How to Become a Data Engineer & Get Hired in 2026

Every company in the world is collecting data. Sales data, customer data, usage data, financial data… You name it, someone’s tracking it.

But here’s the thing. Raw data is basically useless on its own. Before it can power AI models, business decisions, or product features, someone has to build the systems that collect it, clean it, and get it where it needs to go.

That someone is a Data Engineer. And right now, there aren’t nearly enough of them.

It’s a highly technical role with a seriously impressive paycheck to match, and in this guide, I’ll show you exactly what skills you need and the fastest path to getting hired.

So let’s dive in…

How to become a Data Engineer without a degree!

Just a quick heads up, but this guide is based on our Data Engineer career path:

In the future, feel free to check that and follow along as a fast-track cheat sheet. It also covers some additional steps you can take to move into more senior roles later on.

For this guide, though, we’ll focus on what you need to know to first get hired in this role.

Optional step. Speed up your learning

Because you’re going to be learning a lot of new skills, I recommend taking a slight detour and checking out this guide or, better still, this course:

Average time to learn: 5.5 hours

It will teach you how to learn using concepts you’ve never heard of before.

Why care?

Because it’ll help you learn faster, which will then reduce the total time it takes you to learn all these other skills you’ll need. (It’s kind of like stopping the car to fix a flat tire, because you know it will make the whole journey much quicker and smoother).

It’s important to understand that Data Engineering isn’t typically an entry-level role. Most people come to it having already worked as a Software Engineer, Data Analyst, or Data Scientist, so they have some of the key skills already.

That said, the steps below cover everything from scratch, so whether you’re starting from zero or just filling in a few gaps, you’ll find what you need here. But by picking up these efficient learning skills, you can start from zero and fast-track becoming a Data Engineer at a more accelerated pace.

Step #1. Learn the core skills

Alright, so time to learn the main skills. 90% of your time will be spent here, and as I said earlier, if you have some of these skills, feel free to jump ahead.

Learn Python

Python is the backbone of modern Data Engineering, with almost every tool, pipeline, and workflow you’ll encounter in this role being built in Python.

So, as you can imagine, it’s worth knowing, which is why we’ll start here:

Average time to learn: 60 days.

Don’t let that timeframe put you off. Python is genuinely one of the most beginner-friendly languages out there, and you don’t need to become an expert before moving on.

All you’re doing here is building a solid enough foundation to understand and write the kind of scripts and logic that Data Engineers use every day for processing files, manipulating data, automating tasks, and connecting systems together.

Once you’ve done that, it’s time to learn about handling data…

Learn SQL + Databases

As a Data Engineer, you’ll be querying, transforming, and managing data stored in databases constantly, and SQL is the universal language for doing that across almost every database system out there.

So this is what you need to learn next:

Average time to learn: 45 days.

Beyond just writing queries, you’ll also need to understand how databases are designed, how they scale, and how to keep them performing well as data volumes grow.

Why?

Well, these are the kinds of decisions you’ll be making on the job, such as choosing the right database for the right situation, structuring data so it can be retrieved efficiently, and making sure nothing breaks when the business doubles in size.

Once you’ve got that foundation, you’re ready to put it all together and learn what Data Engineering is actually about…

Learn Data Engineering

Alright, so now it’s time to learn the specific Data Engineering skills that you’ll need for this role (and pull together what you’ve learned so far).

You’ll need to learn:

The good news is I cover all of this in my Data Engineering course:

Average time to learn: 28 days.

So what does all this mean?

Well, you’ll learn how to build pipelines that stream data from thousands of sources simultaneously, storing it in cloud-based data lakes on AWS, and processing it at a scale that would make your laptop cry!

You’ll also learn how to use tools Apache Kafka, Flink, and Spark, which are the industry-standard tools that power data infrastructure at companies of every size.

And because AI is now baked into almost every modern data system, you’ll also learn how to integrate machine learning workflows and LLMs into production-ready pipelines. Something that a lot of employers are looking for.

Not bad right?

Once you’ve got all of that under your belt, there’s one more thing worth adding to your toolkit before you start applying for jobs…

Learn Python Automation

By now, you know Python, you know SQL, and you understand how data systems are built.

Awesome!

Now you could just stop here, but don’t.

Why?

Well, most people don’t realise, but a huge part of a Data Engineer’s job is repetitive by nature. Running the same pipeline checks, processing the same file formats, hitting the same APIs, etc.

That’s why I highly recommend that learn how to set up and build automations so you stop redoing the same things manually and start doing it at scale. You set up an automation, click a button, and it does it all for you!

Average time to learn: 32 days.

Not only will learning this make your life easier, but it’ll actually improve your work. Simply because you’ll be faster and remove the chance for human error.

Sounds like a win-win to me!

By this point, you now have all the core skills you need to start applying for jobs. However, there’s just one more thing you need to do first…

Step #2. Build a portfolio to prove you can do the work

Here’s the thing about tech jobs that catches a lot of people off guard, and it’s the fact that tech companies don’t really care about your qualifications.

What they care about is whether you can actually do the work, and the way you prove that before you’ve had the job is with a portfolio.

So before you start applying, you’ll want to make sure three things are in order.

• Your LinkedIn profile is up to date and looking professional

• You have a one-page resume ready to go

• And you have a portfolio of project work that shows what you can do

The good news is that fellow ZTM instructor Dan Schifano goes through each of these tasks in detail in his course on personal branding, so that you can get hired, as well as some other great tips to help you stand out even further.

Average time to learn: 1-2 weeks.

You’ll have completed some project work in the past 4 courses, so make sure to add them to your portfolio.

Step #3. Apply for Data Engineer jobs

Alright, now it’s time to apply for jobs and get hired!

If you’re a ZTM member, then I HIGHLY recommend you check out Andrei’s new course on getting hired at your dream job:

Average time to learn: 12 days.

He covers the entire application and interview process in detail, including his technique, where he gets a 90% interview success rate!

Trust me, you’ll never feel 100% ready, but if you’ve followed along so far, you are ready to start working in the real world. Also, don’t forget, you’ll pick up a lot of skills and experience simply by doing the job.

It’s not about having every single skill. It’s about having the right skills to get started, and you already have that, so start applying already!

Become a Data Engineer today!

So there you have it. The entire roadmap to becoming a Data Engineer within the next 7 months, or sooner, depending on how long you can dedicate each week.

Data Engineering is a great career to get into right now, with high demand, a great salary, and interesting topics to learn. Sure, it’s not the easiest thing to pick up as a beginner, as you’ll be learning a lot of different skills and tools, but it is achievable.

The trick is to just get started!

P.S.

Want some great news?

All of the courses that I mentioned in this guide for this role are included in a single Zero To Mastery membership. So once you become a member, you have access to all of these courses right away and will have everything you need in one place.

Plus, as part of your membership, you’ll get to join me and 1,000s of other people (some who are alumni mentors and others who are taking the same courses that you will be) in the ZTM Discord.

You can ask questions, help others, or just network with other Data Engineers and other tech professionals.

So what are you waiting for? Come join me and get started on becoming a Data Engineer today!