Data engineering is one of those career paths that more and more people are aiming for when entering the world of IT. But what does a data engineer really do? What does their typical day look like? What skills are essential, and how has this role evolved in the age of cloud and AI?
I invited Darek Butkiewicz, better known online as Nieinformatyk, to answer exactly these questions. Darek is a popular educator on databases and a seasoned data engineer with years of experience. He shared his journey into data, gave us a behind-the-scenes look at his daily work, and offered insights into how this role is shifting in today’s tech landscape.
Below, you’ll find a structured and lightly edited version of our conversation, written in a friendly and accessible tone—while keeping the natural back-and-forth of a real dialogue.
The Origins of a Career in Data
Kajo: For those who might not know you yet—who are you, and what do you do?
Darek: I currently work as a data engineer at a company in the agriculture sector—we deal with agricultural data, oilseed processing, and more. My job is to build ETL pipelines that move data from various sources through the data warehouse to the final datasets used by analysts.
Kajo: So you’re in that dream role many people in data aspire to—technical, well-paid, and specialized. How did you get there?
Darek: It was definitely a journey. I studied management, with an IT-related specialization, but I only started getting interested in tech during the final year of my master’s. I wanted a remote job, good salary, and something mentally engaging. Programming sounded like a good bet.
From Call Center to Data Engineering
Kajo: Did you have any technical background at that point?
Darek: Not at all. I used to think HTML was a programming language. But I started learning. I had some exposure to databases at uni, so I dove into SQL. I borrowed books, watched tutorials, read blogs. After about six months, I landed my first job.
Kajo: Classic career switch—from a call center to IT. Sounds familiar.
Darek: Exactly. What also helped was getting into personal development. I started setting goals, learning how to learn. That mindset shift gave me a lot of motivation.
What a Data Engineer Really Does
Kajo: Fast forward to today—what does your day-to-day work look like?
Darek: It varies by project, but I mostly work on classic ETL processes. We gather data from various systems, clean it, enrich it, model it, and prepare it for analysis. Once my work ends, the analysts pick it up from there.
Kajo: What’s happening under the hood, between source systems and the final output?
Darek: First, we capture data changes—using CDC (Change Data Capture). Then the data is ingested into a warehouse or a data lake. We validate it, transform it, and model it using what’s often called a medallion architecture (raw, silver, gold). Finally, we load it into datamarts.
Tools of the Trade
Kajo: What technologies are must-haves in your work?
Darek: SQL, cloud (like GCP, AWS, Azure), and Python. That’s the core trio for data engineers.
Kajo: When you say “cloud,” what does someone actually need to know?
Darek: It’s more about concepts than clicking buttons. You need to understand what a bucket is, what ingestion means, what a data lake is. You may not be managing it hands-on every day, but you have to know the basics of how it works.
Why SQL Is Still King
Kajo: SQL is ancient by tech standards. Why is it still so relevant?
Darek: Because its purpose is still valid. SQL was built to make querying data easier and more human-readable. And it worked. Even today, it has a low barrier to entry and is incredibly powerful. New tech comes and goes, but SQL remains essential.
Where AI Fits Into the Workflow
Kajo: Has AI changed how you work?
Darek: I use ChatGPT daily—for learning or writing messages in English. I don’t often generate code with it—I’m usually faster writing it myself. AI doesn’t replace me, but it does speed things up and help with communication.
Good Practices in Data Engineering
Kajo: What best practices do you live by?
Darek: Documentation. It saves time for everyone and makes onboarding easier. I also care a lot about order and consistency—naming conventions, clean code, readable structure. And I believe in learning multiple systems. Specializing is good, but broader knowledge gives you flexibility and confidence.
Is AI a Threat or a Tool?
Kajo: People say AI is ending our careers. What’s your take?
Darek: I don’t think so. As long as you stay up to date, you’ll be fine. Sure, some roles will disappear, especially if someone refuses to evolve. But tech is full of opportunity if you keep learning.
Working with Analysts
Kajo: How’s your collaboration with analysts?
Darek: It’s crucial to have clear specs. Sometimes I get vague tasks like “just pull that data,” and I don’t even know the goal. Communication is key—on both sides. Analysts need to understand the business, and engineers need to understand what’s actually needed.
Final Thoughts: Is Data Engineering Worth It?
This conversation reminded me just how fascinating and multidimensional our field is. Darek’s story proves you don’t need to start with a tech background to become a successful data engineer—you just need curiosity, drive, and a smart approach to learning.
Whether you’re just starting out or considering a pivot into data, hearing from someone who’s done it—and is still excited about the job—is always refreshing. Data engineering is more than code and pipelines. It’s about solving real problems, collaborating with teams, and building systems that power modern decision-making.
And it looks like it’s going to stay that way for a long time.
Prefer to read in Polish? No problem!
Other interesting articles:
- How to become a data analyst? Complete guide
- GPT-5 vs Gemini 2.5 – A Big Test of AI Models in Analytical Tasks
- Power BI is not Excel. A conversation on implementation, Copilot and training with Iga Kwiecińska
- How to Become a Data Analyst with No Experience? Izabela Witczak’s Real Story
The article was written by Kajo Rudziński – analytical data architect, recognized expert in data analysis, creator of KajoData and polish community for analysts KajoDataSpace.
That’s all on this topic. Analyze in peace!
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