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If you Google „data analyst,“ you’ll see people working from home, six screens, perfect dashboards, and someone casually glancing at numbers and knowing the answer right away. That’s not what everyday life looks like. The real tasks of a data analyst are less glamorous and far more interesting: real questions, raw data, and lots of conversations with people. Here’s an honest look at a typical workday, from the first email in the morning to the last check-in with stakeholders in the afternoon.
Tom Gadsby, a senior data scientist at CareerFoundry (now part of StackFuel), shows what a typical workday looks like in a “day in the life” video—from his morning routine to team meetings and data work, all the way to reports and stakeholder discussions. The video is in English, but you can turn on subtitles in the player at any time and have them automatically translated into German if needed.
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What Does a Data Analyst Do? A Brief Overview of the Job Responsibilities
A data analyst uses data to answer specific questions from within the company. Responsibilities include: prioritizing requests, retrieving data from the database using SQL, cleaning and analyzing it, and presenting the findings in a way that allows colleagues without a data background to act on them. Communication is just as important as technical skills.
The Morning: Sort First, Don't Analyze Right Away
The idea: You sit down, open a dashboard, and dive into in-depth analysis. The reality: The morning is first and foremost about getting your bearings.
You start with emails and messages. Often, a question has come in overnight. Someone from marketing wants to know why a certain number dropped last week. You check the backlog and decide what’s urgent and what can wait.
Next comes the stand-up—the brief team meeting where the day’s priorities are set. This is often where it’s decided what you’ll actually be working on, and that regularly differs from what you had planned. A typical example: You wanted to finish a major analysis, but then an urgent question comes in from sales that needs to be answered by the noon meeting. So you have to reschedule.
Tom also describes his morning exactly that way in the video above: first, checking emails and reviewing the backlog, then the standup. He works in sprint cycles of one to two weeks, during which the team agrees on the most important goals, and priorities are reassessed during the morning meeting. What you had planned often takes a back seat to whatever is more urgent at the moment.
Good to know: A data analyst rarely spends hours working undisturbed on a single task. You juggle multiple requests, and part of the job is deciding which ones to tackle first. This ability to calmly reorder priorities is just as much a part of the job as the analysis itself.
The Morning: Retrieving Data, Organizing, Understanding
That's the heart of the job, and for most people, it's the most fun part. It just doesn't look as spectacular as the glossy videos make it seem.
Take this morning’s marketing question. Why did the number plummet? You start by using SQL to pull the data from the database. Then comes the part that hardly anyone shows: cleaning up the data. The data isn’t clean. There are duplicates, missing values, and a column with a different name than expected. A large portion of the time isn’t spent on the spectacular analysis itself, but on preparing the data so that the analysis can be reliable in the first place.
In the video, Tom gives a clear example of this quality control: For instance, he checks to see if numbers suddenly appear in a column that’s supposed to contain names. It’s precisely these kinds of hidden errors that ultimately determine whether an analysis is reliable.
Once the data is clean, that’s when things get interesting. You take a look at what happened. Maybe it wasn’t a real spike at all, but a holiday the week before. Maybe a campaign ended. You test, compare, and narrow down the possibilities until you have an explanation you trust.
Important: Not every question leads to a spectacular result. Sometimes the answer is simply, „It was a holiday the week before—no cause for concern.“ That, too, is valuable work, because it prevents a team from drawing the wrong conclusions from a random number.
The afternoon: explain, present, defend
Now you have an answer. The idea: You send a number, and that's it. The reality: This is where the part that makes all the difference begins.
You present the explanation in a way that someone without a data background can understand. Usually, this takes the form of a brief update in Power BI or a single slide. Not twenty charts, but a single statement: „The decline was seasonal; here’s the comparison to last year, and this is my recommendation.“
As Tom explains in the video, there are two ways to do this: interactive dashboards in tools like Power BI, Tableau, or Looker that update as the data changes, or a static presentation for the one-time wrap-up of a project. Both rely on the right mix of charts and text to get the message across.
Then you present your findings and receive questions—sometimes critical ones. „Are you sure? Couldn’t this be due to something else?“ That’s exactly why you need to be able to defend your analysis calmly and clearly. That’s the moment when numbers turn into a decision.
The same number can be good news for marketing and a warning for financial control. Your job is to present it in a way that allows both teams to understand what it means for their decisions. You don’t want everyone to just nod politely and then, in the end, no one to actually do anything with it. That’s why communication in this profession is at least as important as technical skills. People coming from sales, nursing, or administration often already bring this skill with them—a real advantage when it comes to A Career Change into the World of Data.
The Honest Side: What Nobody Puts on the Flyer
Every „Day in the Life“ shows the good parts. Here are the others.
There’s the last-minute request, right before the end of the workday, with „Can you quickly do this for me by tomorrow morning…?“ There are interruptions where three people all need something at the same time while you’re in the middle of an analysis. And there are unclear requests: Someone asks for „the numbers“ without knowing exactly what question they actually want answered. Part of the job is figuring out the right question in the first place.
The other side of the coin—and the reason why many people enjoy this profession—is that the work is varied; hardly any two days are alike. You have a real impact because your analysis shapes what a team does. The pay is solid; a realistic picture of this is provided by the Federal Employment Agency Wage Atlas and our overview of the current salaries. And in many roles, you'll have a good amount of flexibility when it comes to working hours and working from home.
There are days when, by evening, you feel like all you've done is answer emails. Then comes the moment when an analysis changes a real decision, and that carries you through the tougher days.
Perception vs. Reality: An Overview
| Time of day | The Presentation | The Reality |
|---|---|---|
| Tomorrow | Dive right into the analysis | Emails, backlog, stand-up. Sorting priorities |
| Morning | Building Elegant Models | SQL query, then focus on cleaning up the data |
| afternoon | Send the number, done | Explain, present, and answer critical questions |
| Fundamental tone | A Lonely Genius at a Six-Monitor Setup | Many conversations, incomplete data, real impact |
What tools do you really need?
Most of the day goes by SQL, Excel, and Power BI. SQL is the tool that lets you access the data in the first place, and it comes up as a small exercise in practically every job interview. Excel remains the go-to tool for quick intermediate steps. Power BI turns the results into something your colleagues can understand.
Python comes into play when an evaluation is repetitive or too complex for a quick query. It’s an extension, not a starting point. Anyone who reverses the order and starts with Python because it sounds the most impressive often wastes months. Which Tools—in what order? Whether they make sense is a separate question.
In the video, Tom also encourages viewers not to be intimidated by a new programming language. Once you've mastered one language, it's easier to learn the next, and many companies use different languages anyway.
Tip: The need is real. According to Bitkom, the German economy is short about 109,000 IT professionals, and 85 percent of companies report a shortage. 22 percent now have their own programs for career changers.
How do you get the job? The path rarely involves a college degree. Most data analysts working in German companies today entered the field through continuing education or a career change. What matters isn’t your degree, but proof that you’ve mastered the craft: one or two projects you can showcase in which you’ve taken a real-world problem from the raw data all the way to a recommendation. Subsidized continuing education programs are AZAV-certified and can be accessed through the education voucher pay.
If this part sounds exactly like you—that is, if you enjoy distilling a clear answer from complex data—then you can work in a free consultation see if this path is right for you. The Data Analyst Training provides training specifically for this job and can be accessed via the education voucher promote. You are not entitled to this; the agency decides on a case-by-case basis.
Conclusion: Not flashy, but it makes a real difference
A typical day as a data analyst looks like this: sorting data in the morning, pulling and cleaning data in the early afternoon, building and defending the story in the afternoon, and in between, the usual chaos of interruptions and last-minute requests. It’s not glamorous, but it’s varied, pays well, and gives you real influence over decisions.
If you like this mix, it's one of the best ways to get started in the world of data. Just schedule your free consultation. Together, we'll take a look at your background and the next concrete step. StackFuel has been AZAV-certified since 2020 and has supported more than 8,000 graduates.
Frequently asked questions
What are the typical responsibilities of a data analyst?
Prioritize requests from within the company, extract data using SQL, clean it up, analyze it, and prepare and present the results in a way that allows colleagues without a data background to make decisions based on them. Data preparation and communication take up more time than most people expect.
How much time does analysis really take?
Less than you might think. A significant portion of the morning is spent cleaning up the data so that the actual analysis can be reliable. The analysis itself is half detective work and half cleanup.
What tools does a data analyst use on a daily basis?
SQL, Excel, and Power BI cover most of our day-to-day work. Python is used as a supplement when analyses become repetitive or more complex.
Is being a data analyst an office job that involves a lot of working alone?
No. The stereotype of the quiet analyst wearing headphones isn't true. You work closely with stakeholders, need to identify their real question, and must explain and defend your findings in a way that's easy to understand.
