May 2026 is a truly exceptional moment for me. After more than seven years of working in the data world, including three incredibly intense years as a Data Architect, I am closing a certain corporate chapter of my life. I have decided to step away from full-time employment to dedicate myself completely and entirely to building KajoData. It is a time for deep summaries, profound reflections, and sharing knowledge that can genuinely change the trajectory of your career.
Whenever I publish new materials on my YouTube channel, I receive a massive wave of questions from you. They cover an incredibly wide spectrum of our daily professional lives. You ask about hard technical skills, the nuances of the recruitment process, the psychological burdens of the job, and the ever-looming shadow of artificial intelligence. I decided it was high time to gather the most recurring and fascinating questions, sit down, and give you comprehensive, honest answers. If you are wondering whether fluent English is the ultimate gatekeeper in IT, how to overcome the paradox of needing experience to get a job, or whether you can escape grueling physical labor at the age of thirty-six to sit behind a desk, then you are in the right place. Grab a cup of good coffee, get comfortable, and let us dive straight into the realities of the data analytics world.
The Myth and Reality of English in the IT Industry
One of the most frequent questions I get revolves around the English language. In many of my videos and articles, I heavily emphasize technical stacks—I talk about Python, SQL, Power BI, and data modeling. I mention languages less often, which naturally raises your concerns. You ask how this looks during the recruitment stage and whether that mythical B2 level, prominently displayed in almost every job posting, is an insurmountable barrier.
The honest truth is that English is absolutely necessary in this industry. There is no escaping it. However—and this is a crucial distinction—it is required on a completely different level than most people imagine. The golden B2 standard listed by Human Resources departments is often just a hollow corporate buzzword. Let me assure you: nobody in a tech company is going to ask you to present a language certificate, and no one will be standing over your shoulder with a ruler, measuring the grammatical perfection of your past perfect continuous tense.
What truly interests your future employer, your manager, and your team is the answer to two very simple, practical questions: can you communicate your thoughts, and can you listen with comprehension? Many aspiring analysts severely underestimate their own abilities. You likely possess a massive vocabulary derived from reading technical documentation, Stack Overflow threads, and software manuals, yet you freeze when it is time to speak. You must remember that modern IT companies are thoroughly international environments. You will be working with brilliant minds from India, Spain, Germany, Brazil, and dozens of other places. You will encounter a kaleidoscope of different accents, and I promise you, almost none of them will sound like the perfect, polished British English you hear in educational audiobooks.
Instead of stressing over official certificates, you just need to start talking. If you are preparing for a job interview, the best thing you can do is practice answering standard recruitment questions out loud, in English, in front of a mirror or a camera. The mere practice of articulating your thoughts and getting your vocal cords used to the language will significantly lower your stress levels and boost your confidence.
Battling Imposter Syndrome When the Code Refuses to Work
Someone recently asked me a very poignant question: “How did you deal with imposter syndrome? Did you look for solutions at home, ask others for help, or just disconnect and hope for a fresh idea the next day?” This question resonated with me deeply. Imposter syndrome—that nagging, persistent feeling that you are a fraud and that everyone is about to discover you actually know nothing—is an absolutely normal phenomenon in IT. It haunts juniors, it plagues mid-level developers, it shadows seniors, and yes, it even sits on the shoulders of data architects.
In my own analytical journey, the most important coping mechanism I found was achieving a state of extremely deep focus. I was never the type of person who immediately ran to senior colleagues for help the second I hit a roadblock. I have always felt that doing so robbed me of a crucial learning opportunity. What helped me the most was colliding head-on with the problem and thinking about it almost obsessively. I would lock myself in a mental bubble and search for the root cause.
Paradoxically, the actual solution rarely came to me during those moments of peak, intense concentration. Usually, the “eureka” moment struck when I finally let go for a second—when I went to make a cup of tea, when I left the office, or when I was taking a shower. However, I have a strong observation regarding this process: it was precisely that earlier phase of deep, obsessive focus that paved the way for the solution. Your brain needs to load all the variables of the problem into its working memory before it can process them in the background.
As I progressed to higher levels of technical seniority and tackled increasingly complex challenges, I noticed that I was becoming more resistant to imposter syndrome. Why? Because my own accumulated experience began to prove to my brain that it was misleading me. Even if a new, terrifying problem appeared and my initial instinct was, “I am definitely not going to figure this out,” I could look back and recall dozens of similar situations from my past. I had felt this exact same panic before, yet I always managed to find a way out. Your past achievements are the ultimate antidote to your present doubts. Experience builds psychological resilience.
The Natural Progression to Data Engineering and Role Blending
Another common dilemma is whether someone aiming to become a Data Engineer should first pass through the role of a Data Analyst, especially if they eventually want to move toward Machine Learning. I hate to use the most cliché answer in the IT playbook, but: it depends.
It depends heavily on your background. If you already possess a strong technical foundation—perhaps you have coded before, you understand software engineering principles, or you have a deep grasp of database administration—jumping straight into a Data Engineer role is entirely feasible. However, if you are completely new to the tech world and lack that foundational experience, attempting a direct leap into data engineering will likely be an incredibly painful and frustrating experience.
It is also crucial to recognize that roles in modern IT are increasingly overlapping. The rigid boundaries of the past are dissolving. Data Engineers are frequently required to understand the analytical business context of the data they move, while Data Analysts are increasingly taking ownership of the data pipelines that feed their dashboards. This convergence has given rise to the highly popular role of the Analytics Engineer. It is a fantastic career path that flirts heavily with both software engineering practices and data science methodologies.
I constantly remind my audience that a vast number of people can become successful data analysts because they are already analyzing data in their current jobs without even realizing it. Sales representatives, logistics coordinators, accountants, financial advisors—all these professionals pull reports, look for trends, calculate metrics, and draw conclusions based on numbers. Transitioning to a formal data analyst role simply means taking that specific slice of your current job and professionalizing it with better tools. In KajoDataSpace, our subscription-based learning platform, we provide extensive materials and community support specifically designed to help you identify these hidden analytical skills from your past roles and leverage them in your career pivot.
Escaping Physical Labor: Is a Career Change at Thirty-Six Possible?
This is perhaps one of the most fascinating and emotionally charged topics I encounter. I regularly receive messages from viewers around thirty-five to forty years old who have spent their entire lives doing exclusively physical labor. They ask me if pushing toward a career in data analysis makes any sense at their age.
There is a striking paradox at play here. In the media and on various social platforms, physical labor is often heavily romanticized. We constantly hear narratives claiming that blue-collar jobs are the only ones safe from the AI revolution, that they are “real” work, and that everyone should abandon their laptops to become electricians or plumbers. Yet, the physical workers themselves are the ones contacting me, desperate for a change. They know the other side of the coin. They know that this work is grueling, frequently underpaid, performed in harsh conditions, and takes a devastating, irreversible toll on their health. It should not surprise anyone that these individuals want to transition to a “desk job,” regardless of how negatively that term is sometimes portrayed.
Is it difficult to make this transition at thirty-six? Extremely. If you were twenty-two, the problem would barely exist. At thirty-six, you are facing a massive mountain of hard work. The primary challenge is not just learning the technology; it is convincing a future employer to envision you functioning smoothly in a corporate office environment. If you have spent the last fifteen years laying paving stones or working on a construction site, the cultural and optical leap is immense.
Because of this, I often recommend a strategy of small, calculated steps. It is usually much easier to first transition into a simpler, entry-level office role—such as customer service, technical support, or basic administrative work. In these roles, you acclimatize to the office environment, you build a resume that proves you can thrive in white-collar work, and you can slowly start volunteering for reporting tasks, integrating data tools into your daily routine.
Is a direct transition possible? Yes, it absolutely is. Within the KajoDataSpace community, we have seen inspiring examples of this. Take Daniel, for instance—a professional driver who successfully completed the retraining process and secured a role in data, despite having zero prior connection to the IT world. I must warn you loyally that this is a punishingly difficult path. However, you must always ask yourself about the alternative. Visualize yourself in your current, physically demanding job and ask honestly: “What will my health, my knees, my back, and my life look like in exactly five years?” Sometimes, undertaking a brutal career change is not just a choice; it is the only sensible rescue plan for your future.
University Degrees: Specialized Tracks Versus General IT
Many younger viewers, particularly high school seniors, ask for advice on university degrees. They wonder if they should choose a highly specialized track like “Data Science in Business” or stick to a broader, more traditional degree like Computer Science, waiting until later to pick a specialization.
The truth is, both paths have proven to be highly successful. I have witnessed the careers of brilliant professionals who started very broadly and gradually carved out a niche, as well as those who dove headfirst into a specialized industry and later backfilled their foundational technical knowledge.
When you are at the very beginning of your journey, the most critical question you need to ask yourself is deceptively simple: what actually excites you? What do you genuinely enjoy doing? The better you align your daily tasks with your authentic interests, the more internal fuel you will have to persevere when things get tough. And make no mistake, things will get tough. Overcoming those inevitable difficulties is what hones your skills. In the long run, people who possess genuine passion will always outpace those who are only doing the work out of obligation or financial necessity.
Regarding the value of university itself—despite the popular internet mantra that “college is a scam and degrees are useless”—I firmly believe that studying is worthwhile. You do not go for the piece of paper; you go for the irreplaceable life experience. Human development and career trajectories are rarely linear. You do not just walk a straight line from point A to point B. It is the friction of bouncing between different environments, meeting diverse groups of people, participating in student exchange programs, and getting involved in weird, chaotic projects that enriches you the most.
Look at my own background. I briefly studied law, eventually graduated with a degree in Polish philology, and today I am a data architect and a business owner. This multiplicity of seemingly unrelated experiences grants me immense confidence when navigating wildly different business environments.
As for specialized roles at the intersection of IT and business, we are seeing the emergence of fascinating niche positions like the Forward Deployed Engineer. These are highly technical individuals who are simultaneously pushed to the front lines to interact directly with business clients. The massive advantage of these hybrid roles is the low competition. Very few people can seamlessly blend hardcore analytical engineering with refined soft skills and business acumen, which often results in premium compensation for those who can.
The Corporate “Prison” Fallacy
In the comments sections of my videos discussing job security or layoffs, I sometimes see a very specific, cynical narrative: “Are you afraid of getting fired? Then you are already a corporate slave.” This is a highly toxic and reductive mindset. We live in an era where the internet aggressively promotes a false dichotomy: you are either a free, independent, hustling solopreneur sipping cocktails on a snow-white yacht, or you are a chained, miserable slave suffering in the purgatory of a mega-corporation.
I consider this perspective to be complete nonsense. Corporations offer something that is virtually impossible to obtain on your own at the start of your career: the economy of scale. When you join a large organization, you gain immediate access to massive, multi-million dollar projects, prohibitively expensive enterprise tools, complex data architectures, and hundreds of highly experienced colleagues. You can absorb a gigantic amount of high-level competencies in a very short period. Naturally, this requires a degree of professional cunning. You must learn to adapt to the organizational culture, navigate office politics, and strategically position yourself.
If you feel frustrated by a lack of promotion, if you fear layoffs, or if the bureaucracy drives you crazy, you must learn to separate that specific, localized frustration from the broader concept of corporate employment. If you adopt a defensive posture, treating your employer as an inherent enemy just to play the romanticized role of a “miserable but free hero,” you are only sabotaging yourself. You cut your own wings and deny yourself access to immense educational and professional resources. Use the corporation as a vehicle for your own growth.
The Experience Catch-22 and Navigating a Difficult Market
“Everything you say is great, Kajo, but every single job posting requires prior experience. What are beginners supposed to do?” This is the classic Catch-22 of the job market. The answer has multiple layers.
First and foremost, people without experience get hired as data analysts all the time. It happens every single day. Is it a common, easy occurrence? No. But when you are competing for something highly valuable—and a lucrative, comfortable job in IT is exactly that—you simply have to try harder and accept the rules of the arena. Trophies are not handed out just for showing up to the fight.
Secondly, you need to fundamentally understand how job postings are created. There is no monolithic, perfectly rational entity called “The Employer.” A company is a chaotic collection of people. Sometimes, the HR department just copies and pastes an old template from five years ago because they have no idea what the technical manager actually needs. Other times, the hiring manager has an inflated ego and writes a “letter to Santa Claus,” demanding a unicorn expert while only offering an average entry-level salary. The result is that job descriptions rarely reflect the brutal, pragmatic reality of what the team actually needs. If you meet even half of the requirements, apply without hesitation.
Instead of complaining about a lack of commercial experience, shift your mindset to what you can actively control. Build a robust, public portfolio of projects. Volunteer your analytical skills for non-profit organizations. Attend local tech meetups and hackathons. Build genuine relationships on LinkedIn. You have to manufacture your own experience outside the boundaries of official employment.
Technical Expectations: Excel, Databases, and Interviews
Sometimes I hear from candidates who are absolute wizards in SQL but are terrified because they lack advanced Excel experience. Is this a dealbreaker? Generally, the higher you climb in the analytical hierarchy, the less you will touch Excel, as your workflow will rely on well-architected cloud pipelines, automated data warehouses, and advanced BI tools.
On the other hand, we must acknowledge reality: Excel remains an absolutely phenomenal scratchpad and the native, universal language of business. If you need to quickly discuss a trend with a Sales Director, you are going to do it in a spreadsheet, not by showing them a Python script. Therefore, it is highly beneficial to be comfortable with pivot tables, basic tracking mechanisms, and data lookups. You do not need to be a VBA macro magician, but fluency in Excel simply makes your daily corporate life and cross-departmental communication infinitely smoother.
And what do I actually ask when I am interviewing junior candidates? I can guarantee you I do not ask for encyclopedic definitions of database normal forms or a recited list of index types. A data analyst is not a Database Administrator. Instead of asking “what is an index,” I present a practical scenario: “We have a SQL query that is running painfully slow. What steps can we take to improve its execution speed?”
Then, I just listen. Does the candidate mention adding indexes? Do they bring up table partitioning? Do they notice that the issue might be poorly structured joins, or perhaps nested, unoptimized subqueries? Based on the depth, complexity, and practicality of their answer, I can flawlessly assess whether this person will survive in my team. Anyone can memorize an encyclopedia; analytical thinking and problem-solving must be demonstrated live.
Will Artificial Intelligence Replace Data Analysts?
Finally, we must address the elephant in the room—the topic that dominates every tech headline: Artificial Intelligence. Is the data analyst profession doomed? The answer is both yes and no.
Of course, a significant portion of our daily tasks is being automated. AI has drastically accelerated the process of writing documentation, generating boilerplate analytical code, and drafting simple ad-hoc SQL queries. However, it has done the exact same thing to virtually every single profession that involves sitting in front of a computer.
What the fear-mongers forget is the monumental importance of the human element in analytics. Someone still has to deeply understand the unique business context, locate the correct (and often undocumented) data sources, stitch together highly incompatible legacy systems, and proactively answer the business questions that the stakeholders haven’t even figured out how to ask yet. In this context, compared to the apocalyptic promises made in 2019—when Twitter gurus swore we would all be unemployed in two years—AI has completely failed to deliver on its doomsday prophecies. Even basic administrative roles and light bookkeeping haven’t vanished at the predicted velocity.
You must remember that the media thrives on fear and anxiety. Mass layoffs generate clicks; they are the “breaking news” banner, exactly like an airplane crash. Nobody writes articles about the thousands of flights that land safely every day, nor do they write about the thousands of companies that are quietly, stably hiring tech talent, because stability is boring. The job market is undoubtedly a bit tougher today than it was a few years ago, that is a fact. But AI should not be viewed as the grim reaper of your career; it is simply another component of the technological stack. It is just one more tool in your arsenal that you need to master.
Conclusion
Where do I see myself in five years? If I am being entirely honest, I have absolutely no idea. For now, I have landed here, embracing my role as an educator, and I am putting my heart into building KajoData. I am not going to feed you a fabricated story about this all being part of some grand, meticulously calculated master plan. Life is not a chess game; it is more like playing tennis with reality. I am simply trying my best to accurately return the balls that are served over the net my way. And I truly hope that we will meet here again, exactly five years from now, to see how the game has evolved.
If you believe this article could help someone you know—perhaps a friend who is hesitating about a career change, someone stressing over an upcoming technical interview, or a colleague doubting whether their English is “good enough”—please share it on your social media. Your shares on LinkedIn, Facebook, and Twitter are what help me reach the people who genuinely need this knowledge to build a better future. Talk to you soon!
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!
Did you like this article 🙂?
Share it on Social Media 📱
>>> You can share it on LinkedIn and show that you learn something new every day.
>>> You can throw it on Facebook – and perhaps help a friend of yours who is looking for this.
>>> And remember to bookmark this page, you never know if it won’t come handy in in the future.
You prefer to watch 📺 – no problem
>>> Subscribe and watch my English channel on YouTube.
Prefer to read in Polish? No problem!
Other interesting articles:
