You are currently viewing a placeholder content from YouTube<\/strong>. To access the actual content, click the button below. Please note that doing so will share data with third-party providers.<\/p>\n More Information<\/a> <\/div>\n This article explains what a junior data analyst does, what employers really expect, what the starting salary is, and how to land your first job\u2014even if you're coming from a completely different field.<\/p>\n\n\n\n During their first one to two years on the job, a junior data analyst works on clearly defined analytical tasks under the guidance of more experienced colleagues. They retrieve data using SQL, prepare it in Excel or Power BI, create reports, and answer questions from business units. As they gain experience, they begin to make their own recommendations. This role is the typical entry point into the profession.<\/p>\n\n\n\n My day-to-day routine consists mostly of cleaning up. In reality, data is rarely clean: a column is missing, a value doesn\u2019t make sense, and sometimes you spend half an hour trying to figure out why a number is wrong, only to find out in the end that it was a typo in the raw data. On top of that, there are inquiries from marketing or sales, as well as small analyses and reports that come up repeatedly.<\/p>\n\n\n\n Getting started is therefore less glamorous than the glossy pictures, but it\u2019s full of variety. You can see what this everyday life looks like in the specific Responsibilities of a Data Analyst<\/a>. One thing is especially important for entry-level positions: You don't have to be able to do everything on your own, but you must be able to work carefully, ask questions, and explain what you've found.<\/p>\n\n\n\n Starting salaries vary depending on the source and region. According to StepStone<\/a> The average salary for junior data analysts is around 41,800 euros per year. The analysis of JOIN<\/a> For positions in small and medium-sized businesses, the median is 42,000 euros, with a range from 38,000 euros (lower quartile) to 47,000 euros (upper quartile). The major cities of Munich, Hamburg, and Berlin drive these figures upward, while regional locations often fall below them.<\/p>\n\n\n\n Two points to note here. First, these are average figures from salary websites\u2014not a guarantee for your salary negotiations. Second, an entry-level salary is not a top salary. The good numbers at the Data Analyst Salary<\/a> come with experience, expertise, and a proven track record of what you've delivered.<\/p>\n\n\n\n A job posting is like a wish list. This is especially true for the phrase \u201eone to two years of professional experience\u201c in a junior position. Alexander Eckrot, Strategic Operations Manager at StackFuel, has been helping career changers land their first data job since 2018. He advises applying if you meet about 70 percent of the requirements. This rule of thumb applies to a Analysis of Over 6,000 Job Postings<\/a>, according to which meeting just about half of the criteria gives you the same chance of getting an interview as meeting 90 percent of them.<\/p>\n\n\n\n In practice, this means that Excel, SQL, and a visualization tool like Power BI are essential. Python, cloud experience, or one to two years of relevant work experience are usually a plus, but rarely a deal-breaker. If you wait until you\u2019ve checked off every requirement before applying, you\u2019ll be waiting too long. In the video \u201eBecoming a Data Analyst in 2026,\u201e Alexander Eckrot says: \u201cThe first 20 applications are a learning experience.\u201d<\/p>\n\n\n\n Three tools are the starting point, and the order matters. First, Excel, with pivot tables, functions, data cleaning, and simple visualizations. Excel is still the most widely used tool for data work in most companies. Next, SQL and Power BI\u2014ideally in parallel: SQL retrieves the data from the database, Power BI<\/a> visualizes them in reports. Together, they meet the core requirements of many data analyst positions.<\/p>\n\n\n\n Python is added as an extension and is not a starting point. According to Alexander Eckrot, anyone who learns Python at the same time as Excel and SQL won't learn any of them properly. The order in which the tools are introduced is shown in the A Roadmap to Becoming a Data Analyst<\/a>.<\/p>\n\n\n\n In addition to the tools, there\u2019s something else that\u2019s harder to measure: you need to be able to explain numbers. Data analysts work closely with business departments. Someone who can turn a number into a clear recommendation is more valuable in the long run than someone who\u2019s just technically strong.<\/p>\n\n\n\n Alexander Eckrot estimates that it takes about six months full-time if you study in a structured way, and twelve months if you\u2019re working at the same time. This is based on his experience working with many people changing careers\u2014it\u2019s not a guarantee. Some people learn faster, while others take longer.<\/p>\n\n\n\n Keep in mind that the job applications don\u2019t wait until the end of the six months to begin. In months three and four, you\u2019ll work on your first job application project, and your first applications will go out. In months five and six, you\u2019ll start learning Python, and you\u2019ll treat interviews as a learning loop: after each interview, write down what wasn\u2019t clear and what you can do better next time.<\/p>\n\n\n\n With a portfolio project. That\u2019s the part that often makes the difference in entry-level applications. Learning projects where you take a clean dataset and apply a tool help you learn. But they don\u2019t show employers whether you can do the job. An application project answers a real-world question using real\u2014or at least realistic\u2014data, utilizes at least two of the three tools, and concludes with a summary and recommendation.<\/p>\n\n\n\n Here\u2019s an example from the video: You take public data on rental price trends in your city, clean it up using Excel or SQL, and build a dashboard in Power BI that shows prices by district and number of rooms. Then you write a one-page summary: What stands out, what does it mean, and what would you recommend to someone looking for an apartment? This demonstrates technical skill, judgment, and communication all at once. Alexander Eckrot sums it up this way: \u201eA job application project that answers a real-world question is worth more than five learning projects.\u201c<\/p>\n\n\n\n Even better is a project with a real person behind it\u2014such as volunteering for a nonprofit, conducting an analysis for a small business, or taking on a freelance assignment. Through projects like these, you\u2019ll gradually build a Data Analyst Portfolio<\/a> that you highlight in your job applications.<\/p>\n\n\n\n According to Alexander Eckrot, three approaches make all the difference. First: Use your background to your advantage. If you\u2019ve worked in logistics for ten years, you already understand half of what logistics data entails. Check out job postings for operations analysts, marketing analysts, sales analysts, or financial analysts, because those roles require specialized knowledge. When you apply with an industry focus, you\u2019re competing against a different pool of candidates than you would for general data analyst roles.<\/p>\n\n\n\n Second: Start sooner than you feel ready. No one feels ready on the very first day of applying. That feeling doesn't just come on its own, and if you wait for it, you'll never apply.<\/p>\n\n\n\n Third: Reach out. A 15-minute conversation with someone in your target field is often more effective than ten applications that go nowhere. You\u2019ll learn what the role is like in day-to-day life and what employers are currently looking for, and sometimes a door will open that you would never have found otherwise. A direct message on LinkedIn with a genuine question works more often than most people think. A generic connection request, on the other hand, does not.<\/p>\n\n\n\n This ties in with a thought Alexander Eckrot shares at the end of the video: \u201eView every application process as a source of information, not as a judgment.\u201c<\/p>\n\n\n\n You can teach yourself the basics through a boot camp, a subsidized continuing education program, or (rarely) a retraining program leading to a professional certification from a trade association. Self-study is the most affordable option, but it requires discipline and a structure that you create for yourself. A continuing education program provides structure, feedback, and a portfolio project.<\/p>\n\n\n\n At StackFuel, you\u2019ll build a project using real data and receive feedback throughout the training program, culminating in a portfolio project. The Data Analyst course lasts seven months full-time or 14 months part-time and covers Excel, SQL, Power BI, and object-oriented Python. StackFuel has been AZAV-certified since 2020, which means funding is available through the education voucher. There is no legal entitlement to this funding; the Employment Agency decides on a case-by-case basis. How you you'll receive the education voucher<\/a> We've summarized the steps for preparing for the interview in a separate section.<\/p>\n\n\n\n Can I become a junior data analyst without any work experience?<\/strong> Yes. Tools like Excel, SQL, and Power BI are essential, as are a compelling application project and the courage to apply even if you don't meet every requirement.<\/p>\n\n\n\n How much does a junior data analyst earn?<\/strong> Salary websites report an average of about 41,800 euros (StepStone) and a median of 42,000 euros (JOIN) per year. The figure varies significantly depending on region, industry, and company size.<\/p>\n\n\n\n Do I need to know Python to become a junior data analyst?<\/strong> Not from the very beginning. Excel, SQL, and Power BI form the core. Python is added as an extension and is required for many advanced roles.<\/p>\n\n\n\n How long does it take to get started?<\/strong> As a general guideline, Alexander Eckrot suggests six months of full-time study or twelve months while working. This is not a guarantee and depends on your background and the time you have available.<\/p>\n\n\n\n When should I start applying?<\/strong> If you meet about 70 percent of the requirements listed in a job posting. Your first few applications are a learning experience and provide you with information for the next ones.<\/p>\n\n\n\nWhat does a junior data analyst do?<\/h2>\n\n\n\n
What does the work actually look like at the beginning?<\/h2>\n\n\n\n
How much does a junior data analyst earn?<\/h2>\n\n\n\n
What Employers Really Expect<\/h2>\n\n\n\n
![\"Sample](\"https:\/\/stackfuel.com\/wp-content\/uploads\/2026\/10\/indigo-5_rule-junior_halftone-1024x576.png\")
<\/figure>\n\n\n\nWhat skills do you need for your first job?<\/h2>\n\n\n\n
How long does it take to get started?<\/h2>\n\n\n\n
![\"Recommended](\"https:\/\/stackfuel.com\/wp-content\/uploads\/2026\/10\/indigo-6_timeline-zeitplan_dots-1024x550.png\")
<\/figure>\n\n\n\nHow do you gain work experience if you don't have any yet?<\/h2>\n\n\n\n
How do you apply for a job as a career changer?<\/h2>\n\n\n\n
What's the path to your first job?<\/h2>\n\n\n\n
Frequently Asked Questions About the Junior Data Analyst Role<\/h2>\n\n\n\n
Your Next Step<\/h2>\n\n\n\n