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

Avery: 00:00:00

As the host of the number one data podcast on Spotify, I've

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had the opportunity to interview

a lot of cool people, including

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a lot of data hiring managers who

you'll hear from in this episode.

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And they've given really great advice

on how to get hired in the data space.

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In this episode, you'll hear the best

snippets from those hiring managers

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and get actionable advice on how

actually to land a data job straight

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from the hiring manager's mouth.

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Let's get into it.

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Our first hiring manager you

may have seen before on YouTube.

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It's Alex, the analyst or Alex Freyberg,

and he has really great hiring experience

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from when he was at his corporate job.

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And in this example here, I want you

to pay attention to what he thought

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mattered most when getting hired.

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It's probably going to surprise you

when you were hiring people, like what

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was important in a candidate for you?

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Like what was the first few

things you were looking at?

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Alex: Yeah.

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I'm going to be like brutally honest

because I think people tend to sugarcoat

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this process and the hiring process is.

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A lot of people on like LinkedIn

or YouTube will tell you like

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the sugar coated version.

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I'm going to tell you

what I truly looked for.

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I was on a hiring team

when I was a data analyst.

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I was the one who gave

the technical interviews.

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And so that was my part

of the hiring team.

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And then you're right.

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I became a hiring manager.

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And so then as the hiring manager, I did

the whole process and usually brought

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in like my boss as well for some like

the final interviews during the hiring.

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The, when I was on the hiring

team during that process, we

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were mostly hiring data analysts.

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I eventually started when I was a

hiring manager was doing developers,

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database engineers, or data engineers,

and then data analysts as well.

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So that was a little bit different,

but on the hiring team, just for

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data analysis, we always looked for

someone who had a good personality

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and most people will tell you.

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I've seen it online.

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They're like, well, you know, as long

as you have the right skills and you get

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in there and you smile, you know, that's

a good, that's what you need to do.

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I, I think when you're on a team,

you really do look for someone who's

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going to fit well with your team.

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And so, yeah.

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So, I always kind of gravitated

towards people who are more outgoing,

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and is that 100 percent fair?

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No, I don't think so, but hiring,

the hiring process isn't super fair.

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And so, the people who are more

outgoing, I tended to gravitate

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to, and so did my whole team.

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Our whole team was very outgoing, very

social, and so we didn't want to, someone

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come in and have a very different flow

to them, or, or personality to them.

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And so that's just like a brutal

truth, you know, people always

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say diversity is like crazy good,

but for personality, I think.

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The, the, that piece of it is actually

the flow of the team and how that, uh,

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people gel together is really important.

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The second thing we looked for is, uh,

being able to articulate well, their

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skills, abilities, and their experience.

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And so oftentimes we'd have people

come in and SQL is really important.

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When I was a, on the hiring team, SQL was

the most important skill because we used

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it like really in depth for a lot of our

processes and so people would come in and

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I was like, well, tell me how you've done.

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You know, data cleaning or

tell me how you use SQL.

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And if people can articulate really

well, like here's how I use it.

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They were just like, Oh, well, you know,

I've, I've taken a few courses in my job.

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I use SQL, but I don't

really use it that much.

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And they would kind of

beat around the bush.

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And I'd be like asking

really pointed questions.

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They couldn't articulate those

questions that I would think is if

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you've really used SQL well, you

should know how to answer those

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questions because I can tell you.

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Even at that time, I could be

like, well, here's the process

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that I would take to clean data.

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Here's how I do that in SQL.

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Here are, you know, here

are the exact steps.

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That's what you need to be saying

and people would beat around the

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bush and wouldn't want to say things.

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And that was always a big red flag.

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And then the last thing that I

think we would look for is someone

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who is technically proficient.

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So I was the one

conducting the interviews.

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We would always do some type of

whiteboarding and then some type of

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general technical interview question.

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So the whiteboarding,

you know, um, Uh, um, uh.

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Uh, um, it really is the number

one thing that I, like this

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is straightforward stuff.

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And we were hiring at like the mid level.

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So mid level SQL on their

resume for three years.

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This should be a no brainer.

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Like, like, This is like super simple,

like, like just aggregating something with

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a group by nothing crazy or just a simple

joint, just combine these two tables

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and people would have trouble with it.

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And that was an immediate red flag.

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Like we couldn't hire them.

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And so those three things I

would say are the biggest things

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that we look for and like really

ranked on during those interviews.

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But if I'm being like completely

honest, the personality thing

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was like 50 percent of it.

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If you have a good personality, then

that like really puts you higher up.

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And it's not just like I don't know,

personality is very objective and so

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it's hard to describe, but just somebody

who's more outgoing, very friendly.

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That is like kind of

what we were looking for.

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The being able to articulate and the

technical interviews is the other 50%.

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So those two things were still

very important, but if they looked

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like they were very teachable.

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If they looked like they were like

really driven and we were like, you

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know, they may not be where we want

them today, but I was like, that

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person will be good in like a month.

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We would still hire them.

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And we did that for one of our

business analysts who we hired, um,

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who were, he kind of knew SQL, but

his job wasn't as intensive as Eagle

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for his, that, for that position.

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So we were like, Hey, let's,

Let's hire him cause he would

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fit really well with our team.

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And we trained him like I, he was my

mentor, my, my, my mentee on my team.

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So I trained him in SQL and he,

within like a month he was up and

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running and I didn't really have

to help him that much anymore.

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So again, it was like that trainability

piece, the, um, the attitude,

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the, uh, how driven they were did

play a big role in who we hired.

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So I know I was long winded

on that, but they, you know,

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that's a, that's a really tough.

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Process to our to talk about, you know,

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Avery: it is I think you did.

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I think you did really well And I don't

I mean although it was you know, you did

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talk about maybe the extrovert versus the

introvert I don't think it was too brutal

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I think I think it's like an opportunity

you have a good personality and you

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maybe aren't the best technical person

on planet Earth You still have a chance.

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Alex: Yeah.

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Well, I talked to a lot of people who

give me that feedback They're like,

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well, I'm a really big introvert I

get really nervous and it's true and

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they're like, how can I get past that?

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and so So there are

things that you can do.

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I really believe in practicing

before interviews, mirror, looking

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in a mirror and practicing smiling.

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Cause believe it or not, I was

that person back in interviews.

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Um, I used to be very, very nervous

and very scared for interviews.

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Um, I used to be much more

introverted than I am now.

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I've worked through a lot of that as I

got into the workplace just by having

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to, in order to like succeed on teams.

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But I used to be very, very, very nervous.

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And, and so what I would do is

I'd practice in a mirror and then

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my, I'd practice with my wife.

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And so she'd be like, Oh,

you're doing that weird, you're,

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you're doing this weird thing.

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And she'd be really honest with me.

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And so I needed somebody who

could give me that feedback.

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And that helped immensely in interviews.

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So I'm kind of, I feel like I can

point even to myself as like a

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testament of someone who, who got over

that and was able to push through.

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And then I was really able to.

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Understand, like I have to

do that in order to really be

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successful in an interview.

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Avery: All right.

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So maybe not exactly what you expected.

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Personality really matters.

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And I think that's

actually a positive thing.

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Of course you have to have the skills

that is like the bare minimum, but your

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ability and your personality can actually

set you apart from other candidates.

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Now you might be thinking,

well, that's great.

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And like I said, I think it's a positive

thing because it means that there's

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room for all of us in the data world.

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Some of you guys will be thinking

like Alex said, Oh, I'm introverted.

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I don't have that great of

a personality or I'm kind of

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scared to share my personality.

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And I don't think you have

to become extroverted.

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I'm actually an introvert,

believe it or not.

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But I think really practicing in

those interviews and at least just

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coming off very personable in the

interviews is really important.

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I love what Alex said about the mirror.

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I actually built a software.

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It's called Interview Simulator

that lets you practice.

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Interview questions, uh, with the

hiring manager in front of you, like a

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mirror, and you actually record yourself

and get feedback on your responses.

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If you want to check that out,

it's at interview simulator.

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io or I'll have a link in

the show notes down below.

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Okay.

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Our next hiring manager is Megan McGuire.

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In this snippet, she's going to

walk us through what it's like

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to actually hire someone in the

data world from beginning to end.

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She'll talk about posting the job,

how many applicants she got, how many

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people The recruiter talked to how

many people she talked to as the hiring

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manager and kind of what the next

steps and how they ultimately hired

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someone who actually didn't have all

that traditional of a data background.

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Let's take a listen.

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Okay.

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So you write this job

description, you hand it over HR.

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They, they post it somewhere

on the internet somewhere and

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applications start to come in.

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So can you walk us through how

many applications, how long you,

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maybe you guys have the job open

and how many applications you got?

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Megan: Yeah, I think we had this

role listed for like a week.

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We didn't give it long, because we

got 285 applications within a week.

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Honestly, when I looked at them, and

I looked at every single one of them,

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like, looked at every resume, probably

about 70 percent of that applicant pool

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could have been successful in the role.

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Again, it's an entry level role.

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A lot of this is about what you're

able to learn and like what you've

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shown some promise in so far.

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So yeah, most of these people honestly

could have done pretty well in the role.

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So that makes it really

hard to narrow down.

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Honestly, when I hire a senior analyst,

that's a lot easier because I can go

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through and see that like, you don't

have the body of experience to support

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that you've done this for a long time.

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You don't have the portfolio.

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You don't have the projects.

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When I'm looking at a junior

analyst, I assume you're not

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going to have those things.

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So I have to parse out on a

lot more stringent criteria.

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So if you don't have experience in

the tech stack that I'm looking for,

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285 applicants, if only half of those

have experience with Tableau, which

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is what we use as our visualization

tool, I'm going to talk to the half

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with Tableau before I talk to the

other half with Power BI or Looker.

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You have to prioritize on these

things just because there's a

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lot of people coming through.

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So out of that 285, I think we

had 12 talk to our recruiter.

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That's our next stage is

we do our recruiter screen.

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I'm a big believer in the hiring process.

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Like I'm not going to ask you to

do a technical screen before we've

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put some time forward to you.

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We need to have that

sort of give and take.

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So you talk to our

recruiter at that stage.

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After that, we had.

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Five candidates exit because they

either didn't respond, or location,

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or salary requirements didn't line up.

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So then we had seven candidates

take our code assessment.

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We do a SQL test on CodeSignal

to review candidates skills.

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I really enjoy having something,

like, technically grounded, where I'm

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able to see the code you can write.

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It doesn't really work well to do, like,

a quick Tableau assessment, but SQL's

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such a core skill, and it's really easy

to test with a lot of SQL questions.

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We're doing some grouping.

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I think.

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There might be a window

function question on there.

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So at that stage, actually everybody

passed our SQL interview, but we

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did have one candidate accept a

different offer at that stage and exit.

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Yeah.

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Our average completion time

on that stage was 24 minutes.

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My goal is also to keep

that stage pretty short.

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I don't want to ask you

for like a six hour test.

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You're applying for lots of jobs,

especially at the entry level.

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I'm not trying to keep

you for many, many hours.

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The stage actually that we moved

to was my hiring manager interview.

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And in that stage, I'm asking usually

some more problem solving questions.

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So I'm going to ask you about something

in your portfolio, something that you've

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gone deep on, and ask you things like,

how would you expand that project?

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What else are you curious about

this project that you might've

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worked on in your portfolio?

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If you were rebuilding it, like, what

would you do differently this time?

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What other data would be

helpful for driving decisions?

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Those sorts of questions to dive deep.

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Again, like, I'm not asking you about

all of your experience in data analytics.

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I assume that you don't have that

applying for an entry level role.

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I talked to six in the higher edger

state and four of them went through.

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The biggest gap for the two candidates

who exited there, I think was like

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visualization and data exploration skills.

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So then we moved into the team

technical interview where I have two

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of the senior analysts on my team go

through much more technical questions.

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So in that stage, you're going to

see like, let's walk through your

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portfolio project and talk about like.

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How you build this in Tableau, you put

something on Tableau public, we're going

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to talk through the stages of building it.

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So they're going to be vetting your

technical skills with a lot more detail.

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This shouldn't be a scary stage.

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Just feel confident speaking to the

stages of not only how you did things,

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like we're not going to ask which

button did you push, but think about

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the methodology and why you chose

to build something a certain way.

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So like, if you chose to do

a calculation in SQL versus

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in a data visualization tool.

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Why did you do that?

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And how did you go

about figuring that out?

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Those are going to be the sorts

of questions to talk to there.

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Avery: Okay.

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Awesome.

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So then you're, you're analysts, you're

kind of doing this like team interview.

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Now let me ask you this.

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I mean, they've done this probably a few

times, maybe in their careers, right?

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Do you give them questions?

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Do they come up with their own questions?

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Megan: They come up with

their own questions.

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I talk to them primarily about the goals.

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This is very similar to my management

style in general is I want to

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talk to them about the goals.

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What are we trying to find out?

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To bring it all back into the data world,

interviewing is a form of getting data.

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This is a means of data collection.

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So I talk to them about like,

what do we want to learn about

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the candidates at this stage?

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And I will help them with writing

questions if they need it.

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But for the most part, I'm

telling them like, I want to learn

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about their technical skills.

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I want to learn about how they

go about solving problems.

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I want to learn about how

comfortable they feel in this system.

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And you should be able to

come back and tell me about.

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After that, that's

actually our last stage.

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So we do that technical interview and

then I'm reviewing all of the feedback.

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So our system.

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As we collect scorecards after

every interview, and then I have

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access to review all of them.

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So I can see something that's been

scored relatively objectively across

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every interview and every candidate

and sort of evaluate how that adds up.

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So that'll be scoring on

things like technical skills.

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How are your SQL skills?

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How are your Tableau skills?

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But it'll also include things like

problem solving and other soft skills.

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How are you as a communicator?

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And I can evaluate against all of that.

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In this case, I had two candidates

that I was sort of debating

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between in the final stage.

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And then I made the call on

who to extend an offer to.

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Really the differentiator for

the candidate who got the offer

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is we're an education company.

309

: 00:14:02

We're here to help people upskill,

learn data analytics, was that she

310

: 00:14:06

had prior experience in education.

311

: 00:14:08

So all of her technical skills were great.

312

: 00:14:10

Her communication skills were great.

313

: 00:14:11

Her portfolio was great, but I had

multiple candidates who met all of that.

314

: 00:14:15

So her differentiator was really

that education experience that

315

: 00:14:19

was really helpful for us.

316

: 00:14:20

It was something that set

her apart and made her like

317

: 00:14:23

the perfect candidate for us.

318

: 00:14:25

Avery: And I want to emphasize that here.

319

: 00:14:26

I work with a lot of teachers who

want to get into data analytics

320

: 00:14:29

and a lot of them are fearful.

321

: 00:14:30

Hey, I don't have a technical background.

322

: 00:14:32

I come from an unusual background,

but in this case, that non technical

323

: 00:14:36

background, the unusual background

was actually kind of the superpower

324

: 00:14:39

that got her the job or him the job.

325

: 00:14:42

Megan: Yeah, like it's

super, super helpful.

326

: 00:14:44

I can combine that portfolio,

combine all the things that you've

327

: 00:14:47

learned about data analytics with

the other things that you know.

328

: 00:14:49

Somebody out there is making

ed tech software that needs

329

: 00:14:52

to be sold to teachers.

330

: 00:14:54

Like you understand teaching, you

understand the education world.

331

: 00:14:57

You can apply that knowledge to

data analytics in that setting.

332

: 00:15:00

Be the perfect candidate for that

company rather than a pretty good

333

: 00:15:04

candidate for a whole sea of companies.

334

: 00:15:06

And the same could be applied again for

like, if you've got retail experience

335

: 00:15:09

or customer service experience, you

might look at a customer service

336

: 00:15:13

analytics role, which there are plenty.

337

: 00:15:15

Take your prior experience in customer

service and apply that to analytics.

338

: 00:15:19

I did it myself.

339

: 00:15:20

Like that's how I got into

analytics was I studied healthcare

340

: 00:15:23

in my undergraduate program.

341

: 00:15:25

And I took an analytics role

at a healthcare company.

342

: 00:15:27

So when you can sort of combine

those things, it makes a much

343

: 00:15:30

more powerful profile, makes

you a much stronger candidate.

344

: 00:15:34

Again, like you don't have

to be okay for everybody.

345

: 00:15:36

Okay.

346

: 00:15:37

For everybody will get you a lot of like

looks, but you'll get the offer more when

347

: 00:15:41

you can find a way to make yourself like

just right for one company, those things.

348

: 00:15:46

Avery: Okay.

349

: 00:15:46

How awesome was that behind the scenes?

350

: 00:15:49

I'm going to it's like to actually

hire someone in the data space.

351

: 00:15:53

285 applicants just erased half of

them because they didn't know Tableau.

352

: 00:15:58

That's why it's so important to

list all data skills pretty much

353

: 00:16:00

on your resume and your LinkedIn.

354

: 00:16:03

12 spoke to a recruiter and

out of those 12, only 7 made

355

: 00:16:06

it to the next stage, which was

actually a SQL little coding test.

356

: 00:16:09

Everyone pretty much

passed the coding test.

357

: 00:16:11

One person got offered a

job and so dropped out.

358

: 00:16:13

So six people talked to the

hiring manager, which was Megan

359

: 00:16:15

in this case, kind of a behavioral

interview like you heard.

360

: 00:16:18

Out of those six, she kicked two

out after that and finally had

361

: 00:16:23

four interview with her team.

362

: 00:16:25

The team was part of the process,

which I think is really neat.

363

: 00:16:27

And it goes back to what Alex was

talking about earlier, how you

364

: 00:16:30

really do need to mesh with the team.

365

: 00:16:33

Well, out of those four, two of them

kind of stood out, but that couldn't

366

: 00:16:36

really choose between the two.

367

: 00:16:37

And the benefit of the doubt went to

the person whose domain experience,

368

: 00:16:41

like their past experience that wasn't

data related would help the team.

369

: 00:16:45

In this case, it was someone

with an education background, and

370

: 00:16:47

this was an education company.

371

: 00:16:48

And so that's who they ended

up hiring, which is awesome.

372

: 00:16:51

And I think for you, you should

really be thinking about.

373

: 00:16:53

You know, how can I use my

domain and my previous experience

374

: 00:16:56

to help me land my next job?

375

: 00:16:59

Hopefully your hiring manager is as

good and as kind as Megan, because I

376

: 00:17:03

think she hired very well in this case.

377

: 00:17:06

The next hiring manager we are

going to hear from is Jesse Morris.

378

: 00:17:09

And I want you to pay attention to

what he thinks is most important

379

: 00:17:12

in the hiring process, because once

again, I don't think you're going

380

: 00:17:15

to be able to guess what it is.

381

: 00:17:16

When you've hired some of those entry

level people, what stood out the most

382

: 00:17:19

to you in those hiring processes?

383

: 00:17:21

Jesse: Yeah, that's a great question.

384

: 00:17:22

And I think, you know, if you take

anything from today's conversation,

385

: 00:17:26

I think it's around this.

386

: 00:17:27

And, you know, again,

I think it gets lost.

387

: 00:17:30

You've got to be the most technical in the

room or, you know, your ability to build

388

: 00:17:33

a dashboard and make it a work of art.

389

: 00:17:35

You know, that's like the most important.

390

: 00:17:37

I actually don't think that's the case.

391

: 00:17:39

And I actually think, Avery, you and

I talked about this about, like, how

392

: 00:17:41

a lot of teachers make great analysts.

393

: 00:17:43

And I think there's a lot of truth

to that because ultimately when I

394

: 00:17:46

think when it boils down to it Really?

395

: 00:17:48

It's it's a couple of key things one

It's the ability to tell stories and be

396

: 00:17:52

succinct and that is not that's not just

a data skill set That's a life skill set

397

: 00:17:58

You know If you look actually my original

background is in sales like I actually

398

: 00:18:01

I should say my second job My first

job was I was a data analyst and then

399

: 00:18:05

I realized I needed to get presentation

skills and the ability to tell stories

400

: 00:18:08

So I went into sales for a few years.

401

: 00:18:10

And so I think, you know, that skillset,

the ability, but I think you can get

402

: 00:18:13

that in a myriad of ways, you can

be a great writer, you can, there's,

403

: 00:18:16

there's so many different ways you

can get that skillset, but I think

404

: 00:18:18

that's such a big one, especially.

405

: 00:18:20

You know, a lot of my time is spent

communicating to executives and

406

: 00:18:25

to leadership teams and to boards.

407

: 00:18:26

Like I spent a lot of time telling

stories to the board and that's really

408

: 00:18:30

key is that ability to kind of boil

things down and to here's the most

409

: 00:18:32

important and then you can work back.

410

: 00:18:34

Ultimately, like people, when they get

curious about data, that's when they

411

: 00:18:37

start asking kind of your next layer

of questions and you, you can make

412

: 00:18:40

that, you can bring that curiosity

of the life through storytelling.

413

: 00:18:44

The other one, which is

probably a little bit.

414

: 00:18:46

Less common you want here, but this is

something that just continues to even

415

: 00:18:50

today even with senior analysts It

doesn't matter what level of analysts

416

: 00:18:54

you are but tenacity and mental

toughness Wow, so that's a really funny

417

: 00:18:58

one I tenacity to me like in my world.

418

: 00:19:02

I work in these smaller called

startup type Uh, technology companies.

419

: 00:19:06

And so we're moving at really

fast pace, but we don't get

420

: 00:19:10

weeks to work on projects.

421

: 00:19:11

So if you work in any large corporate

companies, you're going to get that.

422

: 00:19:15

And that's okay.

423

: 00:19:15

I think ultimately it's good

to know, like, what are, what

424

: 00:19:18

type of environment you're in?

425

: 00:19:19

And so if you don't work necessarily

well under pressure and some of these

426

: 00:19:23

things I'm about to talk about, that's

okay, then you're probably maybe better

427

: 00:19:26

designed to work at larger companies

where you're given the freedom to like,

428

: 00:19:30

sit down and work on things for weeks.

429

: 00:19:32

The environment I work in,

we're not given that time.

430

: 00:19:35

And so the ability to, you know,

change prioritization on a dime, to

431

: 00:19:40

juggle nine different projects at once.

432

: 00:19:42

If you talk to my data analyst

today, like this is the reality.

433

: 00:19:44

Like we, this week, we came into the

week with a plan and by Monday afternoon,

434

: 00:19:48

you know, it was Monday morning during

our standup and by Monday afternoon,

435

: 00:19:51

that plan got halfway derailed, right?

436

: 00:19:53

And so it's a reprioritization

game and that's not for everybody.

437

: 00:19:56

I mean, I think ultimately.

438

: 00:19:58

You know, that's a tough thing to wrap

your head around and not get frustrated.

439

: 00:20:02

And, and I think you and I talked about

this before, but it's that like knowledge

440

: 00:20:06

of, I understand what perfect looks like

or really phenomenal looks like, but

441

: 00:20:10

I also understand what good enough is.

442

: 00:20:12

And I think that skill set,

that's a really important one.

443

: 00:20:16

And that's not like, you know, I'm going

to learn this by watching X, Y, and Z.

444

: 00:20:19

I think that's something that you

actually have to work towards and

445

: 00:20:21

build up that, that mental toughness.

446

: 00:20:23

I actually think failure, you know,

it's easy to look at a resume and be

447

: 00:20:26

like, Oh, all this stuff went great.

448

: 00:20:27

I was a founder, you

know, at a tech company.

449

: 00:20:30

Good for me.

450

: 00:20:30

I also failed at that tech company.

451

: 00:20:32

Right.

452

: 00:20:32

I learned a lot of things through

trial and error and that I think

453

: 00:20:35

it's the same for all of us.

454

: 00:20:36

And so those would be some of the

things I think that really stand out

455

: 00:20:39

to me when you boil it down to like

some of the key pieces behind it.

456

: 00:20:43

It's an attitude, right?

457

: 00:20:44

Like it's that willingness to say,

Hey, I messed up here and that's okay.

458

: 00:20:48

Like, cool.

459

: 00:20:49

What'd you learn from it?

460

: 00:20:50

How do we make it better?

461

: 00:20:50

How can I help?

462

: 00:20:52

But I think, you know, those

are ultimately some of the, when

463

: 00:20:54

you boil it down to some of the

things that I look for, no matter

464

: 00:20:57

what stage you're at within it.

465

: 00:20:58

And then I think.

466

: 00:20:59

You know, on the specifically on the

starting out analyst in particular, you

467

: 00:21:03

know, I think just a, again, perspective

is an interesting one, but did you have

468

: 00:21:07

a sales background or did you work for,

I mean, maybe you're working in retail.

469

: 00:21:11

Did you work for Banana Republic

during college where you were

470

: 00:21:14

like, all of those things,

perspective and data is everything.

471

: 00:21:18

And what I mean by that is like your

ability to speak into it from the

472

: 00:21:21

person who's asking the question

or the departments or the leaders

473

: 00:21:24

that are asking the questions.

474

: 00:21:25

Right.

475

: 00:21:26

Because as long as you've got just

that various perspective, that

476

: 00:21:29

actually has a lot more value.

477

: 00:21:30

I think sometimes the technical even does.

478

: 00:21:32

Avery: Yeah.

479

: 00:21:32

And I hope people just heard what

you said, because I think that's

480

: 00:21:35

very impactful, you know, just

to kind of rehash them a bit.

481

: 00:21:39

It's not necessarily how technical

you are that lands you the job.

482

: 00:21:42

Because I think you said this

phrase when we first originally

483

: 00:21:45

talked that the technical stuff.

484

: 00:21:47

It's kind of expected.

485

: 00:21:48

That's like you, you have

it or you kind of don't.

486

: 00:21:50

Right.

487

: 00:21:50

And it's really your storytelling,

your grit, your attitude that separates

488

: 00:21:55

you, which I think for all of you

guys listening who want to be aspiring

489

: 00:21:59

data analysts, that should be really

rewarding because you can have grit.

490

: 00:22:03

You can be, you know,

you can be authentic.

491

: 00:22:05

You can try hard.

492

: 00:22:06

You can have passion.

493

: 00:22:07

You can become a good,

you know, storyteller.

494

: 00:22:10

Those aren't like necessary, like you

have to be spending 25 years of your life

495

: 00:22:14

in SQL to know how to master everything.

496

: 00:22:16

Right.

497

: 00:22:16

That's really, in my opinion, enlightening

and refreshing to hear because it can be

498

: 00:22:21

like, I think most people take the data

career job hunt way too skill heavy.

499

: 00:22:26

Of course, skills are important.

500

: 00:22:28

Right.

501

: 00:22:28

But like, they're not everything.

502

: 00:22:29

And I think you kind of just said that

basically, they're not everything.

503

: 00:22:32

All right.

504

: 00:22:32

Hopefully you're catching

the drift at this point.

505

: 00:22:34

It was a pretty similar theme

here that your technical skills,

506

: 00:22:37

of course, they're important.

507

: 00:22:39

It's the bare minimum.

508

: 00:22:40

It's actually things like your

storytelling, the ability to be

509

: 00:22:43

succinct that sets you apart.

510

: 00:22:45

And that's something you're

probably thinking, Avery,

511

: 00:22:47

you're not very good at that.

512

: 00:22:48

I've listened to your podcast episodes.

513

: 00:22:50

I've watched your YouTube videos

and you're not very succinct.

514

: 00:22:52

And it's true.

515

: 00:22:53

It's something I'm still working on.

516

: 00:22:55

And I think it's a lifelong journey, but

once again, this is like Jesse said, a

517

: 00:22:59

life skill, not necessarily a data skill.

518

: 00:23:01

And so that's something that we can

practice and we can get better at.

519

: 00:23:03

And no matter how technical

or how non technical you are.

520

: 00:23:06

It's something that you can

improve on every day and work at.

521

: 00:23:10

Jesse also brought up tenacity

and mental toughness, and this

522

: 00:23:13

is something that we can all do.

523

: 00:23:14

One thing that I do is I actually

take ice cold showers and baths to

524

: 00:23:18

try to increase my mental toughness.

525

: 00:23:20

Kind of weird, but it works for me.

526

: 00:23:21

The last hiring manager we are

going to hear from is Andrew Madsen.

527

: 00:23:25

I want you to pay attention to what

he says because he kind of repeats

528

: 00:23:28

what has already been said, but he

adds one really important point.

529

: 00:23:31

Part, uh, that the other ones

haven't talked about as much,

530

: 00:23:34

and that is projects, which is

the P part of the SBN method.

531

: 00:23:37

Let's take a listen.

532

: 00:23:38

I wanted you to walk us through the

idea of when you're hiring a data

533

: 00:23:42

analyst, you know, what's really

important to stand out as a candidate.

534

: 00:23:46

What can these listeners do to To stand

out and the data analyst job search.

535

: 00:23:51

Andrew: Yeah.

536

: 00:23:51

My thoughts on this

have evolved over time.

537

: 00:23:53

So the data analyst position has

just grown and grown and grown as

538

: 00:23:57

our needs for quality data analysts

have really permeated every industry.

539

: 00:24:02

So there's a lot of opportunity

there before when I was new at

540

: 00:24:06

hiring data analysts and I was

new into data myself, I really was

541

: 00:24:09

focused on the technical skills.

542

: 00:24:11

I was looking at whatever my stack was,

like we use Tableau, whatever it is.

543

: 00:24:15

And I was looking for applicants

who match that data stack.

544

: 00:24:19

That's how I began looking for applicants.

545

: 00:24:21

Now what I look for if I was hiring a data

analyst, I focus much more on the person.

546

: 00:24:28

I look for somebody who's curious.

547

: 00:24:30

I look for somebody who's resilient.

548

: 00:24:33

I look for somebody who's going

to mesh well with the team because

549

: 00:24:35

data analytics is a team sport.

550

: 00:24:37

You know, one person who just isn't

a team player can really throw off

551

: 00:24:42

the whole dynamic and ultimately

the work and the business insights

552

: 00:24:45

that we're trying to drive.

553

: 00:24:46

So less important to me is your

specific technical skill set.

554

: 00:24:51

You know, if you know Tableau

really well and we're using Power

555

: 00:24:54

BI, that's totally fine with me.

556

: 00:24:56

But you're demonstrating

that ability to learn.

557

: 00:25:00

And some of the ways that you

can do that, like Avery always

558

: 00:25:02

talks about, are projects.

559

: 00:25:04

I love to look at projects.

560

: 00:25:06

I love to see interesting projects.

561

: 00:25:08

We've all seen the Titanic dataset,

and I don't mind if you use that, but

562

: 00:25:11

I want to see something that you're

interested and passionate about, and

563

: 00:25:14

I want to learn about that with you.

564

: 00:25:16

And then if we're interviewing, I

really want you to tell that story,

565

: 00:25:20

because the ability to communicate

as a data analyst is so important.

566

: 00:25:23

You know, I don't want to have

to go to the stakeholders and

567

: 00:25:25

explain what you were doing.

568

: 00:25:26

I want you to go and represent

yourself and present your insights

569

: 00:25:29

and build those relationships.

570

: 00:25:31

So if you can have something you're

passionate about, you uncovered some

571

: 00:25:34

insights and you can communicate those in

a story and a narrative that's engaging.

572

: 00:25:39

Those are so important.

573

: 00:25:40

Those will really set you apart.

574

: 00:25:42

Avery: Okay.

575

: 00:25:43

That's awesome.

576

: 00:25:43

A lot to unpack there.

577

: 00:25:45

I think we, as data analysts,

candidates often over index.

578

: 00:25:50

On how much, you know, the

technical skills matter and

579

: 00:25:52

the technical skills do matter.

580

: 00:25:55

There's people who are willing

to take a chance on you and you

581

: 00:25:57

have to show them that you're more

than just, you know, some NPC.

582

: 00:26:01

I don't even know what that

even, what that means right now.

583

: 00:26:04

What does that mean?

584

: 00:26:05

A non role playing, I don't even know

what it means, but it's like a non player

585

: 00:26:08

Andrew: character.

586

: 00:26:09

Yeah.

587

: 00:26:09

Avery: Non player character.

588

: 00:26:10

You have to show some sort of passion,

some sort of personality, some sort of

589

: 00:26:14

drive, some sort of like, And that can

be even your grit, your communication,

590

: 00:26:19

you know, what you like about projects.

591

: 00:26:20

And it's just interesting that we've

had a couple of different data hiring

592

: 00:26:24

managers on the podcast now, and they've

all let off with a very similar message.

593

: 00:26:27

There you have it, folks, advice

straight from hiring managers on

594

: 00:26:30

how to land your next day at a job.

595

: 00:26:32

If you want even more On how to

land a data job, I highly suggest

596

: 00:26:36

checking out my newsletter.

597

: 00:26:38

Every week I send you a tip that helps you

take the next step in your data career.

598

: 00:26:42

You can subscribe at datacareerjumpstart.

599

: 00:26:44

com or in the show notes down below.

600

: 00:26:45

And if you want even more help on

your data journey, consider joining

601

: 00:26:49

the data analytics accelerator,

which is my ten week bootcamp to

602

: 00:26:52

help you land your first data job.

603

: 00:26:54

You can find that link in

the show notes down below.