Episode 172

full
Published on:

12th Aug 2025

172: Tesla Data Analyst: This is how to land a data job (Lily BL)

What does it take to land a data analyst job at Tesla, and what challenges await you once you're there? Join me as I interview Lily BL, a former Tesla data analyst, who reveals her exhilarating journey in the world of data at one of the world's most innovative companies.

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⌚ TIMESTAMPS

00:00 - Introduction

00:31 - Working on Data Projects at Tesla

01:45 - Was it challenging working at Tesla?

08:34 - Hiring Process and Employee Evaluation

11:56 - Tools and Technologies Used

13:38 - Lily Landing the Job at Tesla

15:42 - Advice for Aspiring Data Professionals

19:36 - How the Data Analytics Accelerator helped Lily

25:11 - Data Analyst Titles Matrix

29:50 - Linking Business Needs to Data Solutions

🔗 CONNECT WITH LILY BL

🤝 LinkedIn: https://www.linkedin.com/in/lilybl/


🔗 CONNECT WITH AVERY

🎥 YouTube Channel: https://www.youtube.com/@averysmith

🤝 LinkedIn: https://www.linkedin.com/in/averyjsmith/

📸 Instagram: https://instagram.com/datacareerjumpstart

🎵 TikTok: https://www.tiktok.com/@verydata

💻 Website: https://www.datacareerjumpstart.com/

Mentioned in this episode:

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Transcript
Avery Smith:

This is my Tesla, but I've never worked for Tesla.

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Luckily, one of my accelerator

students has Lily BL, and today I

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had the chance to interview her.

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I got to ask her what it was like to work

at such a unique, cool company, what it

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actually took to get her there, and what

advice she'd give to those of you watching

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who are interested in working at Tesla

or other really cool tech companies.

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So let's go ahead and

get into the episode.

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Lily, your career has taken you

to Tesla, pg and e, Intel, and

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now the city of San Francisco.

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But your first full-time

data job was at Tesla.

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What was it like working

on data projects at Tesla?

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Uh,

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Lily BL: it was nerve wracking

and exhilarating at the same time.

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I was not sure what to expect because

when I took the role on, it was blended

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between a couple of different areas.

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But as, uh, I worked more and

more like day after day, I could

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see what their data needs were.

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They had data in multiple systems for

multiple reasons, and it was just so

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much in volume that they couldn't keep

track of how to look at it concisely.

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They had to go through embedded

records to get an answer.

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So I think when I first got fired on,

the word on the street was she's like an

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admin assistant to the district manager.

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If you have administrative stuff

you can't do, just give it to her.

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Once the boss saw what I could do.

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It completely changed, and I

was monitoring everything for

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data flow to determine what

kind of visuals could be built.

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The scary part was like, I don't

know, and the exhilarating part

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was, but I can figure it out.

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Avery Smith: That's awesome.

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And I'm glad to hear that.

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Uh, an employer like that was, you

know, first off recognized your talents,

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but then second off was like, okay,

Lily, Lily can, uh, do this stuff.

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Let's give her some, some more tasks.

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Did you feel like what, what you were

doing, like was, was super cutting

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edge or did you feel like it was

more like regular, regular tasks?

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Like, um, did you feel like

challenged in what you were doing?

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Lily BL: I did feel challenged in

what I was doing because it had a

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lot of impact once it was completed.

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The technology and the how to itself

surprisingly was very basic, so it was

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continuously searching for the specific

thing that will fix this specific problem.

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And then gathering all the solutions

to say, Hey, this is how you can

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improve your data situation overall.

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Avery Smith: That's interesting.

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And, and correct me if I'm

wrong, uh, you know, Tesla is

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obviously a, a large company.

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I worked for ExxonMobil, a large

company at these large companies.

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You hear this phrase, I'd never

really heard it before, this word.

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Um, it's called disparate.

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Uh, and basically, or siloed, I

think is the other thing that they

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said a lot at Exxon that data is

siloed or that data is disparate.

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Basically, I think you kind of said

something similar where, you know,

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at these large companies there's a

lot of data, um, and there's a lot of

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systems, but the problem is this system

doesn't necessarily talk to this system

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and this data is kind of stuck here.

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And you know, this data, they only

enter it in an Excel, uh, database.

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So it doesn't really like

integrate with anything else.

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And so it sounds like your job was almost

like you were like data analyst glue.

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To try to tie in all these different

data sets from this different systems.

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Did I, did I get that wrong, or

is that kind of what you did?

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Lily BL: Yeah, you're

nailing it on the head.

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It was very interesting because

a large portion of the actual

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engineering work was done inside

of a software called Jira, which is

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meant for tracking, uh, the project.

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That's the data that was needed

to be reviewed and the company's

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decision was not to view it.

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Outside of that, I would run validations

in Excel to make sure the numbers it gave

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me were accurate to what I visualized,

and so I had to actually learn Jake to

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be able to put together what I needed

and I was limited by the visualizations

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preselected for project management.

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At the end of it, it was super cool

because I kind of created a grid.

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Um, it was like a large,

uh, standing rectangle.

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When you looked at it up and down.

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That was the information for the district.

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Managers.

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Managers.

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So each team and all of their staff.

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You could look at what they

did and when they did it, what

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was still pending Vertically.

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Each team had a, a call.

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When you looked at it horizontally,

those were all the KPIs my

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district manager had requested.

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So they were subject to some standards

and it was so cool because I didn't know

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how to do that till I was done with it.

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Um, and so then I was like,

yeah, this is what I wanted.

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Uh, but I needed some help from

the management team because to

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make sure the data had integrity,

I didn't set the conditions.

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So I did push for them to tell

me, this is how this is defined.

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This is the threshold

to, uh, determine this.

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And once I had them deliver to me,

uh, some definitions, I used those

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decisions to build out the, the

visual and they ended up loving it.

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'cause I was able to color code

it and I assimilated it to red

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light, green light, yellow light.

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So if it's marked green,

don't worry about it.

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You don't have to look.

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If it's yellow, you kind of

need to keep an eye on it.

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But if it's red, you need

to go in and investigate.

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That was just relative to the

data produced by the teams.

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Then there was the data produced

by the hardware and that

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those were different systems.

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So then in those systems, I took

that one to Excel and I was able to

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create a chart that had a threshold.

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So I had them, again, define

what the threshold was, and

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let's say it was like 10.

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Once you got 10 of these things,

the chart would go from being green.

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To now being red 'cause

it crossed the threshold.

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So the division manager could look

at all of these things at a glance

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and be like, oh, red is where I need

to be, and figure out what happened.

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Um, and then, uh, separate from that, I

was also, uh, building into Tableau, uh,

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master portfolio so that the division

manager could just look in there at

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all of the teams and all of the things

that were of interest to him because he

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would take that information back to the

meetings with the rest of management.

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They would decide what would come

next based on what was there.

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So like if a lot of equipment was

failing, they would say, Hey, your

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team is under producing because you

have 10 of these different kinds of

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machines and you're only putting out

like half of the results we expected.

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Or based on the analysis, half of

the machines were not available

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for one reason or another.

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So it's like, this is why our numbers

are lower than what's expected.

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Per what is available.

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Only half is available, which you

couldn't really tell any other

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way is they were kind of, uh.

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Digging constantly before we were

able to build the visuals, uh, to

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determine, uh, what was really going on.

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So it really facilitated

the manager to manage.

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And he was actually a really good manager,

so he knew where the weak points were.

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He just was not a data person or a data

analyst to be like, I need this, felt

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like this, and like that to get this.

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But he knew what he wanted, so it

was a perfect partnership because

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I could build it and he could tell

me if it worked or didn't work.

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Avery Smith: Lily, this is super cool.

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Thanks for sharing all of this.

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Um, I have so many places I, I want

to go based off what you just told me.

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Uh, the first was, I had never

heard of J Quill, but I had

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a chance to look it up here.

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So that's, that's Jira Query

Language or Jira, I don't know

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how to say that, but for those who

never heard of that, it's JIRA.

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Um, and it is owned by

Atlassian, I'm pretty sure.

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Um, and it is like a project management

software that a lot of, uh, software

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companies use to develop software.

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So you're, you were kind of

looking at project data, um,

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which is, which is really neat.

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Uh, and it sounds like you were working

like really close to these, you know,

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kind of higher up stakeholders who,

you know, they need a bird's eye view

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of what's going on in their business.

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It's, they kind of, like you said, have

like a gut feeling of this is, you know,

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maybe this part is struggling right

here and I have a feeling why, but I'm

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not exactly a hundred percent sure.

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What it sounds like is you tied up a

bunch of loose ends and you know, this,

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these disparate data sets, and you're

able to create a data visualization, uh,

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that helps these managers see, you know,

maybe what's struggling in the business,

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maybe what's doing good, um, what they

need to worry about and what they need

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to maybe put their, their focus on.

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Um, so it sounds like you were almost

giving them like supervision goggles to

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like look into their business and like

actually see is everything, is everything

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going the way it's supposed to go because.

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You know, as someone who runs a

business, I obviously do not run a

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business close to the scale of Tesla.

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Like one division of Tesla, I'm sure is

a hundred times bigger than my business.

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Uh, but even now I have, you know, Trevor

Maxwell helping me out with coaching.

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I have Isaac Ania who's

helping with my community.

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I have, uh, a podcast producer

and editor, and I don't know

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what's going on half the time.

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They're just awesome.

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Uh, employees doing a great job,

but I do wanna be like, okay.

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How do I get above the business and like

actually look down on it and see like,

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okay, what's going well and what's not.

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And it sounds like you were able

to do a bunch of analysis to kind

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of produce that for these managers.

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Lily BL: Yeah.

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And uh, one project, uh, that I was at

hair away from completing, 'cause I was

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missing one definition, uh, which I think,

uh, would have had a huge impact, is,

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um, I worked on their hiring process.

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So I would sit in on their

meetings and see how they went

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through their hiring processes and

would sit in on the interviews.

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And then I would also look at fubu.

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They had already hired because of the

way they, uh, did bonuses there, the

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managers had to divide a percentage of

bonus among all of the existing teammates.

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So I was able, based on watching

the data flow, um, I was able to

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determine what the standards were.

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They already knew that they had tiers.

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Like we have engineer 1, 2, 3, 4,

5, a lead, whatever, a, a manager.

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Then I was able to zero in on what

are the standards, uh, that this

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person needs to complete or being

knowledgeable in, in order to ascend

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to the following tier, which translates

to more money for the employee.

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And so then, um, we got, I got, I

reviewed everything and I set it

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up, but what was missing was the

metrics associated to each tier.

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And so I left it alone

to not push a project.

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Uh, and hack the pay be incorrect.

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At the very end of the, uh, contract,

the HR published the standards

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or the, the pay for each scale.

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So that was the missing piece I needed.

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But with that, it would've facilitated

all of the yearly reviews of the

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management team to enable or to

determine very quickly, oh, this person

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hit these projects and these projects

are labeled within this category.

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So while they were working as

an engineer too, their work.

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Function was actually an

engineer, four or five.

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So they qualified for the bonus

and potentially a promotion.

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Um, we were also very proactive there with

kind of working with the, um, employees

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being more, um, uh, how do you call it?

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Um.

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It's not affirmative, but it's being

more proactive about their findings

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and stressing their good works.

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So with so many people on the team, I

don't know every single thing you did,

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so take the initiative and tell me,

Hey, I completed these multiple things.

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That way it's fresh on my mind.

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So I didn't get to the see there.

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But after I would have completed

that project with the raids, my goal

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would've been to work with the team

one-on-one and have them pitch me.

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Their successes and then I

could categorize it for them.

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Like, okay, what you said

falls into this or into that.

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Do you agree or disagree?

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And then teach them how to make the

argument for their good works better.

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Um, it's delicate to do in business,

but it's like a negotiation.

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So you actually need to

practice it in order to get it.

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And this particular company was

open to that they wanted to hear.

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So, uh, that was like the

icing on the cake for me.

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We didn't get to finish it, but

it's something that would've

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been proactive for everybody.

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The company would've had a very,

well, a very articulate staff, which

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is needed for problem resolution.

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And then with the market as it was

constantly laying off and whatnot,

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this employee would have had the

skills sharpened to then go right

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into another position if they were

laid off and quickly get another role.

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Avery Smith: Hmm.

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Very cool.

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Um, while you're at Tesla, what

tools did you use the most?

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Lily BL: Um, I think I used Jira the

most and excel for the validation.

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Um, I got heavier into the

administrative side of, of the

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software because for Tableau and Jira

I was bringing in add-ins to make

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them more functional for analytics.

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Uh, so for companies, uh, you have to

connect the Tableau software to what,

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wherever your data is in the company.

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When you use Tableau as an

individual user, you just

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connect it to your worksheet.

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You can't connect it to something

else if you have it, but typically

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you just use a worksheet.

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So that was different.

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And it was a full host

of security clearances.

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Um, so I did a little bit of the

administrator stuff, but, uh, JIRA

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and Excel round my validations.

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Avery Smith: That's awesome.

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I think that's true.

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And, and you mentioned JQL.

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Is that kind of like SQL or

how, how are those related?

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Lily BL: Yeah, so it's very similar to

the commands in sql, so that's why I

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was able to learn it pretty quickly.

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Uh, but then, um, some

things are specific.

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It uses a lot of, uh, a lot

more keywords than you would

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expect, and they're different.

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Um, the software itself

does try to help you.

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Like it lets you click on buttons

and produces the code for you.

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Uh, to an extent, but then you

have to have modifications.

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So I would allow the software to allow

me to click to build some of the stuff,

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but then I would review it and determine,

oh, it still needs this functionality,

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or this, or this other group of people.

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And you would have to manually put that

into the existing code to make it function

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Avery Smith: super neat.

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So it's basically SQL for Jira, and

they try to make it a little bit easier

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for you to actually write the code.

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Um, okay.

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I'm actually not sure the

answer to this question.

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How did you get this job at Tesla?

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I remember you messaging me when

you got the job offer and you're

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like, Hey, these are the details.

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What do you think?

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Should I take this job or not?

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You know, it's one of the things I try

to do with my accelerator students, but

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I don't remember off the top of my head.

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This was a couple years ago now.

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Um, how you ended up landing this job?

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Lily BL: Yeah, I think it

was through networking.

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Um, at the time I was an instructor

for co-op and I had a cohort that

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I would teach in the evenings.

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One of my cohort students actually

got hired by them about a month or

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so before I helped them finalize his

work that they wanted him to see.

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So then about a month or so later, I got,

uh, contracted by a recruiter on LinkedIn.

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I checked with him.

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It ended up being the same

person that contracted him.

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So I think what happened is that I

popped up for her in association to him.

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But she never said that.

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But that's, that's what we

think the connection was.

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And so then she interviewed me.

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I actually was like number three or

four, because three or four other

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people had said yes and then backed out.

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And so then it was really easy

to consider, oh, you know what?

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I just won't take the job.

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You know, it just seems really hard.

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But I just kept saying, well,

if the, if the manager wants to

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interview me, I'll be available.

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If the position comes back open.

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So after that is how everybody

else, 'cause there was a lot

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of things that it required.

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And then I ended up not

doing most of those things.

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Uh, so I ended up, uh,

hanging in for the interview.

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And then in the, in the interview,

um, he asked me some questions that

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I think everybody else struggled

with and I answered very confidently,

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uh, because of the work that I

had done inside of your bootcamp.

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Avery Smith: That's,

that's awesome to hear.

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So, uh, what I was kind of hearing was.

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Basically you were, you were connected

to the, to a right person, someone

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kind of similar, one of your peers,

um, looking to inundate a job.

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Uh, and then you had a good

LinkedIn because the other thing

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is, uh, on, on LinkedIn, right?

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Like you don't get recommended if

you have kind of a crappy LinkedIn.

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So making sure your LinkedIn was

up to date with all the right

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keywords, all those projects you

had done inside the accelerator,

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I'm sure that helped, uh, as well.

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Yeah.

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You, you nailed the interview.

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Okay, that makes sense.

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So what advice would you give to someone.

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Who's listening right now who's like, wow,

I wanna be cool like Lilly and work for a

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cool company like Tesla in the data space.

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Like what advice would you give them?

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Lily BL: Um, I would recommend that they

kind of determine what part of the data

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portions they like to do, and then after

they figure out, oh, I like building

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the data structures, or the pipeline

or the visualizations, dive into that.

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I do get, uh, a lot of requests for

like, how can I kick off figuring

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out my data stuff and actually

recommend them to your free content?

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'cause I find it really helpful.

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I think you do a good job organizing.

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Okay.

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You gotta go get the data.

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Once you get the data,

you gotta clean the data.

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Well, once you clean the data, you gotta

figure out a quick way to deliver it.

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And also the visuals, how you

build your visuals is gonna kind of

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determine what you can say, um, as

the Bluff Fund, like right up front.

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I know these are things we do to build

the projects, but those directly translate

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into the interview and also into working

with, uh, people on site in the jobs.

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So if you can find a material that helps

you hone the skills you naturally want or

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like inside of your data, uh, career, it

will make it easier for you to get that

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and it will make you a natural to post it.

333

:

'cause you'll actually

be excited about it.

334

:

Like, oh, I had a hard time

learning this particular function

335

:

in Excel, but I nailed it.

336

:

Let me show you guys how I did it.

337

:

You'll naturally be like, oh, we

had overtime with sql, but I figured

338

:

this out and now I'm gonna post it.

339

:

And people actually do look at it.

340

:

They might not comment, they might

not like, but recruiters and also

341

:

other people, uh, interested in data

will come and look at your projects

342

:

because if you had an issue with

it, likely someone else did too.

343

:

So if you're constantly posting your

projects and how you solve the problem.

344

:

Uh, they will naturally gravitate to you.

345

:

And one thing I always stress

is to try to frame my projects

346

:

into a problem and a solution.

347

:

So the purpose of this project

was to address this specific

348

:

problem and here's the solution.

349

:

Maybe they won't care for looking

at the problem, but maybe they're

350

:

interested in just a solution.

351

:

But that's interesting.

352

:

They'll go back and look at the

problem and then read all of the work.

353

:

Avery Smith: Interesting.

354

:

So yeah, projects played a big

role for you, it sounds like, like

355

:

you really believe in, in doing

projects and then posting them on

356

:

places like LinkedIn to get noticed.

357

:

Lily BL: Yeah.

358

:

And in the interview for Tesla

specifically, uh, I think the

359

:

question that sealed the deal for

me was that, uh, the bus had asked

360

:

me what I would do in inside of sql.

361

:

So he asked me just the general

stuff, like, you know, how would

362

:

you get something to come up?

363

:

What would you call the tables?

364

:

And he goes, it was like

his, his secret question.

365

:

It was supposed to catch me off guard.

366

:

He says, what if there

isn't anything in there?

367

:

Like you asked for it and it doesn't

give, like there's nothing in there.

368

:

What seat we're going to do then.

369

:

And we, I had done a module, uh,

to the bootcamp, uh, that you have.

370

:

And I had picked the short data

set instead of the large data set.

371

:

And because I picked the short data

set, my results were different.

372

:

And in fact, missing.

373

:

Mm.

374

:

So I distinctly remember sitting

there for like, what, what happened?

375

:

Did I do it wrong?

376

:

Rewatching the video, redoing

the thing, and trying and trying

377

:

until I got very frustrated.

378

:

And then I realized, oh, I picked

a different data set than he did.

379

:

So our results are probably not the same.

380

:

They're likely missing from mine.

381

:

So then I manually went in and checked,

and that was exactly what happened.

382

:

So when this manager from Tesla asked me.

383

:

I knew exactly what

happened when that occurs.

384

:

And so I was like, you

get absolutely nothing.

385

:

It's the most frustrating

thing in the world.

386

:

Uh, but it's good because you

don't have to keep looking.

387

:

There's absolutely nothing there.

388

:

You, you're just gonna get a, and

because I was so confident about it,

389

:

having sat in the frustration, he

laughed and then was like, I, I think

390

:

that she will be able to figure out

whatever she doesn't know and what

391

:

she does know will benefit us anyways.

392

:

And I think that's what sealed the deal.

393

:

So as you're working through the

projects and honing your skills.

394

:

Think about what you experience.

395

:

'cause that's what's gonna make

you shine in the interviews.

396

:

Avery Smith: I, I love hearing that.

397

:

I love hearing that the experiences

you had inside the accelerator

398

:

program, uh, worked out well

for you in, in an interview.

399

:

And it's interesting because, uh, I

obviously try to design the accelerator

400

:

and we're constantly updating it so that

people have less and less problems, right?

401

:

Like, we wanna try to make it as easy

for people to learn data as possible.

402

:

But the silver lining is

when those problems happen.

403

:

It puts you in a real life

scenario 'cause you're gonna have

404

:

problems when you get on the job.

405

:

And figuring out how to solve

those, figuring out what's

406

:

going wrong, uh, is a skill.

407

:

It's kind of a hard skill to teach.

408

:

But it's a very valuable skill to have.

409

:

So, uh, I love hearing that,

you know, a lot of data skills.

410

:

I'm curious here what order you

learn them in, and if you have any

411

:

tips for anyone who is learning

these different data skills?

412

:

Because there's a lot, right?

413

:

There's Power bi, there's Tableau,

there's Excel, there's sql, there's

414

:

Python, there's R Like what order did

you learn those in, and what advice would

415

:

you give to someone else learning those?

416

:

Sure.

417

:

Lily BL: Uh, I think the order I

learned them in was first Excel

418

:

and the Microsoft Office Suite.

419

:

Uh, I actually was certified through

them, um, to use Word in Excel.

420

:

However, I didn't understand it

as much as I would over time.

421

:

So then with the Excel basic knowledge,

I was able to navigate most data

422

:

and then I realized everything's

trickling into information systems.

423

:

So when I realized that I went back to

school and I got a degree that focused

424

:

in information systems and there I was

introduced to, uh, data visualizations

425

:

where we used a variety of tools.

426

:

Uh, the one that stood out

the most to me was Tableau.

427

:

So from there I joined an apprenticeship

where they used that tool.

428

:

'cause it just was visually stunning.

429

:

The rest of the stuff could get

the things done, including Excel,

430

:

but they were kind of grainy.

431

:

But with Tableau.

432

:

You could just floor somebody

by just the visual alone.

433

:

You wouldn't have to say anything.

434

:

They'd just be looking at it for a while.

435

:

So I was like, I'm really

interested in that.

436

:

So I did that.

437

:

And while I was in that program,

we also covered, uh, Python,

438

:

uh, more basics and sql.

439

:

And, uh, we also did, uh,

presentations, um, of the findings.

440

:

After I had, uh, those things under my

belt, I discovered your bootcamps and

441

:

then went back to square one with Excel.

442

:

Was like, okay, this is how you use

Excel specifically for data analysis,

443

:

not the other stuff I was doing.

444

:

So it redefined, like, it really

sharpened what I knew how to do.

445

:

And from there, uh, I went back into sql.

446

:

A lot of the companies I worked for didn't

use SQL as intensively as I expected.

447

:

So I was more so, uh,

using Tableau frequently.

448

:

And then Power bi.

449

:

Uh, power bi, um, is

like a full stop shop.

450

:

For analytics because it allows

you to do the visual component.

451

:

But to do that you need to

be able to pull in data.

452

:

To pull in the data, you need

to understand like the, uh, the

453

:

stakeholder request, and then also how

to clean the data and it uses Excel.

454

:

So, um, the skills were the same

in all of the software you just

455

:

clicked in a different spot.

456

:

So throughout the software per uh.

457

:

Phases or processes.

458

:

What I was continuously

sharpening was what is the data

459

:

process independent of the tool.

460

:

So if I had to start all the way over,

the way that I would learn these in

461

:

is Excel, uh, power bi and then uh,

Tableau and last sql, unless it you

462

:

are company that you're targeting does,

is focused on sql, I would do Excel

463

:

and then sql because if you understand

what you're doing in Excel, like, um.

464

:

V lookup, a next lookup, an H lookup.

465

:

They're essentially joining data.

466

:

So if you know how to join the data in

Excel and you can articulate it, then

467

:

you can look at any other software.

468

:

Here's um, a sql, let me go

ahead and join data here.

469

:

This is how I do the

joins in this software.

470

:

Okay, now I have Tableau,

how do I do the joins here?

471

:

And you are specifically honing your

skill for joining data, which is like

472

:

the backbone for, uh, data analytics.

473

:

And then that will parlay you

into engineering if you want.

474

:

Uh, but I would go Excel first and

then whatever you learn in Excel,

475

:

mirror it in whatever software

you can get your hands on next.

476

:

I did have the cases sometimes

where I didn't have certain

477

:

software, so I've had to wing it.

478

:

Um, I did a lot of G docs and

the, all of the Gmail suite

479

:

documentation when for some time I

couldn't afford the office software.

480

:

So even if you can't get

the most premium thing.

481

:

Do what is affordable, but focus on

the skill you're trying to sharpen

482

:

and you'll be able to figure it out

even if you've never used it before.

483

:

I

484

:

Avery Smith: think that's a really cool,

uh, point there is like, you know, we use

485

:

different software at different times,

but really a lot of them do similar stuff.

486

:

They get data from places.

487

:

You clean data with them, you do some

sort of aggregations or analysis or

488

:

make some charts obviously, like SQL

doesn't really make a lot of charts.

489

:

Like a pivot table in Excel is pretty

much just like a group buy in sql.

490

:

Um, so there is a lot

of, uh, overlap there.

491

:

So that makes a lot of sense.

492

:

So Lily, when you were trying

to break into data, there's

493

:

obviously a lot of data roles.

494

:

Um, there's data analysts,

there's business analysts, there's

495

:

operations research, which is

what I used to do at ExxonMobil.

496

:

Um, and each one of those jobs,

uh, is kind of complicated.

497

:

They, they're all data analyst roles, but.

498

:

They have different domains,

they have different industries,

499

:

they have different focuses.

500

:

They may use different tools, they might

have different vocab and, and customers.

501

:

So one of the things I really love, um,

that, uh, you sent me was like this matrix

502

:

you made of a couple different, uh, data

analyst titles and what you'd be doing

503

:

slash what tools you'd be using based

off of how experienced you you were.

504

:

So tell me about this matrix you made.

505

:

Why did you make it and, you

know, what does it do for you?

506

:

Lily BL: So I wanted to share this,

uh, with you and, uh, potentially

507

:

to anybody trying to break into data

or further career in data, because

508

:

this is how I was able to do it.

509

:

Uh, pretty much when you start

at the beginning, you don't

510

:

have a bunch of experience.

511

:

Um, in my case, I just knew Excel,

but not specific to analytics.

512

:

So the way that you leverage, uh, the

tool I gave you is that you kind of.

513

:

Set up your goals by a five year plan.

514

:

And the reason why is because by the

fifth year of any profession, you're

515

:

considered a professional 'cause

you've been in it for five years, you

516

:

have enough working hours to do this.

517

:

At a professional level,

you're not guessing anymore.

518

:

You should know, uh,

concretely what you're doing.

519

:

So, uh, depending on what kind

of analytics you wanna do,

520

:

the matrix can kind of guide

you to where you would start.

521

:

Let's use me for an example.

522

:

I started with Excel and I

wanted to be a data analyst.

523

:

My first data rules were

not titled Under Data.

524

:

So what I did is that I said,

Hey boss, I know you want me to

525

:

take care of these appointments.

526

:

And it was clerical work, but it

was, uh, handling a lot of data.

527

:

So I said, Hey, you have an

opportunity here, uh, to figure out

528

:

why your patients are dwindling.

529

:

So I took it upon myself to

offer a project so that I

530

:

can gain the skills I needed.

531

:

So in that project I recovered about half

a million dollars, uh, of lost payments

532

:

because somebody clicked the wrong button.

533

:

And there I secured my Excel experience.

534

:

I secured, uh, the patients being

able to return the company, gaining

535

:

the money, uh, that had originally

been lost because, uh, I used Excel.

536

:

That's what I needed in order to begin

to say, Hey, I have six months work.

537

:

With Excel, I have a

year's worth with office.

538

:

Um, at the time it was very popular

to use the Microsoft Office Suite.

539

:

Uh, let's say you secure the

the time you need with Excel.

540

:

Now you can say, Hey, in Excel.

541

:

I've also executed Pivot charts

and VLOOKUPs so I can join data.

542

:

I'm ready to go onto the next thing.

543

:

Hey, boss.

544

:

Uh.

545

:

We have a lot of data in

a lot of different places.

546

:

We already are integrated with Microsoft,

so we can use Power BI to pulling

547

:

all the data sets into one location.

548

:

Uh, can I get some time to be able

to make that happen so that I can get

549

:

you some support with your recording

and then you start figuring that out?

550

:

You might, when you, when you do this,

you don't have necessarily somebody

551

:

coaching you, so you need to rely

on the bootcamps or the knowledge

552

:

you already have that gives you the

confidence that I can execute this.

553

:

If you can't execute in the

software you're reaching for, don't

554

:

nominate yourself to do the project.

555

:

In there, you do it 'cause you

already know you have, you know

556

:

how to use that software, but the

company's just not implementing it.

557

:

So then you would jump into Power bi.

558

:

Maybe not its most advanced things,

but just enough to get your feet wet

559

:

so that you can figure out, this is

how I use it, this is what I like.

560

:

Once you get in there,

you can be like, Hey boss.

561

:

Uh.

562

:

We're in here with the Power bi.

563

:

We have these simple reports, but we have

a lot of stuff inside of SQL as well.

564

:

I was wondering if you can get me access,

uh, to request permission to join them

565

:

into the Power bi and that way I can

access more data and goes from there.

566

:

Right.

567

:

Well, one of the visualizations in

Power BI is a table, so you can actually

568

:

organize and clean all of your data inside

of Power BI and then export that sheet.

569

:

Put it into something

stunning like tablet like.

570

:

It's hard because as you're working

on it, it's not inherently clear

571

:

what you're doing, but that's how

you use the document I sent you.

572

:

You look at the title you want,

what software or what knowledge do

573

:

I have now and what can I reach for

based on my hidden skills that I

574

:

can start to attribute to my career?

575

:

And that's why you slowly grow it.

576

:

Now, sometimes the companies will say,

no, we don't need any work in Power bi.

577

:

We just want it done in Excel.

578

:

For me that translated to, I need to

find another company because I really

579

:

wanna grow more skills, uh, to get to

the next level because I have five years

580

:

to make it to that professional status.

581

:

And if I hit five years and I don't

have all the things in my tool belt,

582

:

I gotta do more than five years.

583

:

That's what I used to

get into the next thing.

584

:

Um, also, if you don't wanna grow

your career, like you're happy with

585

:

what you're doing, don't volunteer

the projects or the software.

586

:

Um, hone on what you, or focus your

skills on honing what you already

587

:

know and that will make you sharper

and sharper with what you have.

588

:

Avery Smith: Well, I think that's one of

your skills is that you're really good at

589

:

figuring out how to link business to data.

590

:

Uh, and I think a lot of

business and operations people

591

:

kind of struggle with that.

592

:

Um, so it's really cool that you

were able to be like, Hey, I see this

593

:

business need, uh, here's how analytics

could help us, uh, in this case.

594

:

Um, and I think that, you know,

you've done that as well with building

595

:

dashboards for, for stakeholders that

aren't necessarily, uh, data experts.

596

:

Um, I guess how do you have like an eye

for where data can help these businesses

597

:

and how do you, uh, help these maybe

non-technical, non-data folks be excited

598

:

and interested and ready to, to help

with these data analytics projects?

599

:

Lily BL: Well, that's such a,

that's such a good question.

600

:

Um, because you kind of

have to actively listen.

601

:

So, uh, it's almost like

speaking another language.

602

:

Somebody can say, oh man, like in real,

a real life example, a boss that I had

603

:

said, oh, I just want this inside of

Excel, and I'll be happy if we could

604

:

just get it from where it is into Excel

so that I can analyze it, I'll be happy.

605

:

So I got it done.

606

:

After it was done, they were so

happy that they decided, I want,

607

:

I wish everything can go in there.

608

:

And I said, what?

609

:

What you want, sir?

610

:

Is a warehouse of data.

611

:

Mm.

612

:

So what he said is, I want everything

in there, or I want this in Excel.

613

:

But what they're asking for

is an accumulation of data.

614

:

They're asking for a pipeline.

615

:

If you understand the data portion of

that, you can translate the regular

616

:

English into what that looks like in data.

617

:

And that's how you can determine

I can fix that or I can give you

618

:

something to help you hit that goal.

619

:

Or you can determine, oh, you know

what, that's just outside of my reach.

620

:

'cause your Google and you have a bunch

of data, I can't handle that much stuff.

621

:

Like I need servers, I need a

bunch of other stuff, but these

622

:

portions I can handle for you.

623

:

And that's how you determine I can

do this versus I can't do that.

624

:

I should offer you this 'cause

I know I can execute that.

625

:

Avery Smith: Lily, that is awesome.

626

:

I think that is a superpower

that, that you have.

627

:

Thank you so much for giving a glimpse

into what your career was like, telling

628

:

us what it was like to work as a data

analyst at Tesla and give us some

629

:

good, uh, advice and feedback for.

630

:

Trying to learn these data skills and

trying to maneuver in our data careers.

631

:

Is it okay if we put your, uh, LinkedIn

in the show notes down below and if people

632

:

have questions they can reach out to you?

633

:

Sure.

634

:

Okay.

635

:

Awesome.

636

:

Lily, thank you so much

for coming on the podcast.

637

:

It's so good to have you

and, uh, good to catch up.

638

:

Lily BL: Thank you.

639

:

Likewise.

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Show artwork for Data Career Podcast: Helping You Land a Data Analyst Job FAST

About the Podcast

Data Career Podcast: Helping You Land a Data Analyst Job FAST
The Data Career Podcast: helping you break into data analytics, build your data career, and develop a personal brand

About your host

Profile picture for Avery Smith

Avery Smith

Avery Smith is the host of The Data Career Podcast & founder of Data Career Jumpstart, an online platform dedicated to helping individuals transition into and advance within the data analytics field. After studying chemical engineering in college, Avery pivoted his career into data, and later earned a Masters in Data Analytics from Georgia Tech. He’s worked as a data analyst, data engineer, and data scientist for companies like Vaporsens, ExxonMobil, Harley Davidson, MIT, and the Utah Jazz. Avery lives in the mountains of Utah where he enjoys running, skiing, & hiking with his wife, dog, and new born baby.