I’m not particularly interested in polished success stories. You know the kind: a straight line from point A to point B, no doubts, no detours, no moments of “maybe this was a bad idea”. Real career changes rarely look like that.
That’s exactly why my conversation with Katarzyna Zielina stayed with me for a long time.
From the outside, her path might look almost textbook. Today she works with data, moves comfortably between SQL, Python and Spark, operates on large datasets, and builds her own educational presence online. But the longer we talked, the clearer it became that this wasn’t a story about one good decision or one perfect course.
It was a story about process. About testing yourself in practice. About doubt, frustration, patience and curiosity. And about discovering flow not through motivation quotes, but through real work.
This article is a structured, edited version of that conversation.
Before Data: People, Hotels and Constant Motion
Before data entered her life, Katarzyna worked in hospitality. Hotels, events, people, constant dynamics. Every day different, every problem slightly unexpected. A job where empathy, quick reactions and emotional intelligence matter more than perfectly structured processes.
As she put it herself, after hospitality “nothing human surprises me anymore”. That experience gave her a thick skin, strong communication skills and a deep understanding of people.
But over time, something started to feel off.
Not because hospitality was bad. Not because it was toxic or unbearable. It simply stopped being hers.
She noticed that the moments she enjoyed most were the quieter ones. Working with numbers. Building spreadsheets. Analyzing performance. Connecting dots. Excel was already part of her work, and earlier studies included financial analysis and plenty of work with data.
Those were the first signals. Nothing dramatic. Just a growing awareness that she felt better working deeply with numbers than constantly reacting to people and events.
Treating Career Change as a Project
One of the most important things that came out of our conversation was how intentionally she approached career change.
There was no “I quit everything and go into IT” moment.
It started with a very pragmatic question: where are the jobs that allow remote or hybrid work? COVID only accelerated that thinking. IT naturally appeared as an industry offering flexibility, stability and scale.
Then came research. Webinars. Interviews. Meetups. Conversations. Observing not only technologies, but also people.
That part is often overlooked. Katarzyna didn’t choose her path solely based on tools or salaries. She paid close attention to who works in specific roles, how they communicate, how they think, and whether she could imagine herself among them.
She considered many options. Marketing. Project management. Frontend. Backend. Systems engineering. IT support. Nothing was ruled out upfront.
Data analytics emerged gradually. Not because it sounded impressive, but because it aligned best with her skills, preferences and energy.
The Hard Parts No One Likes to Talk About
The transition wasn’t smooth.
The first major challenge was going back to an entry-level role after years of being experienced in another industry. Becoming a junior again. Asking questions. Making mistakes. Feeling incompetent in areas where others seemed fluent.
That’s emotionally hard. It hits the ego.
The second challenge was financial. Career change often means a temporary downgrade. That reality had to be accepted and planned for, not ignored.
The third challenge was specialization. From the outside, IT looks like one world. Inside, it’s dozens of roles, paths and technologies. Without hands-on work, it’s easy to feel lost.
Only real projects helped her understand what she actually enjoyed doing versus what only looked good on paper.
Flow Doesn’t Come at the Beginning
One of the most powerful moments in her story was her first real experience of flow.
Not during theory. Not during the first project. It happened during the second or third one.
She described a Saturday when she started working in the morning and suddenly realized it was already 6 p.m. She hadn’t eaten. She hadn’t checked the time. She wasn’t exhausted in the familiar, draining way.
It was just her and the data. Problems unfolding step by step. A clear sense of direction.
That was the moment she thought: “This is a good sign.”
Flow didn’t appear immediately. It appeared when she had just enough skills to move forward, but still enough challenge to stay engaged. That’s an important lesson for anyone starting out.
Python, Frustration and the AI Temptation
It wasn’t all smooth after that either. Especially at the beginning with Python.
Errors that now seem trivial could block her for hours or even days. There were moments of doubt, thoughts of quitting, and comparing herself to others.
Interestingly, she was learning at a time when tools like ChatGPT already existed. And she deliberately avoided using them at the start.
Her reasoning was simple: if you want to build a real skill, you need to experience the errors yourself. You need to struggle. Only then does the knowledge become yours.
Today she uses AI as a support tool, not a replacement for thinking. It speeds things up, but doesn’t remove the need to understand what’s happening.
That distinction matters more than ever.
Being a Data Analyst Is Not Just Writing Code
Another misconception we talked about is the idea that data analysts spend eight hours a day writing SQL or Python.
In reality, a large part of the job is thinking. Designing solutions. Understanding business problems. Planning pipelines. Optimizing performance. Code comes later.
Katarzyna now works with large datasets, often using Spark. At that scale, intuition isn’t enough. You need to think about efficiency, order of operations, and computational cost.
At the same time, Excel hasn’t disappeared. It’s still incredibly useful for quick checks, validations and small datasets.
Tools are just tools. Knowing when to use which one is part of the craft.
Working With the Business and Soft Skills
A significant part of our conversation focused on working with business stakeholders.
In simple terms: analysts have data, business teams have questions. The problem is that they often speak different languages.
A data analyst’s job is not just to calculate something correctly, but to understand what the business actually needs. Sometimes even before the business can articulate it clearly.
Here, her hospitality background became a huge advantage. Empathy, listening, reading between the lines, anticipating needs. These aren’t “nice extras”. They’re core skills.
We also talked about introversion. Katarzyna doesn’t see it as a blocker in data roles. Teams can be structured in a way that balances communication styles. And soft skills, to a large extent, can be developed if someone is willing to work on them.
Continuous Learning as a Feature, Not a Bug
In data, there is no “I learned it once and I’m done”.
Katarzyna talked about this openly and without frustration. She enjoys the constant learning. New tools. New approaches. New problems.
She plans her development, but stays flexible. Life happens. New opportunities appear. Some plans get postponed or reshaped.
That flexibility helped her during the transition and continues to help her now.
Work-Life Balance Without a Hard Border
One of the more interesting parts of our discussion was about work-life balance.
Katarzyna doesn’t feel the need for a strict separation between work and life. Not because she doesn’t rest, but because her work is part of her life, not something she needs to escape from.
If a new technology appears, she explores it out of curiosity, not obligation. And then she goes hiking, practices yoga or spends time outdoors.
This model isn’t for everyone. But it’s worth knowing that it exists and that it can work if the work itself is aligned with who you are.
Conclusion
This conversation reinforced something I’ve seen many times. Successful career change is rarely about one bold move. It’s about many small, intentional steps. Research. Projects. Reflection. Adjustments.
Katarzyna’s story isn’t a blueprint. It’s a map. It shows that you can come to data from very different backgrounds. That projects matter more than certificates. And that flow is often a better guide than external opinions.
If you found this article useful, share it. Especially with people who are considering a career change or are already somewhere in the middle of it. Sometimes one honest story is worth more than a hundred generic guides.
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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