” pop up more often in the last year or two. It’s showing up in job boards, career newsletters, and was one of the highest demanded courses from our community of over 500,000 tech professionals.

But why is everyone talking about it, and more importantly, should you actually learn it? (Spoiler alert but yeah, you probably should).

In this guide, I’ll walk you through the top reasons #4 why Data Engineering is becoming one of the smartest skills you can learn this year.

So let’s get into it.

Reason #1. The Data Engineering industry is growing incredibly fast

Unless you’ve been living under a rock, you’ve no doubt seen the huge growth in the AI industry.

However, the thing that most people don’t realize is that for AI to work, it needs reliable data flowing in the background, and that’s where Data Engineers come in. It's their job is to build the systems that collect, clean, and move data so it’s ready for real use. 

That might mean combining data from sales tools, customer platforms, and internal databases, or simply transforming that data into the right format so a machine learning model can use it. Otherwise models break, results are skewed, and teams waste time hunting down missing values or trying to stitch together broken pipelines. 

For example

• OpenAI’s model training pipelines rely on massive datasets with literally hundreds of terabytes of text, code, and media. This is why Data engineers at OpenAI use distributed tools like Apache Spark and Ray to clean, transform, and stream that data into usable training batches. Without that infrastructure, the models couldn’t be trained

• Spotify’s personalized playlists like Discover Weekly are driven by billions of user interactions. Data engineers use tools like Apache Beam and BigQuery to pull and process listening history so that recommendations can feel personal, even in real time

• Airbnb’s dynamic pricing and search ranking systems are powered by live data from bookings, events, and customer behavior. Their engineers use Airflow and Spark to combine and transform data across platforms, so their models always have fresh, clean input

This is why companies are hiring more Data Engineers right now, and why it’s being talked about so much.

It’s not just hype either. The global market for big data and data engineering services was valued at around $51.8 billion in 2024 and is projected to reach approximately , with a CAGR of about 13.3% during that time frame.

This is why everyone is talking about the role, and why companies are willing to pay big bucks to get Data Engineers on their team.

Speaking of which…

Reason #2. Data Engineering pays very well

According to Glassdoor, the average salary for a Data Engineer in the U.S. is , with top earners making well over $200,000, especially in major tech hubs like San Francisco or Seattle.

The thing is though, even entry level Data Engineers start out around $83,000 to $134,000 per year! 

Not bad right, but it gets better.

Reason #3. The job market continues to expand

You would think that with such a high salary these jobs would be super competitive, but the reality is that the demand far outweighs the number of trained Data Engineers in the market.

A quick search on LinkedIn job postings shows 5,680 open Data Engineer jobs in the US alone right now.

(And that's just one platform!)

Add in the fact that the global demand for Data Engineering is forecast to grow at a compound annual rate of over 30% for the next several years, and we have literally thousands more jobs coming down the pipeline (no pun intended).

Because it's not just AI that needs Data Engineers, but also industries like healthcare, retail, finance, education, and logistics. They all rely on clean, reliable data, and they all need the infrastructure to manage it.

And that’s where you come in.

You just need to pick up the right skills…

Reason #4. You don’t need to be great at math or have a CS degree to become a Data Engineer

One of the biggest misconceptions about data roles is that you need to be some kind of math genius to qualify. And sure, you might need some math for Data Science or Machine Learning research, but it’s not true for Data Engineering.

Why?

Simply because Data Engineering is more about systems thinking than math. 

It’s about understanding how data flows through a business:

• Where it comes from

• Where it’s going

• What format it’s in

• And how to make it useful

And yes, you need some technical skills but you don’t need a degree or to go to college to learn them. In fact, so many people in this industry are self taught or learned online via tutorials and digital courses.

So what kind of skills do you need?

You’ll need a base level of experience with tools like:

• SQL for querying data

• Then you’ll need Python for automating and transforming it

From there, you’ll need to learn more specific Data Engineering skills:

• Apache Spark to process large real-world datasets at scale

• AWS tools like S3, EMR, Glue, and Athena to build cloud-based data lakes

• Apache Airflow to orchestrate data pipelines and run workflows automatically

• BigQuery or Snowflake for storing and managing data in cloud data warehouses

• Kafka and Flink to build real-time streaming systems

• And finally, the ability to integrate AI and LLMs into your data pipelines

It might seem like a lot to learn but we teach all of this in just 3 courses here at Zero To Mastery inside of a single monthly membership.

You can start off with Python:

Beginner

Complete Python Developer in 2025: Zero to Mastery

32 Hours •346 Lessons

Learn Python from scratch, get hired, and have fun along the way with the most modern, up-to-date Python course on the web.

Andrei Neagoie

Then pick up some SQL:

Beginner

Complete SQL + Databases Bootcamp

26 Hours •293 Lessons

This SQL Bootcamp will teach you SQL, database management, and database design using real-world exercises working with all database types. No experience needed.

Mo Binni&Andrei Neagoie

And then take my brand new Data Engineering course:

New!

The Data Engineering Bootcamp: Zero to Mastery

14 Hours •165 Lessons

Master modern data engineering with Spark, Kafka, Airflow, AWS, and LLMs. Build real-time pipelines, data lakes, and production-grade ML workflows from scratch.

Ivan Mushketyk

It teaches you everything you need to become a Data Engineer step-by-step.

So that by the time you’ve finished, you'll know how to build end-to-end, production-grade data systems using the most in demand tools and get hired ASAP.

Reason #5. These skills are useful even if you are not a Data Engineer

The final benefit that most people miss, is that you don’t have to become a full-time Data Engineer to get value from these skills - because they complement other tech fields!

For example

If you’re a backend or full-stack developer, then chances are pretty high that you already work with databases, APIs, or logs. And so knowing how to pull data from different sources and get it into the right format can save you hours and help your team move faster.

The same goes for Data Scientists and ML Engineers because clean data is everything. If your pipeline breaks or the input is messy, your model won’t work the way it’s supposed to. That’s why so many companies want people who understand how data flows, not just how to use it.

So even if you're not planning to fully move into a Data Engineering role, simply by learning these skills it will give you an edge in your current job.

What are you waiting for? Learn Data Engineering today!

As you can see, Data Engineering is a fantastic career to move into. It’s in high demand, pays extremely well, and the market is predicted to continue to grow for the next decade - thanks to its interaction with AI and data backed systems.

So whether you’re looking to level up your current role or make a full career pivot, there’s never been a better time to learn data engineering.

Get started and learn this skill today!