A Conversation with Dominik Szcześniak
Data analytics is usually associated with charts, dashboards, and colorful reports. With what is visible. With the frontend.
But the longer I work in this field, the more clearly I see that the real work often happens somewhere else. Deeper. In the background. In places the end user will never see.
That is exactly why I invited Dominik Szcześniak for this conversation. Dominik is the creator of the blog and podcast Dane są wszędzie and someone who has spent years working at the intersection of business intelligence, backend analytics, and data engineering. He started in finance, worked extensively with Excel and SAP, and gradually moved closer to the technical core of analytics systems.
We talked for a long time. About studies, career switching, backend analytics, Shadow IT, AI, learning, teaching others, common mistakes beginners make, and how not to lose your private life along the way.
What follows is a structured, editorial version of that conversation.
Are You an Analyst, a BI Developer, or a Data Engineer?
We started with a question that keeps coming back in the data world: how should we actually define our role?
Dominik was very clear. He does not see himself as a “classic analyst” focused on dashboards and visual storytelling. He feels much closer to a BI role, but not in the superficial sense. For him, BI is about building foundations.
His professional journey started in finance and accounting data. Excel, SAP, financial reporting, and automating repetitive processes. Over time, he became more interested in where the data comes from, how it flows through systems, and why reports sometimes contradict each other.
Today his work is largely end-to-end:
- extracting data from source systems,
- integrating external APIs,
- transforming and modeling data,
- preparing semantic models that other analysts can safely consume.
He still creates reports when needed, but that is not the core of his work. As he put it, he has a “backend soul”. He is more interested in the engine than in the paint job.
This distinction matters, because in many organizations these roles blur together. In one company, a BI developer focuses almost entirely on Power BI visuals. In another, the same title refers to someone building data warehouses and semantic layers, while others handle reporting.
Economics Studies and the “I Don’t Know What’s Next” Phase
We went back to Dominik’s student years. He studied economics and, like many people, did not have a clear career plan from day one.
His interests evolved during his studies. Some fields looked interesting on paper but quickly lost their appeal once he started learning them more deeply. Toward the end of his studies, paid internships funded by EU programs became available, which was an attractive option for many students.
The recruitment process focused on financial knowledge and basic Excel skills. Excel turned out to be one of the key differentiators.
Dominik was not a bookworm. He did not spend his life in the library, but when it was time to study, he did. His grades were good, but he never believed grades were a reliable indicator of future professional success.
What the studies gave him was something more valuable: a solid business foundation. Understanding finance, accounting, and business logic later became a huge advantage when working with data.
Excel as a Gateway to Data Work
One of the most interesting parts of the conversation was about Excel. Not Excel as a “beginner tool”, but Excel as a platform for building real solutions.
During his studies, Dominik took a logistics management course where, together with a colleague, he built something resembling a simple ERP system entirely in Excel. It was basic, but for him it was a revelation.
That was the moment he realized:
- processes can be automated,
- data can be aggregated meaningfully,
- tools can be used to solve real problems.
During his internship, these skills quickly proved useful. The tasks were not technically complex, but they allowed him to automate reports and save time for the team. That gave him a strong sense of satisfaction.
A key concept emerged here: agency. The feeling that you create something that solves a real problem and continues to be used even after you leave the team.
From Internship to Full-Time Role
After the internship, Dominik was offered a full-time position. He accepted and completed his master’s degree part-time at the same time. It was a pragmatic decision: no gap in his CV, immediate professional experience, and a smooth transition into the job market.
Initially, his responsibilities were still centered around finance, but more and more analytical and technical elements appeared. There was no single moment when he could say “from today I am an analyst”. It was a gradual process.
This is an important point. Career switching is rarely a dramatic overnight change. More often, it is a series of small steps that eventually form a coherent story.
Looking Back: Would He Change Anything?
I asked Dominik whether he would change anything in his career path if he could go back.
His answer was calm and grounded. He does not spend much time thinking about alternative scenarios. Even less successful experiences contributed to who he is today.
Business analysis, financial data, communication with stakeholders, backend analytics. Every stage added something valuable. Looking at his current professional satisfaction, there is no need to rewrite history.
The Backend of Analytics: Why Does It Exist?
This was one of the central topics of our conversation.
Analytics is often associated with the frontend because that is what people see. But the larger the organization, the more chaotic the data landscape becomes. More systems, more sources, more inconsistencies.
Backend analytics exists to manage that chaos.
Data warehouses, lakehouses, central repositories. The goal is to create a single source of truth. Data is collected from multiple systems, standardized, validated, and only then exposed for analysis.
Without this layer:
- each department calculates metrics differently,
- data silos emerge,
- Shadow IT grows,
- governance and security start to break down.
Backend analytics requires investment. It costs time and money. But at scale, it is unavoidable.
Shadow IT and Its Hidden Risks
Shadow IT refers to situations where business users build their own analytical solutions outside the official infrastructure.
On the surface, it makes sense. Someone needs a report urgently. The IT team has other priorities. So someone builds a workaround.
But the risks are serious:
- undocumented solutions,
- inefficient data processing,
- uncontrolled resource usage,
- security and compliance issues.
Shadow IT does not come from bad intentions. It comes from unmet needs. But over time, it can seriously damage an organization’s data ecosystem.
Is Backend Analytics “Real Programming”?
I asked directly whether backend analytics is closer to “real programming” or to clicking tools.
The answer was simple: it sits somewhere in between.
A BI developer or data engineer does not need to be an object-oriented software developer. But advanced SQL is already programming. So are:
- stored procedures and transactions,
- Python and Spark-based processing,
- API integrations,
- CI/CD pipelines,
- PowerShell and DevOps automation.
It is not drag-and-drop work. But it is also not classic application development. It is its own discipline.
Can a Frontend Analyst Move into the Backend?
Yes. But it requires effort.
Dominik himself transitioned from finance into IT. Many organizations support internal career moves like this, because analysts already understand data and business context.
The key ingredients are:
- curiosity,
- understanding core concepts,
- asking questions,
- willingness to learn beyond daily tasks.
Tools change. Concepts remain.
AI in Backend Analytics: Help or Threat?
AI naturally came up in the discussion. Dominik uses it mainly for:
- debugging,
- writing one-off scripts,
- speeding up operational tasks.
He does not believe in a scenario where a manager writes a prompt and a complete data warehouse magically appears. At least not yet.
AI accelerates work, but only if you understand what you are doing. Otherwise, it creates a dangerous illusion of productivity.
Many organizations want to implement AI without having clean, well-structured data. In such cases, AI cannot fix fundamental problems.
Teaching Others and Sharing Knowledge
Dominik has been sharing knowledge for years through blogging, podcasting, training sessions, and meetups.
What matters most to him is that he teaches as a practitioner. He understands real-world limitations, trade-offs, and tool constraints.
The biggest reward comes when:
- someone saves hours of work,
- a simple solution changes how someone works,
- a concept finally “clicks” for another person.
Teaching also forces you to organize your own knowledge. To face difficult questions. To say “I don’t know, I need to check”.
Common Mistakes Beginners Make
From his teaching experience, Dominik sees recurring patterns:
- Trying to learn everything at once.
- Lack of consistency.
- Focusing on tools instead of concepts.
- Working only with abstract, meaningless datasets.
- Expecting quick results.
Learning with your own data makes a huge difference. Personal finance, fitness data, anything that has real meaning.
Private Life, Relationships, and Mental Balance
The conversation also touched on life outside work. Dominik emphasized the importance of:
- relationships,
- support from close ones,
- having an outlet unrelated to work.
Not everything needs to be a passion. A hobby is enough. Something that clears your head, without pressure to excel.
Without that balance, burnout becomes almost inevitable.
Why “Dane są wszędzie”?
At the end, I asked why he runs his blog and podcast at all.
The answer was honest:
- the need to build something of his own,
- the joy of talking to interesting people,
- satisfaction from positive feedback,
- relationship building,
- personal growth.
It is a niche project. But a meaningful one.
Closing Thoughts
This conversation shows that data analytics is much more than dashboards and visuals. It is about processes, decisions, trade-offs, backend systems, people, and life beyond work.
If this article resonates with you, share it with others. It might help someone make sense of their own path in the data world.
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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