139: The TRUTH About Landing a Data Job (Hiring Managers Tell All)

As the host of the number one data podcast on Spotify, I've had the opportunity to interview a lot of cool people, including a lot of data hiring managers who you'll hear from in this episode. And they've given really great advice on how to get hired in the data space. In this episode, you'll hear the best snippets from those hiring managers and get actionable advice on how actually to land a data job straight from the hiring manager's mouth. Let's get into it. Our first hiring manager you may have seen before on YouTube. It's Alex, the analyst or Alex Freyberg, and he has really great hiring experience from when he was at his corporate job. And in this example here, I want you to pay attention to what he thought mattered most when getting hired. It's probably going to surprise you when you were hiring people, like what was important in a candidate for you? Like what was the first few things you were looking at?

Yeah. I'm going to be like brutally honest because I think people tend to sugarcoat this process and the hiring process is. A lot of people on like LinkedIn or YouTube will tell you like the sugar coated version. I'm going to tell you what I truly looked for. I was on a hiring team when I was a data analyst. I was the one who gave the technical interviews. And so that was my part of the hiring team. And then you're right. I became a hiring manager. And so then as the hiring manager, I did the whole process and usually brought in like my boss as well for some like the final interviews during the hiring. The, when I was on the hiring team during that process, we were mostly hiring data analysts. I eventually started when I was a hiring manager was doing developers, database engineers, or data engineers, and then data analysts as well. So that was a little bit different, but on the hiring team, just for data analysis, we always looked for someone who had a good personality and most people will tell you. I've seen it online. They're like, well, you know, as long as you have the right skills and you get in there and you smile, you know, that's a good, that's what you need to do. I, I think when you're on a team, you really do look for someone who's going to fit well with your team. And so, yeah. So, I always kind of gravitated towards people who are more outgoing, and is that 100 percent fair? No, I don't think so, but hiring, the hiring process isn't super fair. And so, the people who are more outgoing, I tended to gravitate to, and so did my whole team. Our whole team was very outgoing, very social, and so we didn't want to, someone come in and have a very different flow to them, or, or personality to them. And so that's just like a brutal truth, you know, people always say diversity is like crazy good, but for personality, I think. The, the, that piece of it is actually the flow of the team and how that, uh, people gel together is really important. The second thing we looked for is, uh, being able to articulate well, their skills, abilities, and their experience. And so oftentimes we'd have people come in and SQL is really important. When I was a, on the hiring team, SQL was the most important skill because we used it like really in depth for a lot of our processes and so people would come in and I was like, well, tell me how you've done. You know, data cleaning or tell me how you use SQL. And if people can articulate really well, like here's how I use it. They were just like, Oh, well, you know, I've, I've taken a few courses in my job. I use SQL, but I don't really use it that much. And they would kind of beat around the bush. And I'd be like asking really pointed questions. They couldn't articulate those questions that I would think is if you've really used SQL well, you should know how to answer those questions because I can tell you. Even at that time, I could be like, well, here's the process that I would take to clean data. Here's how I do that in SQL. Here are, you know, here are the exact steps. That's what you need to be saying and people would beat around the bush and wouldn't want to say things. And that was always a big red flag. And then the last thing that I think we would look for is someone who is technically proficient. So I was the one conducting the interviews. We would always do some type of whiteboarding and then some type of general technical interview question. So the whiteboarding, you know, um, Uh, um, uh. Uh, um, it really is the number one thing that I, like this is straightforward stuff. And we were hiring at like the mid level. So mid level SQL on their resume for three years. This should be a no brainer. Like, like, This is like super simple, like, like just aggregating something with a group by nothing crazy or just a simple joint, just combine these two tables and people would have trouble with it. And that was an immediate red flag. Like we couldn't hire them. And so those three things I would say are the biggest things that we look for and like really ranked on during those interviews. But if I'm being like completely honest, the personality thing was like 50 percent of it. If you have a good personality, then that like really puts you higher up. And it's not just like I don't know, personality is very objective and so it's hard to describe, but just somebody who's more outgoing, very friendly. That is like kind of what we were looking for. The being able to articulate and the technical interviews is the other 50%. So those two things were still very important, but if they looked like they were very teachable. If they looked like they were like really driven and we were like, you know, they may not be where we want them today, but I was like, that person will be good in like a month. We would still hire them. And we did that for one of our business analysts who we hired, um, who were, he kind of knew SQL, but his job wasn't as intensive as Eagle for his, that, for that position. So we were like, Hey, let's, Let's hire him cause he would fit really well with our team. And we trained him like I, he was my mentor, my, my, my mentee on my team. So I trained him in SQL and he, within like a month he was up and running and I didn't really have to help him that much anymore. So again, it was like that trainability piece, the, um, the attitude, the, uh, how driven they were did play a big role in who we hired. So I know I was long winded on that, but they, you know, that's a, that's a really tough. Process to our to talk about, you know,

it is I think you did. I think you did really well And I don't I mean although it was you know, you did talk about maybe the extrovert versus the introvert I don't think it was too brutal I think I think it's like an opportunity you have a good personality and you maybe aren't the best technical person on planet Earth You still have a chance.

Yeah. Well, I talked to a lot of people who give me that feedback They're like, well, I'm a really big introvert I get really nervous and it's true and they're like, how can I get past that? and so So there are things that you can do. I really believe in practicing before interviews, mirror, looking in a mirror and practicing smiling. Cause believe it or not, I was that person back in interviews. Um, I used to be very, very nervous and very scared for interviews. Um, I used to be much more introverted than I am now. I've worked through a lot of that as I got into the workplace just by having to, in order to like succeed on teams. But I used to be very, very, very nervous. And, and so what I would do is I'd practice in a mirror and then my, I'd practice with my wife. And so she'd be like, Oh, you're doing that weird, you're, you're doing this weird thing. And she'd be really honest with me. And so I needed somebody who could give me that feedback. And that helped immensely in interviews. So I'm kind of, I feel like I can point even to myself as like a testament of someone who, who got over that and was able to push through. And then I was really able to. Understand, like I have to do that in order to really be successful in an interview.

All right. So maybe not exactly what you expected. Personality really matters. And I think that's actually a positive thing. Of course you have to have the skills that is like the bare minimum, but your ability and your personality can actually set you apart from other candidates. Now you might be thinking, well, that's great. And like I said, I think it's a positive thing because it means that there's room for all of us in the data world. Some of you guys will be thinking like Alex said, Oh, I'm introverted. I don't have that great of a personality or I'm kind of scared to share my personality. And I don't think you have to become extroverted. I'm actually an introvert, believe it or not. But I think really practicing in those interviews and at least just coming off very personable in the interviews is really important. I love what Alex said about the mirror. I actually built a software. It's called Interview Simulator that lets you practice. Interview questions, uh, with the hiring manager in front of you, like a mirror, and you actually record yourself and get feedback on your responses. If you want to check that out, it's at interview simulator. io or I'll have a link in the show notes down below. Okay. Our next hiring manager is Megan McGuire. In this snippet, she's going to walk us through what it's like to actually hire someone in the data world from beginning to end. She'll talk about posting the job, how many applicants she got, how many people The recruiter talked to how many people she talked to as the hiring manager and kind of what the next steps and how they ultimately hired someone who actually didn't have all that traditional of a data background. Let's take a listen. Okay. So you write this job description, you hand it over HR. They, they post it somewhere on the internet somewhere and applications start to come in. So can you walk us through how many applications, how long you, maybe you guys have the job open and how many applications you got?

Yeah, I think we had this role listed for like a week. We didn't give it long, because we got 285 applications within a week. Honestly, when I looked at them, and I looked at every single one of them, like, looked at every resume, probably about 70 percent of that applicant pool could have been successful in the role. Again, it's an entry level role. A lot of this is about what you're able to learn and like what you've shown some promise in so far. So yeah, most of these people honestly could have done pretty well in the role. So that makes it really hard to narrow down. Honestly, when I hire a senior analyst, that's a lot easier because I can go through and see that like, you don't have the body of experience to support that you've done this for a long time. You don't have the portfolio. You don't have the projects. When I'm looking at a junior analyst, I assume you're not going to have those things. So I have to parse out on a lot more stringent criteria. So if you don't have experience in the tech stack that I'm looking for, 285 applicants, if only half of those have experience with Tableau, which is what we use as our visualization tool, I'm going to talk to the half with Tableau before I talk to the other half with Power BI or Looker. You have to prioritize on these things just because there's a lot of people coming through. So out of that 285, I think we had 12 talk to our recruiter. That's our next stage is we do our recruiter screen. I'm a big believer in the hiring process. Like I'm not going to ask you to do a technical screen before we've put some time forward to you. We need to have that sort of give and take. So you talk to our recruiter at that stage. After that, we had. Five candidates exit because they either didn't respond, or location, or salary requirements didn't line up. So then we had seven candidates take our code assessment. We do a SQL test on CodeSignal to review candidates skills. I really enjoy having something, like, technically grounded, where I'm able to see the code you can write. It doesn't really work well to do, like, a quick Tableau assessment, but SQL's such a core skill, and it's really easy to test with a lot of SQL questions. We're doing some grouping. I think. There might be a window function question on there. So at that stage, actually everybody passed our SQL interview, but we did have one candidate accept a different offer at that stage and exit. Yeah. Our average completion time on that stage was 24 minutes. My goal is also to keep that stage pretty short. I don't want to ask you for like a six hour test. You're applying for lots of jobs, especially at the entry level. I'm not trying to keep you for many, many hours. The stage actually that we moved to was my hiring manager interview. And in that stage, I'm asking usually some more problem solving questions. So I'm going to ask you about something in your portfolio, something that you've gone deep on, and ask you things like, how would you expand that project? What else are you curious about this project that you might've worked on in your portfolio? If you were rebuilding it, like, what would you do differently this time? What other data would be helpful for driving decisions? Those sorts of questions to dive deep. Again, like, I'm not asking you about all of your experience in data analytics. I assume that you don't have that applying for an entry level role. I talked to six in the higher edger state and four of them went through. The biggest gap for the two candidates who exited there, I think was like visualization and data exploration skills. So then we moved into the team technical interview where I have two of the senior analysts on my team go through much more technical questions. So in that stage, you're going to see like, let's walk through your portfolio project and talk about like. How you build this in Tableau, you put something on Tableau public, we're going to talk through the stages of building it. So they're going to be vetting your technical skills with a lot more detail. This shouldn't be a scary stage. Just feel confident speaking to the stages of not only how you did things, like we're not going to ask which button did you push, but think about the methodology and why you chose to build something a certain way. So like, if you chose to do a calculation in SQL versus in a data visualization tool. Why did you do that? And how did you go about figuring that out? Those are going to be the sorts of questions to talk to there.

Okay. Awesome. So then you're, you're analysts, you're kind of doing this like team interview. Now let me ask you this. I mean, they've done this probably a few times, maybe in their careers, right? Do you give them questions? Do they come up with their own questions?

They come up with their own questions. I talk to them primarily about the goals. This is very similar to my management style in general is I want to talk to them about the goals. What are we trying to find out? To bring it all back into the data world, interviewing is a form of getting data. This is a means of data collection. So I talk to them about like, what do we want to learn about the candidates at this stage? And I will help them with writing questions if they need it. But for the most part, I'm telling them like, I want to learn about their technical skills. I want to learn about how they go about solving problems. I want to learn about how comfortable they feel in this system. And you should be able to come back and tell me about. After that, that's actually our last stage. So we do that technical interview and then I'm reviewing all of the feedback. So our system. As we collect scorecards after every interview, and then I have access to review all of them. So I can see something that's been scored relatively objectively across every interview and every candidate and sort of evaluate how that adds up. So that'll be scoring on things like technical skills. How are your SQL skills? How are your Tableau skills? But it'll also include things like problem solving and other soft skills. How are you as a communicator? And I can evaluate against all of that. In this case, I had two candidates that I was sort of debating between in the final stage. And then I made the call on who to extend an offer to. Really the differentiator for the candidate who got the offer is we're an education company. We're here to help people upskill, learn data analytics, was that she had prior experience in education. So all of her technical skills were great. Her communication skills were great. Her portfolio was great, but I had multiple candidates who met all of that. So her differentiator was really that education experience that was really helpful for us. It was something that set her apart and made her like the perfect candidate for us.

And I want to emphasize that here. I work with a lot of teachers who want to get into data analytics and a lot of them are fearful. Hey, I don't have a technical background. I come from an unusual background, but in this case, that non technical background, the unusual background was actually kind of the superpower that got her the job or him the job.

Yeah, like it's super, super helpful. I can combine that portfolio, combine all the things that you've learned about data analytics with the other things that you know. Somebody out there is making ed tech software that needs to be sold to teachers. Like you understand teaching, you understand the education world. You can apply that knowledge to data analytics in that setting. Be the perfect candidate for that company rather than a pretty good candidate for a whole sea of companies. And the same could be applied again for like, if you've got retail experience or customer service experience, you might look at a customer service analytics role, which there are plenty. Take your prior experience in customer service and apply that to analytics. I did it myself. Like that's how I got into analytics was I studied healthcare in my undergraduate program. And I took an analytics role at a healthcare company. So when you can sort of combine those things, it makes a much more powerful profile, makes you a much stronger candidate. Again, like you don't have to be okay for everybody. Okay. For everybody will get you a lot of like looks, but you'll get the offer more when you can find a way to make yourself like just right for one company, those things.

Okay. How awesome was that behind the scenes? I'm going to it's like to actually hire someone in the data space. 285 applicants just erased half of them because they didn't know Tableau. That's why it's so important to list all data skills pretty much on your resume and your LinkedIn. 12 spoke to a recruiter and out of those 12, only 7 made it to the next stage, which was actually a SQL little coding test. Everyone pretty much passed the coding test. One person got offered a job and so dropped out. So six people talked to the hiring manager, which was Megan in this case, kind of a behavioral interview like you heard. Out of those six, she kicked two out after that and finally had four interview with her team. The team was part of the process, which I think is really neat. And it goes back to what Alex was talking about earlier, how you really do need to mesh with the team. Well, out of those four, two of them kind of stood out, but that couldn't really choose between the two. And the benefit of the doubt went to the person whose domain experience, like their past experience that wasn't data related would help the team. In this case, it was someone with an education background, and this was an education company. And so that's who they ended up hiring, which is awesome. And I think for you, you should really be thinking about. You know, how can I use my domain and my previous experience to help me land my next job? Hopefully your hiring manager is as good and as kind as Megan, because I think she hired very well in this case. The next hiring manager we are going to hear from is Jesse Morris. And I want you to pay attention to what he thinks is most important in the hiring process, because once again, I don't think you're going to be able to guess what it is. When you've hired some of those entry level people, what stood out the most to you in those hiring processes?

Yeah, that's a great question. And I think, you know, if you take anything from today's conversation, I think it's around this. And, you know, again, I think it gets lost. You've got to be the most technical in the room or, you know, your ability to build a dashboard and make it a work of art. You know, that's like the most important. I actually don't think that's the case. And I actually think, Avery, you and I talked about this about, like, how a lot of teachers make great analysts. And I think there's a lot of truth to that because ultimately when I think when it boils down to it Really? It's it's a couple of key things one It's the ability to tell stories and be succinct and that is not that's not just a data skill set That's a life skill set You know If you look actually my original background is in sales like I actually I should say my second job My first job was I was a data analyst and then I realized I needed to get presentation skills and the ability to tell stories So I went into sales for a few years. And so I think, you know, that skillset, the ability, but I think you can get that in a myriad of ways, you can be a great writer, you can, there's, there's so many different ways you can get that skillset, but I think that's such a big one, especially. You know, a lot of my time is spent communicating to executives and to leadership teams and to boards. Like I spent a lot of time telling stories to the board and that's really key is that ability to kind of boil things down and to here's the most important and then you can work back. Ultimately, like people, when they get curious about data, that's when they start asking kind of your next layer of questions and you, you can make that, you can bring that curiosity of the life through storytelling. The other one, which is probably a little bit. Less common you want here, but this is something that just continues to even today even with senior analysts It doesn't matter what level of analysts you are but tenacity and mental toughness Wow, so that's a really funny one I tenacity to me like in my world. I work in these smaller called startup type Uh, technology companies. And so we're moving at really fast pace, but we don't get weeks to work on projects. So if you work in any large corporate companies, you're going to get that. And that's okay. I think ultimately it's good to know, like, what are, what type of environment you're in? And so if you don't work necessarily well under pressure and some of these things I'm about to talk about, that's okay, then you're probably maybe better designed to work at larger companies where you're given the freedom to like, sit down and work on things for weeks. The environment I work in, we're not given that time. And so the ability to, you know, change prioritization on a dime, to juggle nine different projects at once. If you talk to my data analyst today, like this is the reality. Like we, this week, we came into the week with a plan and by Monday afternoon, you know, it was Monday morning during our standup and by Monday afternoon, that plan got halfway derailed, right? And so it's a reprioritization game and that's not for everybody. I mean, I think ultimately. You know, that's a tough thing to wrap your head around and not get frustrated. And, and I think you and I talked about this before, but it's that like knowledge of, I understand what perfect looks like or really phenomenal looks like, but I also understand what good enough is. And I think that skill set, that's a really important one. And that's not like, you know, I'm going to learn this by watching X, Y, and Z. I think that's something that you actually have to work towards and build up that, that mental toughness. I actually think failure, you know, it's easy to look at a resume and be like, Oh, all this stuff went great. I was a founder, you know, at a tech company. Good for me. I also failed at that tech company. Right. I learned a lot of things through trial and error and that I think it's the same for all of us. And so those would be some of the things I think that really stand out to me when you boil it down to like some of the key pieces behind it. It's an attitude, right? Like it's that willingness to say, Hey, I messed up here and that's okay. Like, cool. What'd you learn from it? How do we make it better? How can I help? But I think, you know, those are ultimately some of the, when you boil it down to some of the things that I look for, no matter what stage you're at within it. And then I think. You know, on the specifically on the starting out analyst in particular, you know, I think just a, again, perspective is an interesting one, but did you have a sales background or did you work for, I mean, maybe you're working in retail. Did you work for Banana Republic during college where you were like, all of those things, perspective and data is everything. And what I mean by that is like your ability to speak into it from the person who's asking the question or the departments or the leaders that are asking the questions. Right. Because as long as you've got just that various perspective, that actually has a lot more value. I think sometimes the technical even does.

Yeah. And I hope people just heard what you said, because I think that's very impactful, you know, just to kind of rehash them a bit. It's not necessarily how technical you are that lands you the job. Because I think you said this phrase when we first originally talked that the technical stuff. It's kind of expected. That's like you, you have it or you kind of don't. Right. And it's really your storytelling, your grit, your attitude that separates you, which I think for all of you guys listening who want to be aspiring data analysts, that should be really rewarding because you can have grit. You can be, you know, you can be authentic. You can try hard. You can have passion. You can become a good, you know, storyteller. Those aren't like necessary, like you have to be spending 25 years of your life in SQL to know how to master everything. Right. That's really, in my opinion, enlightening and refreshing to hear because it can be like, I think most people take the data career job hunt way too skill heavy. Of course, skills are important. Right. But like, they're not everything. And I think you kind of just said that basically, they're not everything. All right. Hopefully you're catching the drift at this point. It was a pretty similar theme here that your technical skills, of course, they're important. It's the bare minimum. It's actually things like your storytelling, the ability to be succinct that sets you apart. And that's something you're probably thinking, Avery, you're not very good at that. I've listened to your podcast episodes. I've watched your YouTube videos and you're not very succinct. And it's true. It's something I'm still working on. And I think it's a lifelong journey, but once again, this is like Jesse said, a life skill, not necessarily a data skill. And so that's something that we can practice and we can get better at. And no matter how technical or how non technical you are. It's something that you can improve on every day and work at. Jesse also brought up tenacity and mental toughness, and this is something that we can all do. One thing that I do is I actually take ice cold showers and baths to try to increase my mental toughness. Kind of weird, but it works for me. The last hiring manager we are going to hear from is Andrew Madsen. I want you to pay attention to what he says because he kind of repeats what has already been said, but he adds one really important point. Part, uh, that the other ones haven't talked about as much, and that is projects, which is the P part of the SBN method. Let's take a listen. I wanted you to walk us through the idea of when you're hiring a data analyst, you know, what's really important to stand out as a candidate. What can these listeners do to To stand out and the data analyst job search.

Yeah. My thoughts on this have evolved over time. So the data analyst position has just grown and grown and grown as our needs for quality data analysts have really permeated every industry. So there's a lot of opportunity there before when I was new at hiring data analysts and I was new into data myself, I really was focused on the technical skills. I was looking at whatever my stack was, like we use Tableau, whatever it is. And I was looking for applicants who match that data stack. That's how I began looking for applicants. Now what I look for if I was hiring a data analyst, I focus much more on the person. I look for somebody who's curious. I look for somebody who's resilient. I look for somebody who's going to mesh well with the team because data analytics is a team sport. You know, one person who just isn't a team player can really throw off the whole dynamic and ultimately the work and the business insights that we're trying to drive. So less important to me is your specific technical skill set. You know, if you know Tableau really well and we're using Power BI, that's totally fine with me. But you're demonstrating that ability to learn. And some of the ways that you can do that, like Avery always talks about, are projects. I love to look at projects. I love to see interesting projects. We've all seen the Titanic dataset, and I don't mind if you use that, but I want to see something that you're interested and passionate about, and I want to learn about that with you. And then if we're interviewing, I really want you to tell that story, because the ability to communicate as a data analyst is so important. You know, I don't want to have to go to the stakeholders and explain what you were doing. I want you to go and represent yourself and present your insights and build those relationships. So if you can have something you're passionate about, you uncovered some insights and you can communicate those in a story and a narrative that's engaging. Those are so important. Those will really set you apart.

Okay. That's awesome. A lot to unpack there. I think we, as data analysts, candidates often over index. On how much, you know, the technical skills matter and the technical skills do matter. There's people who are willing to take a chance on you and you have to show them that you're more than just, you know, some NPC. I don't even know what that even, what that means right now. What does that mean? A non role playing, I don't even know what it means, but it's like a non player

character. Yeah.

Non player character. You have to show some sort of passion, some sort of personality, some sort of drive, some sort of like, And that can be even your grit, your communication, you know, what you like about projects. And it's just interesting that we've had a couple of different data hiring managers on the podcast now, and they've all let off with a very similar message. There you have it, folks, advice straight from hiring managers on how to land your next day at a job. If you want even more On how to land a data job, I highly suggest checking out my newsletter. Every week I send you a tip that helps you take the next step in your data career. You can subscribe at datacareerjumpstart. com or in the show notes down below. And if you want even more help on your data journey, consider joining the data analytics accelerator, which is my ten week bootcamp to help you land your first data job. You can find that link in the show notes down below.