I Analyzed EVERY Data Job Listing, Here’s The Brutal Truth

Chances are if you've been on YouTube and have been trying to break into data analytics, you will have seen a million and one videos about how it's no longer worth it and how the job market has been stolen by AI. Well, I studied over 1,300 data job listings on the market right now, comparing it to figures from years before, and what I find will probably shock you. Now, if you're new here, I'm Kedisha, and over the past 5+ years, I have placed over 500 people into high-paying data roles, and I've firsthand seen what actually has changed over time and what makes for a good scare tactic. So in this video, I'm going to expose the truth. And by the end, you'll know if there are actually less jobs now, the actual impact AI is having, and the one secret trick that makes landing a job 20 times easier. And I'll also walk you through how to solve real business problems like everyone else keeps asking for. So let's get into the data. Let's start with the part that no one wants to hear. A recent market analysis that I looked at found that after the tech layoffs, companies shifted from what you would basically call hire ahead of need to hire when it hurts. And at the same time, the number of people breaking into data analytics has exploded. So you've got bootcamp graduates, career changers, college graduates, self-taught analysts, all competing for a smaller pool of traditional junior openings. So yes, compared to a few years ago, there are far fewer entry-level opportunities and more people are going after them. I'm not going to pretend otherwise, but that does not mean that data jobs have disappeared. In fact, depending on your specific occupation, the research that I reviewed still point to a very strong growth in data-related careers over the next decade. So the market isn't dead by far. It's actually changed. And once you actually look at the jobs that are available, you start to see exactly how. First, there's experience. A few years ago, it was easier to find jobs that were generally comfortable with hiring someone with little to no professional data experience. But today, that sweet spot has moved. The data that I looked at shows that employers are more frequently looking for candidates with somewhere between 2 to 4 years of experience. Now, don't panic when you hear that. I'm going to show you later in this video how you can work around that problem, even if you've never officially had any data analyst job title in your resume. For now, I want you to see what the market is asking for. Because the second big change is the type of jobs we're seeing less of. Pure reporting roles are harder to find. So are the roles where the analyst is just basically extracting data, cleaning it, putting it in a report, and sending it to someone else. Those jobs still exist, but compared to a few years ago, there's far fewer roles like that right now. At the same time, we're seeing far more roles sitting somewhere between technical data skills and a specific business function. Analytics engineer is a good example. Think of it like a full-stack data analyst. So are roles like product analyst, revenue analyst, and healthcare data analyst. This is why I say if you're a career changer, your domain is your competitive advantage, and the exact titles vary from company to company. The common theme is that the analyst is becoming more specialized and the differences become more clearer when you compare the actual job descriptions. A typical job description a few years ago might basically have said, we just want SQL, Excel, and Tableau. And that was really the core of the job description. Today, those skills are still there, obviously, but they're almost assumed. Now you're seeing things like problem framing, working through ambiguity, communicating with different parts of the business and stakeholders, and increasingly generative AI literacy. Even machine learning has started to appear in some often standard data analyst job postings. In the research that I reviewed, mentions of machine learning itself doubled to around 14%. Now again, I'm not telling you that you suddenly need to become a machine learning engineer or go take a 50-hour data science course. I'm just showing you how much the expectations around the typical analyst role has expanded. That's the main thing I want you to take away from this data. The jobs didn't disappear. It's just that the profile of the person companies want has changed. And that matters because if you've been preparing for the 2022 version of the job market, you could be doing everything people told you to do and still wondering why you're not landing interviews. Now let's get back to the 2 to 4 years experience issue that we talked about earlier. Now getting hired today is not just about becoming more qualified. It's also about knowing where to look and Changing where you look can completely change the difficulty of your job search. Now, a few years ago, the standard career advice made perfect sense. You take a course, you learn some Excel and SQL, and you find a junior data job where you spent your first year cleaning spreadsheets, pulling reports, and just doing the lower-level work no one else wanted to do. And then once you prove yourself, you can gradually move up into more serious analytical work. That was the ladder back then. And now if you're entering data today using the exact same ladder, maybe the biggest reason why you're struggling, because the bottom wrong has changed. A lot of the work we traditionally used to give junior analysts is the exact type of work technology has become very good at accelerating. Take basic SQL. If I need to get a starting query, a tool can help produce a starting template almost instantly. If I have some Python code throwing an error, I can paste it into an AI tool and it can help me debug in seconds. That doesn't mean AI has replaced good analysts. It just means a lot of the basic tasks that companies used to hire junior analysts to do that used to take hours and hours to do is becoming more cheaper and faster to complete. And that's the important distinction. The second type of role being squeezed is what I would call the dashboard-only analyst. Imagine your job is basically this. Someone gives you a question, you pull the data, and you put the answer into Power BI or Tableau, and then you send the dashboard back. That's the whole cycle. Modern BI tools are increasingly making basic reporting more self-service. So a marketing manager who just wants a simple answer about last month's performance may not always need to wait for an analyst to build a chart from scratch anymore. So once again, it's not that dashboards are dead. You are still going to be building dashboards. The problem is when your only value is just building dashboards. Then there's a third pressure, outsourcing. Some of the repetitive data work that companies still need to be done is sent to lower-cost teams overseas. And when you combine outsourcing with automation, it becomes much harder to build a career around the work that is purely repetitive. So that's what's changed, but here's the biggest part that's getting missed. You don't have to compete for the same exact junior role that's disappearing. You can bypass all of that competition by using the experience you already have. Let's say you spent the past 7 years working operations or logistics or supply chain. A lot of people make a the move into data will look at those 7 years and think, man, none of this counts because I didn't have an analyst title. That is the biggest mistake you can make. You understand staffing, you understand processes, you understand bottlenecks, you understand what happens when inventory is wrong, when a team is understaffed, and when one part of the operation slows everything else down. Someone who spent 6 months taking an online data course may understand SQL better than you, but they have absolutely no idea how an operation actually works in a business. That is domain experience, and you can do the same thing with almost any background. So someone Someone coming from healthcare already understands healthcare problems. Someone coming from retail understands customer inventory and sales. Someone coming from HR understands hiring, turnover, and workforce problems. The previous career or your current career does not disappear when you decide to pivot into analytics. The goal is to connect your previous experience or your current experience to data. So instead of trying to look like a completely generic beginner, like 99% of everyone else, build projects around the world you already understand. All right, quick pause. Now, if you want to land your first job in data analytics and avoid the lengthy mistakes that I did, click the link in the pinned comment where you can find the exact steps that my students use to land their job in data analytics without going back to school. All right, now let's get back to it. And this brings us to the second way to bypass the entry-level experience problem. Stop waiting for someone to hire you before you start doing real work. One of the biggest differences between a course project and actual analyst work is real messy data and deadlines. Real problems are vague. There isn't always an answer key, so go find it yourself. Find local businesses and nonprofits and offer 30 days of free analytical consulting. Take on a small project. Let's say you help a local business understand why repeat customers are dropping. Now you have to find the data, clean it, talk to stakeholders, decide what matters, perform the analysis, explain what the owner should do next. You're not going to get that from a Kaggle dataset or a regular course. That is very different from reproducing a dashboard from a tutorial online. And if you generally want to complete that work as a real business, you can describe it honestly as freelance data experience. You are being a freelance consultant. That's how you start closing the experience gap before someone gives you a full-time job. So if the traditional entry-level route feels like it's disappearing, don't spend the next year or 2 trying to force yourself through the same doors everyone else. Use your experience that you already have and start creating the experience that you're missing. Now I'm going to say something that's going to make a few people uncomfortable. If you've been spending most of your time studying or memorizing complex Python syntax and trying to master 5 different BI tools, I think you're wasting a lot of time. Notice what I said. I did not say Python is useless or Power BI is useless. I didn't say to stop learning technical skills. I'm saying memorizing syntax is becoming a worse use of your study time, especially if you're working a full-time job, coming home and trying to study at night, and you only got a few hours to work with. You cannot afford the next 3 months learning things that an AI assistant can help produce in seconds once you understand the fundamentals. You need to spend that time building skills where your judgment still matters. And the first one is problem framing. Let's say someone tells you, hey, sales are down. A beginner can take that sentence and immediately build a sales dashboard. But a 6-figure data analyst understands that it needs to turn that into something specific enough to investigate. For example, sales are down 18% in the East region among customers acquired in Q3, and now we need to determine whether that decline is coming from pricing or retention. That is a completely different level of thinking. You have taken a vague complaint and turned it into a problem you can actually investigate with context. That is worth learning. The second skill I will go deep on is SQL and relational databases. Not because you need to memorize every single SQL function in existence. I want you to understand how databases actually work. You need to understand how tables connect, why we use joins, what happens when you join tables incorrectly, how the structure of your data affects your result, and how to recognize when your query technically ran, but the answer is still wrong. Because eventually you're going to start using AI to speed up your work. You need to understand how to QA the output so you're not providing wrong information to your managers. All that understanding transfers. The exact syntax can always be looked up, but the judgment behind the syntax is what you need to build every day. Now, the third major skill you need to focus on is communication, because knowing what happened in the world of data is only half of the job. You need to be able to explain it to someone who may not care about the technical work as you. Your manager probably does not want to hear every single step of your SQL query. They want to know what changed, why it matters, and what recommendation they should do about it. That is a skill, and it's one you can deliberately practice. So if I had exactly 6 weeks to make an aspiring data analyst job ready as possible, here's exactly how I'd want them to spend their time. The first 2 weeks deliberately should go entirely to SQL and relational databases. And I would not spend those 2 weeks passively just watching tutorials. I would write the queries by hand. I would break them down. I would fix them. And I would work with joins until I understood exactly why my row count changes. And make sure you take this time to do whiteboard SQL problems as well. It forces you to think in SQL. And I learned that when you can explain code in pseudocode or simple plain English, it allows your brain to easily understand things quicker. Now for week 3, it would be visualization. Pick one BI tool and get good enough to communicate a data story clearly. Not 5 tools, just one. The point is to not become the world's greatest BI developer in 7 days. The point is to learn how to take any analysis and make the most important parts obvious. Week 4 would be AI-assisted workflows. I would deliberately practice using tools like ChatGPT or Claude to debug your code, help format your data, explain errors, and speed up repetitive work, but I would force myself to understand the output. AI is your assistant always. You still have to be capable of judging whether the answer makes sense or not. And then the final 2 weeks would just be business application. I would be spending those weeks completing full end-to-end projects where the starting point is a high-value business problem and not learning a tool. And I would want to practice presenting those projects to someone non-technical because at the end of those 6 weeks, I want you to be able to say, I solved a business problem using SQL, or I solved a business problem using Tableau, et cetera. That is a formula to become job ready, not just learning skills. So now you're actually ready for a job in 2026. So how do you go about actually getting hired? This is where a lot of people completely undo all the good work they've done so far. They'll spend months learning. They'll have all the projects. Their resume is finally decent. And then they go to LinkedIn, type data analyst, filter for remote roles, and start clicking easy apply as fast as humanly possible. That's their strategy. Apply to 10 jobs this week, apply to maybe 3 the next week, and then complain that the job market is impossible because they're not getting any interviews. I call this spray and pray. And the problem isn't just because of the number of applications that you're doing. It's also how you're applying. If you search for a generic remote data analyst position, you're deliberately putting yourself in one of the most crowded applicant pools available. Everyone knows that title. Everyone searches for it. Everyone wants a remote job. So one of the easiest ways to make the job search more manageable is to stop searching for only one job title. The work has to spread into more specialized roles. You might see revenue operations analyst, supply chain analyst, product analyst, marketing data analyst, process analyst. Those jobs can involve many of the same analytical skills you're already learning. The difference is that they're tied to a different business function, and a lot of aspiring analysts never search for them. And this is where your background becomes useful again. If you spent years working operations, you should be searching heavily heavily around operations and supply chain analytics roles. If your background is in marketing, I would be looking for marketing analytics and revenue roles. You're not changing careers twice. You're finding the overlap between the career you already understand and the analytical skills you've been adding. Now let's talk about your resume real quick, because the same resume can look great to one company and completely irrelevant to another. Imagine your resume says use SQL and Python to analyze data. There is nothing technically wrong with that, but it's very generic. Now imagine you're applying to a SaaS company and your resume shows you analyze customer churn. Immediately you use the language that matters most to that business. Or you can apply to a logistics company and your project focuses on delivery time and supply chain performance. Same analyst, same tool, but different relevance. This is why I believe in tailoring your resume to each job description, not rewriting the entire thing from zero every application. You should have a strong base resume, but then you need to adjust which experience and projects and outcomes you emphasize based on the role that you're applying for. The employer should be able to look at your resume and quickly understand why your background makes sense for their job. And then comes the companies themselves that people target. A lot of people breaking into data immediately start aiming at big tech because those are all the companies people know about. But big tech is also where everyone is looking. Meanwhile, there are mid-sized companies in healthcare, finance, retail, construction, other less glamorous industries that still need people who can work with data. They may not make YouTube videos about their offices, but that doesn't mean they don't have jobs. In fact, those are the exact companies I'll be paying attention to. So here's a system I would use. First, pick 1 to 2 max target industries. Ideally, choose ones where you already understand the business or have some relevant experience. Then you build your entire search around analyst titles that show up inside those industries. And once you find a role that generally fits you, tailor your resume and find the metrics and problems that matter to that company. And then don't stop at submitting the application. Find the hiring manager, find the head of data, find the recruiter, the analytics manager, whatever it is, whoever appears to be connected to that team, send them a short message on LinkedIn, not an say, just introducing yourself. Now you've done something completely different from the person who clicked easy apply and disappeared, and you've shown relevance. Now I can't promise you that person will respond, and no one can, but this is the point I teased earlier. Getting hired becomes much easier when you stop competing in the exact same places as everyone else, because sometimes the biggest advantage isn't just learning another skill, it's just learning where to look. Now you can follow the perfect application strategy, but if your resume and portfolio looks exactly how everyone else's looks, sending more applications is not going to save you. It's like throwing 500 darts at a board while blindfolded. The problem isn't how many times you're throwing, it's that you're not even seeing the target. And in data, one of the easiest ways to take a blindfold off is to show that you can actually solve business problems from beginning to end. Companies don't just want to see that you can use SQL, Python, or Tableau in isolation. So let's say an e-commerce company is losing money at checkout. The weak version of a project could be Sales are down 10%. You can build a dashboard, show the decline, and then stop there. That is what most people do. But don't be like most people because the company already knows sales are down. You're not telling them anything new. This is how a $100K data analyst approaches problem solving. They're going to break the sales down and notice that desktop performance is stable, but mobile conversions has dropped sharply. Then they're going to look at the checkout process and realize customers are making it all the way to the payment page before leaving. Then they're going to investigate further and find that mobile payment page is taking 6 seconds to load. Now they have something useful and way more context about the problem. The problem isn't simply that sales are down. The actual problem is that the company is losing mobile customers during checkout. So you recommend improving the page speed. The engineering team fixes it, the conversions improve, and now your analysis has contributed to actual revenue on the bottom line. That is what you call end-to-end problem solving with data. It's not just a dashboard. You show the problem, the analysis, what you discovered, and the actions you would recommend. Build projects around high-value problem solving like that, and that's gonna make you the most obvious hire. So now you know what to stop wasting time learning and what to actually focus your time on to land a data job. All you need now is an action plan. So if you wanna get my help to get you fully trained and placed into a role, click the link in the top of the description. If you're not ready quite yet, then watch this next video where I break down exactly how I landed a $100K data analyst job in 90 days. I'll see you there.