I Studied 2,493 Data Analyst Resumes, Here’s How To Get Hired

Over the last 5+ years, I have studied over 2,000 resumes from aspiring data analysts, and in that time, I have seen what makes a difference between people who get interviews and those who never hear anything back after applying to 100+ jobs. But here's where things get a little weird. In my experience, getting a high-paying data analyst role has nothing to do with how many certificates you have, which templates that you use, or how many years of experience that you've got. Instead, if you want to get hired quickly in analytics, you have to build your resume following a few boring but repeatable steps that I'm going to share with you in today's video, starting with what I call the 7-second rule. So imagine that you give me your resume, I open it, and for the first few seconds, I'm not really reading your resume. I'm just trying to figure you out. My eyes hit at the top of the page, and I'll glance at the role that you're targeting, and I'll look for a couple skills, and then I will jump to your most recent experience. And pretty quickly, I'm forming an opinion, not necessarily about whether you're qualified, but whether I want to keep looking for more That distinction is important because you might look at your resume and say, but everything they asked for is on there, and you could be completely right. You got your SQL on there, Power BI, maybe you've been doing analytical work for years, but I shouldn't need a bunch of time to discover all that. This actually came up when I was doing a live resume roast. We had one resume where if you sat there and dug through it, there was good stuff, but we kept having to stop and figure things out ourselves. And the recruiter there basically said, the larger that stack of resumes, the less likely someone is going to take the time to spend all that time trying to solve a puzzle. That is what I want you to understand. A resume is not about just having the right information. It is about making the right information obvious. Let's say SQL is one of the strongest reasons that you're qualified for a job. If I have to get halfway down the page before I even see SQL, that's a problem. Not because recruiters physically cannot scroll. They can. The problem is you had something valuable and you hid it. Compare that with a resume where I open it and I can immediately tell that this person is targeting an analyst role. They know SQL. They know Excel, they work with Power BI. And right underneath that, I see a bullet that makes me think, okay, this person actually has the skills and knows what to do with it. That's what makes me slow down and makes me want to read more. That is the real goal of the 7-second rule. You're not trying to tell your entire career story in 7 seconds. You're trying to earn the 8th second and then the 9th and then the 10th. And your bullets work the same way. Let's say your first bullet says responsible for creating weekly reports and analyzing data for management. Okay, that is a task. I understand what you were responsible for, but you're still making me ask the question, what were you actually good at? Now imagine if I read automated weekly Excel reporting, reducing preparation time by approximately 5 hours per week. That's a completely different experience. I understand what you did. I see the tool that you use, and now I see something got better because you were there. So now I'm interested to learn more. That is why when people ask me about ATS, I always want to bring them back to the fundamental thing first. Yes, you use relevant If the jobs you want consistently ask for SQL, you should actually know SQL. And SQL should be easy to find on your resume. But I've seen people become so obsessed with beating ATS that they forget the eventual goal is for a human to actually understand them. They'll upload a resume to some random scoring website and it'll say that their resume scored, say, a 43, and then they're panicking. And then the same website says, for $80, we can fix it. Come on, we see the game here. So before you do any of that, I want you to know whether your resume actually makes sense. So ask these questions yourself. If I open it, can I quickly understand their target role? Can I easily and quickly find the skills that make you relevant? Can I see evidence that you've actually created some type of result? And if I can, you have accomplished the most important thing. You've given me a reason to keep reading. That is a 7-second rule. Now, let's say your resume is not terrible. This is actually where most people are. You've got experience, you've taken the courses, you got some SQL. You've probably rebuilt this thing 5 times already. But you're not getting the responses that you expect. Usually, I don't want to start by tearing someone's entire resume apart. I want to remove friction. So imagine I open yours, and at the top underneath your name it says result-oriented professional. What the heck is that? Result-oriented professional doing what? I don't know what job you're actually targeting, so I'm already interpreting instead of evaluating. That's a tiny problem, but tiny problems will stack up. So if you're targeting data analyst roles, underneath your name it should say. Data analyst. That's boring, but it's effective. Now I know where you're going. And if you're changing careers, maybe underneath that you have a short professional summary that helps me connect the dots. Again, not a paragraph about why you're passionate, hardworking, motivated, or detail-oriented and excited to leverage your unique background for a dynamic organization. Everybody says that. But tell me something useful. Maybe you've spent 6 years working in operations and your strength is using Excel reporting and process data to improve performance and identify bottlenecks. Now you're targeting analytics roles. Okay, I understand the bridge. Now I move down to your experience, and the first thing you tell me about your current job is participated in weekly stakeholder meetings. Why? You've got one of the most valuable pieces of real estate on your resume, and the first thing you wanna tell me is that you sat in on meetings? Then 3 bullets later, I discover that you automated a process that used to take your team 8 hours every month. That should have been the very first bullet. And this is a mistake everyone does. They accidentally bury their best evidence. I don't want your bullets ordered based off of the sequence in which you remembered them. I want them ordered based on how much they help me trust that you could actually do the job. Then we'll get to the actual format. After that comes the actual format of the resume, and this is where I'm going to save you some time. Your resume does not need to be creative. Don't bother with the beautiful resume templates from Canva, the ones with 2 columns, colors, photos, icons, none of that. Or worst off, those little skill bars telling me that you're 85% proficient in Python. No one knows what that 85% even means. The best thing you can do for your resume is make it boring. One clean column, clear sections, consistent formatting. Now, once the resume is clean, then we can look at the language. And this is where keywords actually matter. Not because there's some magic number of keywords that you need to beat the robot. They matter because jobs in the same category tend to ask for similar things. So if you look at 10 data analyst jobs and SQL appears in 8 of them, that tells you something. If Power BI keeps showing up, that tells you something else. If they keep asking for dashboards, reporting, Excel, stakeholder communication, whatever it is, and you have those skills, your resume should reflect that. But this is where I want you to be careful. Do not turn your resume into a collection of words that you think your employers want to hear, because eventually someone is going to ask you about them. If you put Python in your skills section because you took a 6-hour course 3 years ago and the interviewer says, cool, well, tell me about your most recent project with Python. Now, your resume did its job. It got you the interview, but then it created a new problem. And if you have nothing to back that up, they're going to immediately see that you were a fraud. 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. So your resume did its job, it gets you the interview, but always make sure you can back up what it says. Now, what do you do if you have no experience? This is a conversation that comes up so often with career changers because they often think that they don't have the experience. But then I'll look at their resume and I see that they've been working since 2015. So I'm like, what the heck happened in the past? And then they'll follow up to say that they don't have data experience. Being new to the data analytics space and having no professional experience are not the same thing. And I see too many career changers erase valuable years of experience because they don't understand the distinction. Maybe you have 7+ years of operations. and you want to move into a pure data analytics role. So you take a SQL course, you learn Power BI, and you earn a certificate, and you build a couple of projects. And then when you make your new resume, suddenly those 2 projects become your entire identity. Meanwhile, those 7 years of actually working inside of business and operations gets pushed to the bottom like they don't even matter. Don't do that, because the thing that you're calling irrelevant experience is the exact thing that makes you different from someone else who just finished the same SQL course. So again, let's say you work in operations. You tell me, I haven't done analytics work. I just manage processes. Okay, well, what processes? Well, we track orders. How? Mostly Excel or on paper. Well, what are you tracking? Volume, delays, mistakes, completion times. Well, why are you tracking those things? So we can see where we're having issues. And then what happens when you see the issues? Well, we usually figure out what's causing it and then update the process. So hold on, you initially tell me that you don't have analytics experience, but in about 30 seconds of drilling down, you described collecting operational data, monitoring performance, identifying trends, finding problems, and making decisions based off of what you found. This is a translation that you must do if you're changing careers into analytics. I'm not telling you to lie about your experience. It's literally about taking time to see that your old job probably contains a lot of pieces for your new job. So you have to take time to find them. This is part of the career transition story that matters to where you're trying to go. But this is also where people waste a lot of time. You're worried about missing something, so you just keep learning another course, another certificate, another tool, and then 6 months have gone by. Meanwhile, you haven't even spent an hour or 2 just sitting down and really thinking through the experience that you already have. So before you ever decide that you need to add more, extract everything useful from what you already have. Now, once you do a true audit of your experience, there are some of you who maybe don't have enough relevant experience. Maybe you just graduated, or maybe your previous experience just didn't expose you enough to analytical work. And after you've translated as much as possible, there's still more room for proof. And that's okay. This is where we create that experience by working with local nonprofits and small businesses doing 30 days of analytical work for free. They've got Excel sheets, fundraising data, customer data, operational data, and they're not looking for some massive machine learning or data science solution. They just need someone to clean up their data, tell them what's happening, and some recommendations to move forward. And when you do that, instead of doing countless Kaggle projects that everyone else is doing, you can go into an interview and say that you've actually worked with actual businesses that was struggling to understand its monthly fundraising performance. You clean up their data and provide recommendations. That's way stronger than the average person in an interview. So long story short, when it comes to experience, first uncover the analytical work inside of the career that you already have. Then if there's a gap, solve a real problem for a local small business or nonprofit, because especially if you're a career changer, I don't want you throwing away the advantage that you've already been spending years building. You already have more experience than you think. You just need to learn how to translate it. Now it's about evaluating the impact in your bullets. So let's say one says, created weekly Excel reports for management. There's nothing technically wrong with that. I know what you did. I just don't know why it mattered. So if we were looking at your resume together, that's where I'd start asking these questions. What did creating that report actually involve? And then you'd say, well, every Friday I had to download a bunch of different files, clean everything, combine it, and then send the final reports to management. Okay, well, how long did it take? You'll say about 4 hours. Did you ever improve that process? Because that sounds pretty long. Yeah, eventually you automated most of it. And after that, you tell me that you automated it, so eventually you got it down to about an hour. There we go. So that is the final part that we're missing from the initial bullet. So we would update it to say automated weekly Excel reporting, reducing preparation time from 4 hours to 1 hour. Same job, same experience. We just went one layer deeper. The first version just tells me what you're responsible for. But the second version, the way better version, tells me what became better because of your work, why you were there. And that's what I mean when I talk about impact in your bullets. It doesn't have to mean you made the company $10 million. In this case, you saved the company time, which is useful. Maybe somewhere else you reduced errors. Or you made a process faster. Maybe you gave a team access to information that they didn't have before. The point is that I should understand the result of the work, why things got better because of your activity. And this usually leads to people saying, but I don't remember all of my numbers. Well, of course you don't. You probably weren't sitting there for 3 years thinking, I better save this number for my future data analyst resume. So I don't want you making numbers up. I just want you to actually responsibly estimate. So once every bullet effectively has quantifiable impact, your resume stops reading like a job description and starts giving me evidence that you can actually create value. Now, once we fix your resume bullets, we eventually reach a point to where the resume itself can only do so much. You clean it up, you translate your experience, you show your impact, and then you start applying. But then nothing may happen. This is where I start seeing people fixing the wrong problems due to just desperation. Maybe 3 weeks go by without an interview and you start thinking Well, maybe I need to add something else. So you start learning maybe Python or some other random tool rather than rebuild your portfolio, then change your resume again. But what if Python or that other random tool was never the actual reason you wasn't getting interviews? Before I change anything, I want to know where in your job search is actually breaking. So you come to me and say, I've applied to over 200 jobs and haven't gotten a single interview. Okay, I know that's frustrating, but it gives us good data still. You're not getting far enough in the process for interview techniques to be the main issue. So let's look earlier in the process. I want to see what jobs you're applying to. Maybe you're targeting roles that's asking for like 5 to 7 years of direct analytics experience when your background doesn't support that yet. Or maybe the roles actually make sense, but when I open your resume, I still can't see how your previous career connects to them. That gives us something specific to work on. Now imagine you telling me, I'm getting interviews, I've had 8, and I still can't land an offer. Now that's also a completely different problem. At that point, I'm much less interested in tearing apart your resume The resume is getting you in the room, but once you're in the room, your story is falling flat. So I want to hear how you're explaining your background, how you're walking through your projects and experience, how are you handling the technical portions. I want to identify where in those interviews are you breaking down. It's all about identifying the actual bottleneck, and that's why I think of getting hired in data analytics like a success equation. A job offer is a final result, but there are different things feeding into it. So the feedback that you're getting from the market should help you tell you what to work on next. Otherwise, you're just going to end up doing what a lot of frustrated career changers do. When something doesn't immediately work, you want to change everything. When months go by without a job and the scary thought comes up saying, what if I'm just wasting my time? That's exactly why I don't want you randomly adding more work. I want the next thing that you spend time on actually solving the problem that you actually have. This is also why you shouldn't fall into the trap of comparing your timeline to everyone else's. I've seen people get hired in 30 days, 45 days, 90 days, even some take 10 months. So when someone posts on LinkedIn that they got a data analytics job in 30 days, good for them, but you don't know what they walked in with. Maybe they already had 5 years of highly transferable experience. Maybe they knew somebody that knew somebody. Maybe they were targeting a completely different market. Their 30 days doesn't tell you what you should do tomorrow. Your own results do, which is one of the reasons why I like Stoic philosophy. And one of my favorite books of all time is The Art of Living from Epictetus. The idea I keep coming back to is separating what you can control from what you can't, because there's absolutely things in the job search that you cannot control. A company can freeze a role, they can hire internally, the market can be tough, but those things don't tell us what to work on next. Your results do. So if you're not getting interviews, look at your resume and your actual skills. If you're getting interviews but not offers, work on your interview techniques. That's much more useful than assuming the whole strategy is broken every time you get rejected. So when things don't move as quickly as you hoped, figure out what's actually wrong, where you're stuck, and work on that. Now, if you ever want me to look at your resume to see exactly why you're not getting hired, click the first link in the description. We give our students unlimited feedback on their resumes and unlimited mock interviews, etc. Now, once you've got a resume built, there's still a handful of other levers that you can pull to increase your chances of getting hired. So watch this video next, and I'm going to show you exactly what they are. I'll see you there.