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It’s not just about skills; find out what makes hiring managers say, “You’re the one we’ve been looking for.” Featuring hiring managers like Alex The Analyst, Megan McGuire, Jesse Morris, and Andrew Madson, the episode provides actionable tips and behind-the-scenes looks at what it takes to stand out in the data job market.

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Prepare for Interviews: Try the Interview Simulator

⌚ TIMESTAMPS

00:25 Alex The Analyst: The Importance of Personality in Hiring

08:01 Megan McGuire: The Hiring Process from Start to Finish

17:06 Jesse Morris: Storytelling and Tenacity in Data Roles

23:21 Andrew Madson: The Value of Projects and Team Fit

26:27 Conclusion and Additional Resources

🔗 CONNECT WITH GUESTS

Alex Freberg: https://www.linkedin.com/in/alex-freberg

Megan McGuire: https://www.linkedin.com/in/megan-s-mcguire

Jesse Morris: https://www.linkedin.com/in/jessemorris1

Andrew Madson: https://www.linkedin.com/in/andrew-madson

🔗 CONNECT WITH AVERY

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🤝 LinkedIn

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🎵 TikTok

💻 Website

Mentioned in this episode:

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Avery: 00:00:00

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

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: 00:00:03

had the opportunity to interview

a lot of cool people, including

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: 00:00:07

a lot of data hiring managers who

you'll hear from in this episode.

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: 00:00:11

And they've given really great advice

on how to get hired in the data space.

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: 00:00:15

In this episode, you'll hear the best

snippets from those hiring managers

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: 00:00:19

and get actionable advice on how

actually to land a data job straight

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: 00:00:23

from the hiring manager's mouth.

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: 00:00:24

Let's get into it.

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: 00:00:25

Our first hiring manager you

may have seen before on YouTube.

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: 00:00:29

It's Alex, the analyst or Alex Freyberg,

and he has really great hiring experience

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: 00:00:34

from when he was at his corporate job.

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: 00:00:36

And in this example here, I want you

to pay attention to what he thought

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: 00:00:39

mattered most when getting hired.

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: 00:00:41

It's probably going to surprise you

when you were hiring people, like what

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: 00:00:44

was important in a candidate for you?

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: 00:00:46

Like what was the first few

things you were looking at?

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: 00:00:48

Alex: Yeah.

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: 00:00:49

I'm going to be like brutally honest

because I think people tend to sugarcoat

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: 00:00:53

this process and the hiring process is.

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: 00:00:55

A lot of people on like LinkedIn

or YouTube will tell you like

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: 00:00:59

the sugar coated version.

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: 00:01:00

I'm going to tell you

what I truly looked for.

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: 00:01:03

I was on a hiring team

when I was a data analyst.

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: 00:01:05

I was the one who gave

the technical interviews.

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: 00:01:07

And so that was my part

of the hiring team.

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: 00:01:08

And then you're right.

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: 00:01:09

I became a hiring manager.

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: 00:01:11

And so then as the hiring manager, I did

the whole process and usually brought

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: 00:01:15

in like my boss as well for some like

the final interviews during the hiring.

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: 00:01:19

The, when I was on the hiring

team during that process, we

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: 00:01:22

were mostly hiring data analysts.

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: 00:01:24

I eventually started when I was a

hiring manager was doing developers,

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: 00:01:27

database engineers, or data engineers,

and then data analysts as well.

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: 00:01:31

So that was a little bit different,

but on the hiring team, just for

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: 00:01:33

data analysis, we always looked for

someone who had a good personality

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: 00:01:39

and most people will tell you.

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: 00:01:42

I've seen it online.

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: 00:01:43

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

as you have the right skills and you get

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: 00:01:46

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

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

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: 00:01:50

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

you really do look for someone who's

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: 00:01:54

going to fit well with your team.

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: 00:01:56

And so, yeah.

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: 00:01:57

So, I always kind of gravitated

towards people who are more outgoing,

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: 00:02:01

and is that 100 percent fair?

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: 00:02:02

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

the hiring process isn't super fair.

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: 00:02:06

And so, the people who are more

outgoing, I tended to gravitate

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: 00:02:08

to, and so did my whole team.

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: 00:02:10

Our whole team was very outgoing, very

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

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: 00:02:14

come in and have a very different flow

to them, or, or personality to them.

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: 00:02:19

And so that's just like a brutal

truth, you know, people always

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: 00:02:22

say diversity is like crazy good,

but for personality, I think.

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: 00:02:25

The, the, that piece of it is actually

the flow of the team and how that, uh,

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: 00:02:31

people gel together is really important.

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: 00:02:33

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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: 00:02:40

And so oftentimes we'd have people

come in and SQL is really important.

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: 00:02:44

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

the most important skill because we used

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: 00:02:47

it like really in depth for a lot of our

processes and so people would come in and

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: 00:02:54

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

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: 00:02:56

You know, data cleaning or

tell me how you use SQL.

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: 00:02:59

And if people can articulate really

well, like here's how I use it.

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: 00:03:03

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

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

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: 00:03:08

I use SQL, but I don't

really use it that much.

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: 00:03:10

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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: 00:03:23

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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: 00:03:26

Here's how I do that in SQL.

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: 00:03:28

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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: 00:03:45

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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: 00:04:06

This should be a no brainer.

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: 00:04:07

Like, like, This is like super simple,

like, like just aggregating something with

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: 00:04:12

a group by nothing crazy or just a simple

joint, just combine these two tables

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: 00:04:16

and people would have trouble with it.

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: 00:04:17

And that was an immediate red flag.

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: 00:04:19

Like we couldn't hire them.

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: 00:04:19

And so those three things I

would say are the biggest things

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: 00:04:22

that we look for and like really

ranked on during those interviews.

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: 00:04:27

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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: 00:04:35

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

personality is very objective and so

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: 00:04:40

it's hard to describe, but just somebody

who's more outgoing, very friendly.

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: 00:04:44

That is like kind of

what we were looking for.

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: 00:04:46

The being able to articulate and the

technical interviews is the other 50%.

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: 00:04:51

So those two things were still

very important, but if they looked

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: 00:04:56

like they were very teachable.

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: 00:04:58

If they looked like they were like

really driven and we were like, you

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: 00:05:02

know, they may not be where we want

them today, but I was like, that

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: 00:05:04

person will be good in like a month.

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: 00:05:07

We would still hire them.

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: 00:05:08

And we did that for one of our

business analysts who we hired, um,

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: 00:05:10

who were, he kind of knew SQL, but

his job wasn't as intensive as Eagle

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: 00:05:15

for his, that, for that position.

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: 00:05:16

So we were like, Hey, let's,

Let's hire him cause he would

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: 00:05:18

fit really well with our team.

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: 00:05:20

And we trained him like I, he was my

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

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: 00:05:23

So I trained him in SQL and he,

within like a month he was up and

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: 00:05:26

running and I didn't really have

to help him that much anymore.

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: 00:05:29

So again, it was like that trainability

piece, the, um, the attitude,

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: 00:05:34

the, uh, how driven they were did

play a big role in who we hired.

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: 00:05:39

So I know I was long winded

on that, but they, you know,

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: 00:05:40

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

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: 00:05:42

Process to our to talk about, you know,

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: 00:05:44

Avery: it is I think you did.

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: 00:05:45

I think you did really well And I don't

I mean although it was you know, you did

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: 00:05:49

talk about maybe the extrovert versus the

introvert I don't think it was too brutal

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: 00:05:51

I think I think it's like an opportunity

you have a good personality and you

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: 00:05:55

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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: 00:05:58

Well, I talked to a lot of people who

give me that feedback They're like,

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: 00:06:00

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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: 00:06:05

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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: 00:06:12

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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: 00:06:32

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

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: 00:06:34

And, and so what I would do is

I'd practice in a mirror and then

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: 00:06:37

my, I'd practice with my wife.

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: 00:06:39

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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: 00:06:48

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

point even to myself as like a

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: 00:06:51

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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: 00:07:05

Personality really matters.

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: 00:07:07

And I think that's

actually a positive thing.

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: 00:07:10

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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: 00:07:17

Now you might be thinking,

well, that's great.

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: 00:07:19

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

thing because it means that there's

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: 00:07:22

room for all of us in the data world.

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: 00:07:24

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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: 00:07:35

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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: 00:07:41

I love what Alex said about the mirror.

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: 00:07:43

I actually built a software.

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: 00:07:45

It's called Interview Simulator

that lets you practice.

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: 00:07:48

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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: 00:08:00

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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: 00:08:06

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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: 00:08:13

people The recruiter talked to how

many people she talked to as the hiring

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: 00:08:16

manager and kind of what the next

steps and how they ultimately hired

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: 00:08:20

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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: 00:08:25

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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: 00:08:32

applications start to come in.

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: 00:08:33

So can you walk us through how

many applications, how long you,

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: 00:08:37

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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: 00:08:44

We didn't give it long, because we

got 285 applications within a week.

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: 00:08:48

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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: 00:09:11

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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: 00:09:19

You don't have the portfolio.

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: 00:09:21

You don't have the projects.

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: 00:09:22

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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: 00:09:26

So I have to parse out on a

lot more stringent criteria.

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: 00:09:30

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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: 00:09:39

is what we use as our visualization

tool, I'm going to talk to the half

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: 00:09:42

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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: 00:09:49

lot of people coming through.

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: 00:09:51

So out of that 285, I think we

had 12 talk to our recruiter.

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: 00:09:56

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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: 00:10:00

Like I'm not going to ask you to

do a technical screen before we've

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: 00:10:02

put some time forward to you.

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: 00:10:04

We need to have that

sort of give and take.

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: 00:10:06

So you talk to our

recruiter at that stage.

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: 00:10:08

After that, we had.

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: 00:10:10

Five candidates exit because they

either didn't respond, or location,

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: 00:10:14

or salary requirements didn't line up.

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: 00:10:17

So then we had seven candidates

take our code assessment.

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: 00:10:20

We do a SQL test on CodeSignal

to review candidates skills.

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: 00:10:24

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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: 00:10:29

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

a quick Tableau assessment, but SQL's

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: 00:10:33

such a core skill, and it's really easy

to test with a lot of SQL questions.

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: 00:10:36

We're doing some grouping.

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: 00:10:37

I think.

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: 00:10:38

There might be a window

function question on there.

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: 00:10:41

So at that stage, actually everybody

passed our SQL interview, but we

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: 00:10:44

did have one candidate accept a

different offer at that stage and exit.

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Yeah.

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: 00:10:50

Our average completion time

on that stage was 24 minutes.

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: 00:10:52

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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: 00:10:57

You're applying for lots of jobs,

especially at the entry level.

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: 00:11:00

I'm not trying to keep

you for many, many hours.

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: 00:11:03

The stage actually that we moved

to was my hiring manager interview.

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: 00:11:07

And in that stage, I'm asking usually

some more problem solving questions.

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: 00:11:11

So I'm going to ask you about something

in your portfolio, something that you've

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: 00:11:14

gone deep on, and ask you things like,

how would you expand that project?

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: 00:11:18

What else are you curious about

this project that you might've

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: 00:11:20

worked on in your portfolio?

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: 00:11:22

If you were rebuilding it, like, what

would you do differently this time?

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: 00:11:26

What other data would be

helpful for driving decisions?

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: 00:11:29

Those sorts of questions to dive deep.

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: 00:11:30

Again, like, I'm not asking you about

all of your experience in data analytics.

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: 00:11:34

I assume that you don't have that

applying for an entry level role.

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: 00:11:38

I talked to six in the higher edger

state and four of them went through.

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: 00:11:42

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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: 00:11:48

So then we moved into the team

technical interview where I have two

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: 00:11:51

of the senior analysts on my team go

through much more technical questions.

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: 00:11:55

So in that stage, you're going to

see like, let's walk through your

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: 00:11:58

portfolio project and talk about like.

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: 00:12:00

How you build this in Tableau, you put

something on Tableau public, we're going

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: 00:12:04

to talk through the stages of building it.

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: 00:12:06

So they're going to be vetting your

technical skills with a lot more detail.

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: 00:12:09

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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: 00:12:18

the methodology and why you chose

to build something a certain way.

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: 00:12:21

So like, if you chose to do

a calculation in SQL versus

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: 00:12:25

in a data visualization tool.

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: 00:12:27

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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: 00:12:34

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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: 00:12:43

Do you give them questions?

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: 00:12:44

Do they come up with their own questions?

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: 00:12:47

Megan: They come up with

their own questions.

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: 00:12:48

I talk to them primarily about the goals.

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: 00:12:50

This is very similar to my management

style in general is I want to

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: 00:12:54

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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: 00:13:05

And I will help them with writing

questions if they need it.

286

: 00:13:08

But for the most part, I'm

telling them like, I want to learn

287

: 00:13:10

about their technical skills.

288

: 00:13:11

I want to learn about how they

go about solving problems.

289

: 00:13:14

I want to learn about how

comfortable they feel in this system.

290

: 00:13:16

And you should be able to

come back and tell me about.

291

: 00:13:18

After that, that's

actually our last stage.

292

: 00:13:21

So we do that technical interview and

then I'm reviewing all of the feedback.

293

: 00:13:24

So our system.

294

: 00:13:26

As we collect scorecards after

every interview, and then I have

295

: 00:13:29

access to review all of them.

296

: 00:13:31

So I can see something that's been

scored relatively objectively across

297

: 00:13:35

every interview and every candidate

and sort of evaluate how that adds up.

298

: 00:13:39

So that'll be scoring on

things like technical skills.

299

: 00:13:41

How are your SQL skills?

300

: 00:13:42

How are your Tableau skills?

301

: 00:13:44

But it'll also include things like

problem solving and other soft skills.

302

: 00:13:47

How are you as a communicator?

303

: 00:13:48

And I can evaluate against all of that.

304

: 00:13:50

In this case, I had two candidates

that I was sort of debating

305

: 00:13:53

between in the final stage.

306

: 00:13:55

And then I made the call on

who to extend an offer to.

307

: 00:13:58

Really the differentiator for

the candidate who got the offer

308

: 00:14:01

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

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the data analytics accelerator,

which is my ten week bootcamp to

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help you land your first data job.

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You can find that link in

the show notes down below.