Building a career in AI is rarely a straight line. For many data engineers, the path is shaped by curiosity, self-learning, and a desire to solve real-world problems with technology.

That has certainly been the case for me.

In this post, I want to share my experience as a data engineer, how I found my way into AI, what the data engineer role looks like today, and why working at Marvik has been such a defining part of my career.

Finding My Way Into Data Engineering

My academic background wasn’t technical in the traditional sense. I studied Economics and Actuarial Science and spent my first professional years working in finance at Accenture. While the role made sense on paper, something was missing.

What really interested me was the intersection of statistics, mathematics, and programming. I started experimenting with automation at work, building scripts to replace repetitive Excel-based processes. That’s when I realized I enjoyed building systems far more than maintaining manual workflows.

At first, I explored machine learning because of its strong statistical foundation. But over time, I gravitated toward programming, automation, and data pipelines. Discovering tools like Airflow was a turning point. That’s when the data engineer role truly clicked for me.

Learning the Craft: A Self-Taught Career in AI

Like many people starting a career in AI as a data engineer, I didn’t follow a structured or expensive learning path. Most of what I learned came from YouTube, documentation, and real-world experimentation.

I constantly reviewed job descriptions to understand which tools companies actually needed. Instead of trying to learn everything, I focused on fundamentals:

• SQL
  
• Python (and PySpark)
  
• Data pipelines and orchestration
  
• Working with APIs

My advice is straightforward:

1. Start by solving a problem that genuinely interests you.
   
2. It doesn’t need to be super original or world-changing, most real-world work is about solving fairly standard problems anyway.
   
3. In my case, I pulled data from an online video game API and built a data pipeline around it.

When you’re just starting out, it’s less about expertise and more about being curious, asking questions, and wanting to get involved.

That practical, market-driven approach helped me land my first data engineering role and continues to shape how I learn today.

Here’s a few resources I’ve found really helpful:

1. freeCodeCamp has amazing, in-depth courses on almost anything: freeCodeCamp
2. Especially for Python, check out Tech With Tim.
3. For anything Airflow-related, Marc Lamberti is a must-follow.

Why Marvik Was Different

When I first interviewed at Marvik, what stood out wasn’t just the technical challenge. It was the people and the values behind the work. For the first time, “company culture” didn’t feel like marketing language, it felt real.

That impression only grew stronger once I joined. From day one, Marvik felt different not just technically, but on a human level. I found myself surrounded by people I genuinely wanted to learn from. Everyone brings something unique to the table, whether it’s deep technical expertise, strong product thinking, or simply a thoughtful way of approaching problems.

What I value most is how learning happens here. It’s not forced or hierarchical. It happens naturally through conversations, collaboration, and seeing how others think. You’re encouraged to ask questions, challenge ideas, and grow at your own pace, knowing there’s always someone willing to help.

Before Marvik, I never really bought into the idea of company culture. Here, it’s something you experience every day, sharing values, working with people you respect, and feeling supported. That environment makes a huge difference, not only in how much you learn, but in how motivated you feel to do your best work.

Obviously not all the time, but I’m not lying when I say that sometimes I’m genuinely excited for Monday just because I’m still in the zone from Friday.

For me, that’s been one of the most important parts of my journey as a data engineer.

Data Engineering With Real-World Impact

One of the most rewarding aspects of being a data engineer at Marvik is seeing how your work affects real users.

Some projects that stood out for me include:

• Improving product images for sellers on Mercado Libre, helping listings look more professional and appealing
  
• Automating the detection of doors in architectural PDF plans, saving countless hours of repetitive manual work
• Working on large-scale systems for one of the top tech companies in the world, where even small improvements can have massive downstream impact

These projects reinforced why I moved into AI and data engineering in the first place: to automate the boring parts, scale human effort, and deliver tangible business value.

What the Data Engineer Role Looks Like Today

The data engineer role has changed significantly in just a few years.

Today, it’s less about writing code all day and more about:

• Designing scalable data architectures
  
• Understanding business needs, not just technical requirements
  
• Connecting data engineering with DevOps, analytics, and machine learning

AI tools now assist with implementation, but they don’t replace understanding. The real value comes from knowing why a system should exist, not just how to build it.

A manager once told me, almost joking: “If AWS were to shut down tomorrow, I should be able to move everything elsewhere.”

To me, that’s a reminder that understanding the fundamentals, what’s going on behind the scenes, and why a system was built in the first place matters far more than being attached to particular tools.

For anyone starting a career in AI as a data engineer, my advice is simple:

• Master the fundamentals
  
• Don’t get overwhelmed by tools
  
• Focus on problem-solving, not hype

Growing With Marvik

At Marvik, data engineers don’t just implement solutions. We work closely with clients, challenge assumptions, and design AI systems that integrate into real business workflows. 

That mindset, combined with strong technical standards, is what makes Marvik an exciting place to grow as a data engineer. And we’re growing.

Join Us

If you’re a data engineer exploring your next step in AI, curious about working on real-world problems, or simply want to learn more about what we do at Marvik, feel free to reach out.

I’m always happy to chat, answer questions about the data engineer role, or share what it’s really like to build production-ready AI with our team. And if you’re looking for a place where your work has visible impact and the people truly matter, Marvik might be a great fit.

We’re hiring, and we’d love to meet you.

Explore our open positions and apply, or just start the conversation.