221: This Architect Became a Data Analyst AFTER 13 Years

Avery: 00:00:00

I guess, did anyone think you were kind of crazy for

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leaving this architecture career?

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'Cause you had worked in architecture

for what, like 12, 13 years-ish?

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David Kovacs: Data analysts are

just gonna be replaced with AI,

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and the market is saturated.

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Then I was DoorDashing between my

old job and finding my new job,

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hitting the program hard, studying.

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I'm living proof of that.

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Avery: What does it actually look like

to go from, you know, architecture

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and landscape design to data analyst?

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All right, David, take me back before

you were a data analyst into this

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architecture and landscape design life.

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What did that career look like?

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What were you actually doing?

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David Kovacs: Uh, yeah, so I

went to, uh, college for, it's

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called architectural technology.

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Uh, really just a fancy way of

saying architectural drafting.

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So my day-to-day looked like

finishing red lines for project

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managers and filling requests.

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Not quite the creativity that I

was looking forward to when I went

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to school for it, but that's kinda

what my day-to-day looked like.

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Avery: Okay.

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David Kovacs: Th-

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Avery: I- maybe I'm sensing this

wrong, but that sounds like you maybe

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got kind of bored just doing that- Mm

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and it wasn't, like, super

fulfilling necessarily.

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David Kovacs: Eventually, yeah.

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Yeah, boredom kicked in, for sure.

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Avery: Yeah, and that makes sense.

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I mean, that's how I was as

a chemical lab technician.

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Um, you did have the chance to work

on $140 million, uh, baseball stadium.

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What were you designing for them,

and I guess, where was that at?

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David Kovacs: Yeah, again, just filling

the orders of the project managers

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and it's actually about 25 miles

down the road from where I live, so

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I took my, uh, my wife and my in-laws

to go see it this past weekend.

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That is one cool thing about being in

architecture for so long and actually h-

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seeing projects come to life and getting

to, like, go visit them and stuff.

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Avery: You can be like, "I

drew lines on that," and it

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eventually- … came to, to life.

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Um, I guess at one point did you, like,

realize, "Okay, I no longer want to

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be, you know, an architect anymore"?

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David Kovacs: Ooh.

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Really my whole career was a struggle

of, like, just wondering if I'd made

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the right decision, even in college.

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You know, I thought about changing in the

middle of it and I just kind of, I guess

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you could say I white-knuckled my way

through college 'cause I just wanted to

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get done with it and start some kind of

career and- There was definitely a lot of

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struggles over the years, job losses, so

it's been something I played around with

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a lot over the years, and just recently,

last year, kind of personal situation

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with my wife being diagnosed with stage

two breast cancer kind of kicked it

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into gear again that I just wanted to do

better for myself, better for my family.

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Avery: It's really impressive, um,

and really a- admirable of you.

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Yeah, uh, amazing.

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I was gonna ask, like, what, at what

point was, you know, the tipping

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point for you, but obviously that,

that was the tipping point for you.

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Um, I guess did anyone think

you were kind of crazy for

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leaving this architecture career?

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'Cause you had worked in architecture

for what, like 12, 13 years-ish.

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David Kovacs: Yeah, I got, uh, definitely,

uh, some comments about, oh, data analysts

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are just gonna be replaced with AI, and

the market is saturated, and it's not

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a good idea for you to do that, but I

think the data out there says otherwise,

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that analysts are still getting hired.

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I'm living proof of that.

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Avery: I would agree.

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Um, okay, so you didn't listen to

them, and you're like, "I'm still

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gonna pursue this data analytics."

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Um, why in data analytics in particular?

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Like, what drew you to data

as opposed to architecture?

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David Kovacs: Right, so I did a lot

of kind of exploring and searching

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when I decided it was finally time

to leave my career in drafting.

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Um, I found your YouTube channel, you

know, listened to how passionately you

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speak about your own career and listened

to other data analysts speak about how

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passionate they are about that business.

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Avery: That's, that's super cool to

hear that it was, it was via YouTube.

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So were you doing, like, a lot

of research via, via YouTube?

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Like, looking up different careers like

cybersecurity, I don't know, project

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management, those types of things?

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David Kovacs: Yeah, I kind of, like,

did the search of, you know, careers

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that, careers that you can get into

where you either don't need a degree

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or your degree is relevant to the

job that you're going to be doing.

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Um, web development is another path I went

down for a little bit as for deciding.

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It wasn't for me.

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I even took a career assessment,

and, like, three or four of my

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top 10 results were some form of

data analyst, so it was pretty-

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Avery: So you're like-

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… David Kovacs: obvious that

I should pursue that more

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Avery: Oh, perfect.

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That's, that's great.

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Um, okay, so you, you kind

of do some research online.

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You learn about data analytics.

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Um, you watch some YouTube videos.

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Um, obviously architecture, you know,

the tools you're using in architecture

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are a little bit different than the

tools you'd be using as a data analyst.

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What was, like, the first thing,

like, you, you did to, like, learn

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data analytics, and how did that go?

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David Kovacs: Yeah, again, just

watching a lot of videos and tutorials.

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Um, I did do the Google certificate

in my free time while we were-

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Avery: How, how was that experience?

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David Kovacs: It was good.

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I feel like it definitely gave

me a, like a peek into the world

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of what a data analyst does.

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I don't feel like it got

me quite all the way there.

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It was just really- I would say

it's that first- It was just really

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scratching the surface, you know.

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Avery: 100%.

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It's, it's kind of a, a very, uh,

not, not deep, uh, very shallow intro-

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introduction to analytics in general.

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Um, okay, so then, then I'm curious here

because, uh, you end up … You know,

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you're doing the Google Data Analytics

certificate, and then you join my

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program, the Data Analytics Accelerator.

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And obviously I don't have, like, a magic

eight ball to, to what's going on in your

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life and, you know, what's going on with,

with your brain and what's going on with

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what you're thinking, but we all leave

traces of ourselves on the internet.

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So my marketing data tells me that you

first visited my website coming from

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Instagram, which is, I think is really

interesting because I don't really do much

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with Instagram currently at the moment.

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And then 100 days later, you revisited the

website after coming from a YouTube video,

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and you end up, you ended up joining.

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So I'm just curious, kind of like

what happened, if you can remember in

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those 100 days, what made you end up

deciding, you know, "Okay, the Google

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Data Analytics certificate, it was a

great start, but it's not enough for me.

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I want some more"?

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David Kovacs: Oh, yeah.

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I, I had been watching you for a while

and followed you on all platforms and,

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and yeah, I think I, um, I just wanted

to learn with a community and, you

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know, get help and job hunting and-

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Avery: Um, now when you, when you

were in the boot camp, you actually

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ended up doing an internship, and

I think that was just this, this

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last quarter, like our, our January

internship with the UK housing data.

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I'm curious kind of what that

experience was, was like for you.

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Uh, I guess tell about, like, what…

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how big was your group and what

kind of tools you guys used and

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how that whole process was for you.

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David Kovacs: Yeah, so the…

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I think the day after I signed up

for the Accelerator, I had learned

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that you guys offer an internship,

and I kind of wrote Isaac.

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Shout out to Isaac.

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And asked, "Hey, am I too

late to join this internship?

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'Cause I'd really like to do that."

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And he said, "Not at all.

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I'll put you in group seven."

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And, you know, we did our first meeting.

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I kind of became the, uh, business

intelligence portion of the project, where

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I did, you know, charts and dashboards.

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It was a really good learning experience.

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W- was definitely very hard, 'cause you

have four or five brand-new data analysts

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who are all learning with you, and we're

all in different parts of the world.

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I think we had somebody in every time zone

in America, and then somebody in the UK.

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So we were getting up at, like,

6:00 and 7:00 in the morning to,

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to do virtual meetings and talk

about where we go from here.

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But it was definitely worth it, and

I think it did make a difference

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in getting the job that I have now.

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Avery: Very cool.

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Were, were you guys

using a lot of Tableau?

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I, I think, like, I remember group

seven using a decent amount of Tableau.

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David Kovacs: Yep.

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That was us.

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That was me.

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Avery: Awesome.

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Okay, perfect.

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That was you.

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Great.

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Yeah, I do remember group seven

doing, um, doing some good work.

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Um, yeah, and talk a little bit

more about, like, how it, how you

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feel like the internship might

have helped you, uh, land the job.

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Um, one thing I noticed is,

you know, I haven't seen…

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I don't think I've seen your, your resume

maybe, maybe in a while or a l- ever.

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Um, but I know on your LinkedIn you

have that you were a data analyst

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intern for my company, Snowdata Science.

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So I'm assuming that was on your

LinkedIn when you were applying for jobs.

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David Kovacs: Yeah.

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Avery: Okay.

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And did it pop up in the interview

at all, or did they ever ask

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you, like, what you did at that,

you know, at Snowdata Science?

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David Kovacs: Hmm.

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Yeah, it did pop up in the interview.

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Yeah, and they wanted to

hear from you as a reference.

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Avery: That's, that's what

I was actually gonna ask.

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Um, the company that you ended up

landing this role with, is that

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the company that I, I talked to via

email about the reference stuff?

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David Kovacs: Yeah, it is.

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Avery: Okay, awesome.

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Okay, so it did play a, a decent part in

the interview process- Yeah … because,

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you know, they wanted to hear a little

bit more what you did as, as an intern.

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Um, okay, that's awesome.

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I'm glad to hear, uh, plus

one for the internship.

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Uh, our next- we're trying

to do four of those a year.

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So, um, we're only at one, so we gotta

start the next one pretty soon in 2026.

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Um, I'm curious, like, you know,

you were still working while

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you were doing the boot camp.

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Is that correct?

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David Kovacs: For a few weeks I was,

and then a situation arose at my job

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where I had to leave, so I actually,

uh, finished up my two weeks at my job

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and then I was just DoorDashing between

my old job and finding my new job.

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And- I wanna talk more about that … over

the evenings, yeah, and then over the w-

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evenings and weekends I was, you know,

hitting the program hard, studying.

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Avery: That's absolutely

amazing and really admirable.

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I think I'd be very, I think I'd be

very stressed out in that situation.

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Mm-hmm.

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But I think a lot of people are in a s-

in a similar situation where, you know,

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maybe they're doing gig work right now

and they're trying to land a data job.

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Um, can you just tell me more about,

like, what your day-to-day looked like?

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Like what time, like, w-

what time were you studying?

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Was it in the evenings or in the mornings?

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What did the w- day in the life

look like of someone who know, who

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was, you know, doing these gig works

while trying to land a data job?

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David Kovacs: Yeah, you just find

moments during the day to study

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whenever you have free time.

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So I was out there, like, during peak

hours, you know, making deliveries.

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Mm.

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I'd, I'd come home and keep going through

the program, keep building projects.

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Avery: That's super interesting to hear.

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I'm gonna indulge myself in

a, in a selfish question here.

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David Kovacs: Yeah.

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Avery: Um, because you know,

one of the things I try to do in

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the Accelerator is I'm like, you

know, people are busy, you know?

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And not everyone has time to go through,

uh, a 12-week full-time boot camp.

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Still try to make it fast, like

you can do it in, like, 12 weeks.

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I think you were about, from, from

when you joined to when you landed your

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job, I think you were about 90 days.

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Um, ish.

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Um, so obviously it's possible to do still

part-time, but I also try to, like, cater

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to people who have busy lives by making

the material as available as possible in

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as many different avenues as possible.

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So, you know, a lot of our lessons will

have a video lesson, um, but there's

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also the text version with pictures

down below, and we try to do, like, the

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member only podcast, which is kind of

like the audio companion for each one

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of the modules we have in the program.

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We have, like, an audio

version of the module kind of.

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I'm curious, and, and also we have,

you know, all of our, our community and

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also all of the lessons are on an app.

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I'm just curious, were you using,

like, any of, any of that, like, on

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your phone, like, while you were in

the car, like, listening to member

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only podcasts or, like, maybe you were

waiting for your next order, like,

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you did some of the lessons on your

phone, or was it mostly at your desk?

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David Kovacs: Oh, yeah, definitely.

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When I was out driving around, I

listened to just about every episode

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of the podcast, both, well, one

on Spotify and the members only.

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Did a lot of passive learning

while I was out on the road.

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Avery: Awesome to hear.

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I'm super glad to hear that because

you, you create this thing and you're

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like, "I think this is gonna be

useful for people," but you don't

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always hear how people are using it.

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So, um, I'm super excited to, to

hear that it was useful for you.

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Um, I'm curious, like, what you thought

was maybe the hardest part about landing

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a data job in general and, and maybe in

the boot camp or outside the boot camp.

247

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David Kovacs: Definitely coming from

a completely unrelated field was

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pretty hard to, um, I guess find that

first opportunity in this, in this

249

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new industry I'm trying to get in.

250

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Avery: That makes a lot of sense.

251

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I can't even really imagine, like,

how many vocabulary words that

252

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architecture and landscape design

have in common with data analytics.

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Like-

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David Kovacs: Not many

255

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. Avery: May- yeah, like, did you

guys, do you guys use Excel in, in

256

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your industry at all or not really?

257

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David Kovacs: Uh, very little.

258

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Yeah.

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N- in the last job I, no I think we

talk about it on, on the podcast every

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so often, like if you opened Excel

like once a year for the past X amount

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of years, put that on your resume.

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That's probably where I was at.

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Okay.

264

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Yeah,

265

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Avery: that's, that's some

years of experience, right?

266

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Uh, I love that.

267

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Um, okay, so let's talk about, yeah,

the hardest part was actually, you know,

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transitioning out of your, your current

industry to a completely new industry and

269

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getting someone to take a chance on you.

270

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At what point did you start

applying for data jobs?

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David Kovacs: I started applying

around the beginning of the year.

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I got my resume together

and a portfolio together.

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Definitely wasn't perfect, but I

knew at the beginning of the year

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I just had to start doing it.

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Avery: Well, I think you joined

the accelerator like, like almost

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mid-January or early January, and I

think you started this role in April.

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So like were you basically-

Yeah … applying for jobs in January?

278

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David Kovacs: Yeah, I was applying

for jobs before I even started the

279

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accelerator, and I was applying-

I lo- … the whole way through.

280

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Avery: I love that.

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Okay.

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Um, and how many like applications

do you feel like that you sent before

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you started landing interviews?

284

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David Kovacs: So I didn't do a very

good job at tracking that, but I had

285

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to have applied for well over 100,

maybe 200 jobs before landing my role.

286

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Avery: Okay.

287

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That's not bad.

288

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Did you have like a lot of o- other

interviews, only a few other interviews?

289

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David Kovacs: Only two other interviews.

290

00:12:53,485 --> 00:12:53,855

Avery: Okay.

291

00:12:54,125 --> 00:12:55,776

That's still, that's still pretty good.

292

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Um, like a, an app- like if you've

applied for 200 jobs and you had like

293

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three interviews, I mean, that's not,

not terrible, especially, especially like

294

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coming from someone who doesn't have a

degree, who doesn't have prior experience.

295

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Like the, the usual, like an average

for, for data analysts right now

296

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is for every 100 applications you

send out, you get four interviews.

297

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So the job you ended up landing,

where did you find that job?

298

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Like what platform did you find it on?

299

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Do you remember applying for that job?

300

00:13:21,674 --> 00:13:23,074

David Kovacs: Uh, yeah,

found it on Indeed.

301

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Avery: Okay.

302

00:13:23,714 --> 00:13:25,414

On Indeed, and you applied for it.

303

00:13:25,494 --> 00:13:28,044

Um, and then do you remember, like,

how quickly they reached out to you

304

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and, and what that process was like?

305

00:13:29,704 --> 00:13:31,443

David Kovacs: So they reached

out to me pretty quickly.

306

00:13:31,534 --> 00:13:34,104

Avery: And then you had, like, a, a

phone interview with, like, a recruiter?

307

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Is that what it was?

308

00:13:35,094 --> 00:13:38,674

David Kovacs: Uh, I had a virtual

interview with my now supervisor and

309

00:13:38,674 --> 00:13:42,153

another person in the company where

we, you know, talked about the role and

310

00:13:42,313 --> 00:13:44,453

my experience, if it'd be a good fit.

311

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Avery: And, and this company that you're

interviewing with, I guess we should say

312

00:13:47,204 --> 00:13:49,264

what the company is and, and what they do.

313

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What- Yeah … what does

your company do, actually?

314

00:13:51,573 --> 00:13:54,463

David Kovacs: So the company I work

for is called SmartPro Financial.

315

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We're Dave Ramsey SmartVestors,

if you've heard of Dave Ramsey.

316

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I've been listening to Dave Ramsey for

several years, so I was able to speak

317

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very enthusiastically in my interview

and my cover letter that I did.

318

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And the very first interview that I did,

my supervisor told me about how he…

319

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I forget the exact number he gave me,

but it was well over 100 applicants

320

00:14:15,754 --> 00:14:18,583

that he reviewed, and he said I

was the only one who even mentioned

321

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Dave Ramsey in my cover letter, so.

322

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Avery: Very nice.

323

00:14:21,753 --> 00:14:22,173

Okay.

324

00:14:22,173 --> 00:14:23,763

So I'm, I'm hearing two things.

325

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One, you actually wrote, like, a

cover letter, like, as a PDF and, and

326

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just submitted it with your resume.

327

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Is that right?

328

00:14:29,393 --> 00:14:30,683

David Kovacs: Yeah, I

did the cover letter.

329

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I even found the CEO on LinkedIn

and wrote him a DM- Okay

330

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… Avery: cold

331

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David Kovacs: message.

332

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Avery: Very cool.

333

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Very nice.

334

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So you did a, a cold message

and just a cover letter.

335

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And in your cover letter you mentioned

Dave Ramsey, which this company, you know,

336

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has, has lots to do with Dave Ramsey.

337

00:14:44,114 --> 00:14:46,533

And so that's probably one thing that made

you stand out, is you actually connected

338

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to the business on the business' level.

339

00:14:48,373 --> 00:14:51,783

You weren't just, like, another,

you know, data analyst applying.

340

00:14:51,843 --> 00:14:55,454

You were a data analyst who was, you know,

was familiar with and, and intrigued and

341

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interested in this Dave Ramsey world.

342

00:14:57,373 --> 00:14:59,053

So that kind of set you apart, you think.

343

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Is, is that what you're saying?

344

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David Kovacs: Yeah, and they,

they even do their own podcast

345

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and YouTube channel, and I…

346

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When I found out about them, I just

immediately started binging in, finding

347

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out about who they were and, you know,

what they stood for as a company.

348

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And at that point, I think, like, all

other options went out the window for me.

349

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It was, "I have to work for this company."

350

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So I sent my resume, sent a

cover letter, sent cold messages.

351

00:15:23,983 --> 00:15:27,033

I was even going to go show

up at their office with my

352

00:15:27,033 --> 00:15:28,433

resume if that's what it took.

353

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Like, I wanted to get in front

of somebody and talk to them,

354

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be like, "I wanna do this."

355

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Avery: Awesome.

356

00:15:33,952 --> 00:15:37,202

That goes to the whole, uh,

quality over quantity, right?

357

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And, like, you need to, you need to

apply to a lot of data jobs, but the

358

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ones you are really, really interested

in, the more effort you put into them,

359

00:15:43,702 --> 00:15:47,441

the higher chance you have of actually,

you know, moving forward with that.

360

00:15:47,452 --> 00:15:50,201

So that's really cool that

that, that paid off for you.

361

00:15:50,262 --> 00:15:53,951

Um, in the interview, did they

mention, like, uh, the bootcamp?

362

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Did they mention, you know,

being an architect previously?

363

00:15:56,562 --> 00:15:57,942

Did they mention your portfolio?

364

00:15:57,971 --> 00:15:59,602

You know, what did they

mention in the interview?

365

00:15:59,602 --> 00:15:59,822

What…

366

00:15:59,861 --> 00:16:02,921

You know, obviously your interest in,

in the whole- their whole world was

367

00:16:02,922 --> 00:16:05,852

really impressive, but did they say

anything else about, like, why they

368

00:16:05,942 --> 00:16:07,242

w- were interested in you, David?

369

00:16:07,652 --> 00:16:10,941

David Kovacs: Yeah, they did question

about the bootcamp, about the internship.

370

00:16:11,111 --> 00:16:14,741

My supervisor did look at my

portfolio and questioned me about one

371

00:16:14,742 --> 00:16:16,491

particular project that I did in it.

372

00:16:16,762 --> 00:16:17,332

Avery: Super cool.

373

00:16:17,341 --> 00:16:18,441

Do you remember what project it was?

374

00:16:18,442 --> 00:16:20,481

I'm just curious, like, what

caught, what caught their eye.

375

00:16:20,762 --> 00:16:24,351

David Kovacs: So it was actually a random

Google Sheet project that I did that

376

00:16:24,352 --> 00:16:28,971

was completely unrelated to the program,

but that's primarily the program that

377

00:16:29,001 --> 00:16:33,371

we use at SmartPro is Google Sheets,

'cause we have a lot of people in our

378

00:16:33,371 --> 00:16:37,151

office and a lot of advisors outside of

our office that we need to collaborate

379

00:16:37,152 --> 00:16:38,782

with, so that's the tool that we use.

380

00:16:39,032 --> 00:16:39,892

Avery: Perfect, that makes sense.

381

00:16:39,932 --> 00:16:40,491

I love that.

382

00:16:40,492 --> 00:16:42,552

So you had a project off of the tool.

383

00:16:42,581 --> 00:16:44,561

I- I imagine that was listed

in the job description.

384

00:16:44,561 --> 00:16:48,512

So whenever you can have a project with a

tool they mention in the job description,

385

00:16:48,521 --> 00:16:51,531

that's great, and I think it's fantastic

that it was a project outside of the

386

00:16:51,532 --> 00:16:55,162

program because that's what I want, and

that's, like, the importance of, you know,

387

00:16:55,231 --> 00:16:58,002

the, the program in general is, like,

okay, you're gonna- I'm gonna show you the

388

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pattern of taking data and turning it into

a, a published project, you know, nine

389

00:17:02,341 --> 00:17:05,570

times in a row, and then you do that more

in the future, and you take what you've

390

00:17:05,571 --> 00:17:07,011

done in the past and you package them up.

391

00:17:07,021 --> 00:17:09,241

Like, that's part of the process

of just learning to do a portfolio.

392

00:17:09,330 --> 00:17:11,501

So I absolutely love that, and

I love that it's Google Sheets.

393

00:17:11,560 --> 00:17:12,411

Um, I'm a big fan.

394

00:17:12,431 --> 00:17:15,512

I like Google Sheets more than

Excel, but, um, Excel is just

395

00:17:15,512 --> 00:17:18,002

used- Yeah … more, more often,

which is why we, we teach Excel.

396

00:17:18,002 --> 00:17:20,171

But they're basically the same,

so that's, that's perfect.

397

00:17:20,201 --> 00:17:20,962

Um, amazing.

398

00:17:20,962 --> 00:17:22,361

We talked about how we did a reference.

399

00:17:22,451 --> 00:17:27,662

Uh, I mentioned all the good work you did,

uh, in the, uh, on the internship project,

400

00:17:27,681 --> 00:17:29,362

and then you got, you got the offer.

401

00:17:29,411 --> 00:17:33,252

And then this is something, um, as

well that I think was useful to you.

402

00:17:33,331 --> 00:17:37,202

Um, you and I did some DMs back and

forth about the offer you got, and,

403

00:17:37,331 --> 00:17:40,681

like, what we thought maybe was a market

fair offer, and I think we were able

404

00:17:40,681 --> 00:17:42,161

to get you a, a little bit of a raise.

405

00:17:42,161 --> 00:17:42,832

Is that correct?

406

00:17:42,951 --> 00:17:43,521

David Kovacs: That's correct.

407

00:17:43,892 --> 00:17:44,262

Yeah, yeah.

408

00:17:44,392 --> 00:17:44,961

Avery: Love to hear it.

409

00:17:44,972 --> 00:17:44,992

Um-

410

00:17:45,011 --> 00:17:47,892

David Kovacs: Yeah, I told, me, I

told them an amount that I wanted,

411

00:17:47,892 --> 00:17:49,912

and we, we met, kind of met halfway

412

00:17:50,478 --> 00:17:50,868

Avery: Perfect.

413

00:17:50,868 --> 00:17:52,858

Yeah, that's, that's something

that I think a lot of

414

00:17:52,858 --> 00:17:54,218

negotiations kind of go that way.

415

00:17:54,238 --> 00:17:57,468

They say a number, you say a high number,

and we both compromise in the middle.

416

00:17:57,708 --> 00:18:00,438

But I think that's a win-win 'cause, you

know, we're making, we're making more

417

00:18:00,438 --> 00:18:04,118

that way and, um- Sure … yeah, I think

that's what we talked about over, over DM.

418

00:18:04,197 --> 00:18:05,588

Um, okay, so now you have the job.

419

00:18:05,617 --> 00:18:08,917

I'm curious, like, what your job actually

looks like now on the day-to-day.

420

00:18:08,988 --> 00:18:10,887

Um, you mentioned you're

using Google Sheets a lot.

421

00:18:10,937 --> 00:18:12,978

Um, I guess what type of

problems are you doing?

422

00:18:12,988 --> 00:18:16,237

Are you interacting a lot with

internal people, with external people?

423

00:18:16,238 --> 00:18:18,867

What is the, the day in the life

look like for, of David now?

424

00:18:19,048 --> 00:18:22,227

David Kovacs: So I've been building a

lot of tools inside of Google Sheets to

425

00:18:22,228 --> 00:18:26,518

help us run reports more efficiently,

working with, um, the team I'm on.

426

00:18:26,518 --> 00:18:31,147

It's called OSJ, Office of Supervisory

Jurisdiction, where we're responsible

427

00:18:31,158 --> 00:18:33,258

for compliance and training.

428

00:18:33,527 --> 00:18:34,117

Mm.

429

00:18:34,178 --> 00:18:35,158

Avery: That sounds super cool.

430

00:18:35,257 --> 00:18:37,668

Um, so I love that- Well, I'm- … you're

doing, like, the internal tools.

431

00:18:37,668 --> 00:18:41,317

That's always, like, a, a good role for,

for someone to have, is, like, they're

432

00:18:41,318 --> 00:18:45,397

the internal, uh, data tool person, and

you're making everyone's life easier.

433

00:18:45,548 --> 00:18:47,277

Uh, I think that's, that's really awesome.

434

00:18:47,388 --> 00:18:50,247

And the company and, like,

the team, how has that been?

435

00:18:50,257 --> 00:18:51,868

Like, have you enjoyed working with them?

436

00:18:52,017 --> 00:18:56,468

Do you feel like they, like, ever judge

you because you come from, you know, you

437

00:18:56,468 --> 00:18:59,207

don't come from a data background, you

don't come from a financial background.

438

00:18:59,537 --> 00:19:01,847

Uh, you come from, like,

this architecture background.

439

00:19:01,847 --> 00:19:04,058

Do you feel like that's ever

present in the company at all or no?

440

00:19:04,387 --> 00:19:05,807

David Kovacs: Uh, yeah,

so the culture's great.

441

00:19:05,838 --> 00:19:07,667

I've been getting along with everybody.

442

00:19:07,707 --> 00:19:11,717

Bosses, supervisors are very helpful,

as well as all the other employees.

443

00:19:11,717 --> 00:19:15,408

Uh, actually, a lot of us don't

come from a finance background.

444

00:19:15,417 --> 00:19:18,407

Um, and it was kind of explained to

me in the interview that they almost

445

00:19:18,427 --> 00:19:22,087

prefer that you do not come from a

financial background, because the

446

00:19:22,088 --> 00:19:26,077

way that we do things is very much

different than other financial firms.

447

00:19:26,387 --> 00:19:27,457

Avery: That's very cool to hear.

448

00:19:27,457 --> 00:19:31,427

So it's almost like your, your

inexperience or your untraditional

449

00:19:31,437 --> 00:19:35,077

background ended up being an

advantage for you, uh, in the end.

450

00:19:35,088 --> 00:19:35,118

Right.

451

00:19:35,187 --> 00:19:39,557

I'm curious if you could go back to,

you know, David as the architect,

452

00:19:39,777 --> 00:19:43,527

you know, like a year ago, unhappy,

looking to, to pivot, you know,

453

00:19:43,527 --> 00:19:44,907

deciding on data analytics.

454

00:19:45,027 --> 00:19:48,507

Um, like, what's something that you

would tell that David a year ago,

455

00:19:48,527 --> 00:19:51,868

the aspiring data analyst that he was

like, "Can I actually go from being

456

00:19:51,868 --> 00:19:53,718

an architect to, to data analyst?"

457

00:19:53,718 --> 00:19:54,407

What would you tell them?

458

00:19:54,667 --> 00:19:57,287

David Kovacs: I would say it's totally

possible, and don't wait to start

459

00:19:57,848 --> 00:19:58,338

Avery: I love it.

460

00:19:58,418 --> 00:20:00,548

Straightforward, straightforward,

and you can do it.

461

00:20:00,568 --> 00:20:02,608

That's, that's so glad

to, to hear it, yeah.

462

00:20:02,838 --> 00:20:05,248

Is there anything about your

career, your previous career,

463

00:20:05,248 --> 00:20:06,457

that you actually miss at all?

464

00:20:06,538 --> 00:20:07,338

David Kovacs: No, not really.

465

00:20:08,778 --> 00:20:13,048

I mean, um, I guess to speak to something

I said earlier about, like, being able

466

00:20:13,048 --> 00:20:17,127

to, like, go to the sites and see the,

uh, see the project that you work on come

467

00:20:17,127 --> 00:20:20,697

to life, look at this building, you know,

physical evidence of what you've done.

468

00:20:20,797 --> 00:20:25,318

But I feel like in data analytics

you have, like, real time evidence

469

00:20:25,318 --> 00:20:28,628

of the impact that you're making

with the tools that you're building,

470

00:20:28,628 --> 00:20:30,177

the reports that you're building.

471

00:20:30,318 --> 00:20:33,418

So I get just as good, if not

better, feeling from that.

472

00:20:33,568 --> 00:20:34,797

Avery: That's actually a

really interesting point

473

00:20:34,818 --> 00:20:36,218

because, yeah, I can't imagine.

474

00:20:36,307 --> 00:20:39,547

You work on something as an architect, and

then it takes like five years for you to

475

00:20:39,547 --> 00:20:40,308

actually, like, see it be, be finished.

476

00:20:40,308 --> 00:20:40,878

And s- and some

477

00:20:40,878 --> 00:20:42,957

David Kovacs: of these projects

are never even realized.

478

00:20:43,368 --> 00:20:44,358

Avery: So, so true.

479

00:20:44,568 --> 00:20:47,648

When I, when I was a chemical lab

technician, uh, I would, you know,

480

00:20:47,658 --> 00:20:50,708

set up these experiments and, you

know, monitor these experiments, and

481

00:20:50,737 --> 00:20:53,597

it would take like two hours for me

to see the results of the experiment.

482

00:20:53,608 --> 00:20:56,568

I remember thinking, "It sucks waiting

here to see if it actually worked or not,

483

00:20:56,568 --> 00:20:58,687

if, like, my work was, was valid or not."

484

00:20:58,687 --> 00:21:01,507

And that's something you get, like,

immediate feedback with a data analyst.

485

00:21:01,507 --> 00:21:03,437

It's like, does this tool work or not?

486

00:21:03,488 --> 00:21:05,008

Is someone using this tool or not?

487

00:21:05,008 --> 00:21:05,257

So-

488

00:21:05,268 --> 00:21:05,478

David Kovacs: Yes

489

00:21:05,488 --> 00:21:06,457

… Avery: that is pretty satisfying.

490

00:21:06,467 --> 00:21:09,177

That is an interesting part of

data analytics I, I kind of forget

491

00:21:09,177 --> 00:21:10,267

about, I don't think I talk about.

492

00:21:10,448 --> 00:21:14,367

Um, what would you say to someone

who's, who's, like, thinking about

493

00:21:14,367 --> 00:21:15,957

maybe, maybe in the same boat as you?

494

00:21:16,018 --> 00:21:18,708

They're, they're listening to the

podcast in the car, they've been

495

00:21:18,777 --> 00:21:22,117

interested in the accelerator but

not sure if it's, if it's for them.

496

00:21:22,128 --> 00:21:24,428

You know, what, what advice would you give

them, or what would you say about that?

497

00:21:24,677 --> 00:21:27,168

David Kovacs: I'd say I

definitely got a lot of value

498

00:21:27,168 --> 00:21:29,027

out of the accelerator program.

499

00:21:29,107 --> 00:21:31,397

And again, like, what are you waiting for?

500

00:21:31,397 --> 00:21:33,127

Just hop into it, you know?

501

00:21:33,128 --> 00:21:37,128

Um, I think back all the time about,

you know, if I would've done this,

502

00:21:37,128 --> 00:21:41,507

like, several years ago, I could

already be so far into my career by now.

503

00:21:41,507 --> 00:21:43,047

And, you know, you don't wanna wait.

504

00:21:43,438 --> 00:21:46,388

Avery: It's, it's like they say,

like, the best time to plant a tree

505

00:21:46,388 --> 00:21:48,578

was 10 years ago, the next best

time is today or whatever, right?

506

00:21:48,578 --> 00:21:49,038

It's right now.

507

00:21:49,168 --> 00:21:50,708

Um, that makes a lot of sense.

508

00:21:50,788 --> 00:21:53,698

Um, in terms of, like, what's next

for your career, what are you,

509

00:21:53,698 --> 00:21:55,137

what are you focused on right now?

510

00:21:55,138 --> 00:21:55,938

Are you just, like…

511

00:21:55,978 --> 00:21:58,397

I mean, I know you're brand new,

so is it just, like- Hmm … um,

512

00:21:58,418 --> 00:22:01,317

you're interested in, like, learning

from the people around you, you're

513

00:22:01,317 --> 00:22:03,838

learning and making an impact at this

company, and just excited about that?

514

00:22:04,038 --> 00:22:06,317

David Kovacs: Yeah, definitely lear-

you know, learning from the people

515

00:22:06,317 --> 00:22:10,698

around me, like you said, getting the

absolute best at my job that I can.

516

00:22:10,858 --> 00:22:14,047

And then from there, you know, I'd

like to learn more coding and get

517

00:22:14,047 --> 00:22:18,487

into more data engineering type stuff

maybe, like to learn how to, how I

518

00:22:18,487 --> 00:22:22,457

can help other departments in the

company, and I'd like to maybe even

519

00:22:22,497 --> 00:22:24,108

start doing some freelance work.

520

00:22:24,307 --> 00:22:24,927

Avery: That's awesome.

521

00:22:24,938 --> 00:22:27,607

The- you're just starting out in

your journey, and there's so many

522

00:22:27,607 --> 00:22:29,237

cool places, um, that you can go.

523

00:22:29,237 --> 00:22:32,787

It's, like, really that, that data analyst

role is such, like, a, a steppingstone

524

00:22:32,798 --> 00:22:35,367

role where you could literally go so many

different places, like you said, data

525

00:22:35,368 --> 00:22:37,477

engineering or- Mm-hmm … or more coding.

526

00:22:37,567 --> 00:22:40,507

You know, those, all those different

routes that you have, and that's awesome.

527

00:22:40,637 --> 00:22:41,677

Well, great, David.

528

00:22:41,677 --> 00:22:44,317

I think, uh, everyone who's listened

to this episode will have, like,

529

00:22:44,317 --> 00:22:47,407

a really good feeling of, like, if

you're in a career that you hate, there

530

00:22:47,407 --> 00:22:50,807

is light at the end of the tunnel,

no matter how different it is from

531

00:22:50,807 --> 00:22:52,467

data analytics, and it can be done.

532

00:22:52,478 --> 00:22:56,337

You know, uh, it took you about a, a

year, I think, from maybe deciding to

533

00:22:56,338 --> 00:22:59,467

actually landing your data job-ish,

um, from joining the, the boot camp to

534

00:22:59,467 --> 00:23:02,677

landing your first data job, like 90

days, which I think is an obtainable

535

00:23:02,737 --> 00:23:04,807

timeframe for, for pretty much everyone.

536

00:23:04,807 --> 00:23:07,168

I think I'm grateful for you

for being willing to come on the

537

00:23:07,168 --> 00:23:10,667

show and be an example of what

that roadla- roadmap looks like.

538

00:23:10,688 --> 00:23:13,638

What does it actually look like to

go from, you know, architecture and

539

00:23:13,638 --> 00:23:15,487

landscape design to, to data analyst?

540

00:23:15,497 --> 00:23:18,767

Because as far as I know, I mean, I'm sure

you're not the only one on planet Earth.

541

00:23:18,767 --> 00:23:20,857

But you're the only one that I

personally know, and I've been

542

00:23:20,857 --> 00:23:23,018

doing this, uh, s- for six years.

543

00:23:23,027 --> 00:23:26,517

So, um, I think, I think you're, you're

a really great example, and I appreciate

544

00:23:26,567 --> 00:23:28,748

you coming on and sharing your story.

545

00:23:28,748 --> 00:23:29,447

I really appreciate it.

546

00:23:29,577 --> 00:23:30,147

David Kovacs: Thanks, Avery.

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