Breaking into data engineering can feel like a chicken-and-egg problem: every job advert wants experience, but how do you get experience without a job?
The good news is that employers hiring junior data engineers aren’t expecting you to walk in with five years under your belt. What they do look for is proof that you can think like an engineer, work with real tools, and pick things up fast. And that proof comes from three places: your projects, your GitHub, and your cloud exposure.
We built our Data Engineering, AI & Machine Learning Bootcamp around exactly this, so let’s break down what hiring managers look for, and how you can show up prepared.
1. Projects: show, don’t tell
Anyone can put “Python” and “SQL” on a CV. What employers want to see is what you’ve built with them.
A strong project tells a hiring manager several things at once:
- You can take a messy, real-world problem and turn it into a working pipeline
- You understand how data actually flows: collected, stored, transformed, and made useful
- You can make decisions and justify them (“I chose this approach because…”)
This is why our bootcamp gets you working with data from day one, rather than saving all the “real” work for the final few weeks. By the time you reach your group project in the last two weeks of the bootcamp, you’re already used to coding, pair programming, and problem-solving. This means you’re ready to work on your project in a team to build a realistic data application, using Agile ways of working, Kanban boards, stand-ups, and proper collaboration on Git. That’s the same rhythm you’ll walk into on your first job.
When you’re job hunting, a couple of solid, well-documented projects will do far more for you than a long list of tutorials completed. Employers want evidence you can build something end to end, not just follow along.
2. GitHub: your public track record
Your GitHub profile is often the first thing a hiring manager actually looks at, sometimes even before your CV even gets a proper read. It’s your working history, out in the open.
Here’s what they’re checking for:
- Consistency: regular, meaningful commits, not one giant upload the night before applying
- Readable code: clear structure, sensible naming, and comments where they help
- Good practice: tests, a decent README, and evidence you didn’t just copy-paste a tutorial
- Collaboration: pull requests, branches, and code review, showing you can work as part of a team, not just alone in a bedroom
Git and GitHub are woven through the whole bootcamp. From the Python Fundamentals weeks right through to the group project, you’re committing, branching, and reviewing code the way real engineering teams do.
3. Cloud exposure: where the data lives
Something many beginners miss is that data engineering today involves cloud engineering. Almost every employer runs their data infrastructure on AWS, Azure, or GCP. If your only experience is running scripts on your own laptop, that’s a real gap.
Employers want to see that you understand:
- How to deploy and run data applications in a cloud environment
- The basics of infrastructure-as-code and CI/CD, so you know how code gets from your machine into production
- How orchestration tools schedule, monitor, and troubleshoot jobs in something closer to a live setting
Our curriculum tackles this head-on over our Cloud Engineering and DevOps block, where you work with AWS, the market-leading cloud stack.
From there, you build on that foundation with ETL, orchestration, and data modelling. Then you’ll learn to schedule jobs, make them observable, and optimise them, just as you would in a real workplace. By the time you’re job hunting, cloud experience is something you can discuss with ease in a job interview.
The common thread
Projects, GitHub, and cloud exposure aren’t three separate boxes to tick. They’re all evidence of the same thing: can you do the job? Employers hiring juniors know you won’t have all the answers on day one. What they need to see is that you can learn, build, collaborate, and ship something that works.
That’s the whole point of the bootcamp. You’ll learn Python, SQL, servers, and AI and machine learning. In practice, that means you leave with a GitHub history, real projects, and genuine cloud experience that employers actually recognise.
If you’re ready to build the kind of portfolio that gets you noticed, take a look at our Data Engineering, AI & Machine Learning Bootcamp.