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

2

Luckily, one of my accelerator

students has Lily BL, and today I

3

had the chance to interview her.

4

I got to ask her what it was like to work

at such a unique, cool company, what it

5

actually took to get her there, and what

advice she'd give to those of you watching

6

who are interested in working at Tesla

or other really cool tech companies.

7

So let's go ahead and

get into the episode.

8

Lily, your career has taken you

to Tesla, pg and e, Intel, and

9

now the city of San Francisco.

10

But your first full-time

data job was at Tesla.

11

What was it like working

on data projects at Tesla?

12

Uh,

13

Lily BL: it was nerve wracking

and exhilarating at the same time.

14

I was not sure what to expect because

when I took the role on, it was blended

15

between a couple of different areas.

16

But as, uh, I worked more and

more like day after day, I could

17

see what their data needs were.

18

They had data in multiple systems for

multiple reasons, and it was just so

19

much in volume that they couldn't keep

track of how to look at it concisely.

20

They had to go through embedded

records to get an answer.

21

So I think when I first got fired on,

the word on the street was she's like an

22

admin assistant to the district manager.

23

If you have administrative stuff

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

24

Once the boss saw what I could do.

25

It completely changed, and I

was monitoring everything for

26

data flow to determine what

kind of visuals could be built.

27

The scary part was like, I don't

know, and the exhilarating part

28

was, but I can figure it out.

29

Avery Smith: That's awesome.

30

And I'm glad to hear that.

31

Uh, an employer like that was, you

know, first off recognized your talents,

32

but then second off was like, okay,

Lily, Lily can, uh, do this stuff.

33

Let's give her some, some more tasks.

34

Did you feel like what, what you were

doing, like was, was super cutting

35

edge or did you feel like it was

more like regular, regular tasks?

36

Like, um, did you feel like

challenged in what you were doing?

37

Lily BL: I did feel challenged in

what I was doing because it had a

38

lot of impact once it was completed.

39

The technology and the how to itself

surprisingly was very basic, so it was

40

continuously searching for the specific

thing that will fix this specific problem.

41

And then gathering all the solutions

to say, Hey, this is how you can

42

improve your data situation overall.

43

Avery Smith: That's interesting.

44

And, and correct me if I'm

wrong, uh, you know, Tesla is

45

obviously a, a large company.

46

I worked for ExxonMobil, a large

company at these large companies.

47

You hear this phrase, I'd never

really heard it before, this word.

48

Um, it's called disparate.

49

Uh, and basically, or siloed, I

think is the other thing that they

50

said a lot at Exxon that data is

siloed or that data is disparate.

51

Basically, I think you kind of said

something similar where, you know,

52

at these large companies there's a

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

53

systems, but the problem is this system

doesn't necessarily talk to this system

54

and this data is kind of stuck here.

55

And you know, this data, they only

enter it in an Excel, uh, database.

56

So it doesn't really like

integrate with anything else.

57

And so it sounds like your job was almost

like you were like data analyst glue.

58

To try to tie in all these different

data sets from this different systems.

59

Did I, did I get that wrong, or

is that kind of what you did?

60

Lily BL: Yeah, you're

nailing it on the head.

61

It was very interesting because

a large portion of the actual

62

engineering work was done inside

of a software called Jira, which is

63

meant for tracking, uh, the project.

64

That's the data that was needed

to be reviewed and the company's

65

decision was not to view it.

66

Outside of that, I would run validations

in Excel to make sure the numbers it gave

67

me were accurate to what I visualized,

and so I had to actually learn Jake to

68

be able to put together what I needed

and I was limited by the visualizations

69

preselected for project management.

70

At the end of it, it was super cool

because I kind of created a grid.

71

Um, it was like a large,

uh, standing rectangle.

72

When you looked at it up and down.

73

That was the information for the district.

74

Managers.

75

Managers.

76

So each team and all of their staff.

77

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.

80

When you looked at it horizontally,

those were all the KPIs my

81

district manager had requested.

82

So they were subject to some standards

and it was so cool because I didn't know

83

how to do that till I was done with it.

84

Um, and so then I was like,

yeah, this is what I wanted.

85

Uh, but I needed some help from

the management team because to

86

make sure the data had integrity,

I didn't set the conditions.

87

So I did push for them to tell

me, this is how this is defined.

88

This is the threshold

to, uh, determine this.

89

And once I had them deliver to me,

uh, some definitions, I used those

90

decisions to build out the, the

visual and they ended up loving it.

91

'cause I was able to color code

it and I assimilated it to red

92

light, green light, yellow light.

93

So if it's marked green,

don't worry about it.

94

You don't have to look.

95

If it's yellow, you kind of

need to keep an eye on it.

96

But if it's red, you need

to go in and investigate.

97

That was just relative to the

data produced by the teams.

98

Then there was the data produced

by the hardware and that

99

those were different systems.

100

So then in those systems, I took

that one to Excel and I was able to

101

create a chart that had a threshold.

102

So I had them, again, define

what the threshold was, and

103

let's say it was like 10.

104

Once you got 10 of these things,

the chart would go from being green.

105

To now being red 'cause

it crossed the threshold.

106

So the division manager could look

at all of these things at a glance

107

and be like, oh, red is where I need

to be, and figure out what happened.

108

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

was also, uh, building into Tableau, uh,

109

master portfolio so that the division

manager could just look in there at

110

all of the teams and all of the things

that were of interest to him because he

111

would take that information back to the

meetings with the rest of management.

112

They would decide what would come

next based on what was there.

113

So like if a lot of equipment was

failing, they would say, Hey, your

114

team is under producing because you

have 10 of these different kinds of

115

machines and you're only putting out

like half of the results we expected.

116

Or based on the analysis, half of

the machines were not available

117

for one reason or another.

118

So it's like, this is why our numbers

are lower than what's expected.

119

Per what is available.

120

Only half is available, which you

couldn't really tell any other

121

way is they were kind of, uh.

122

Digging constantly before we were

able to build the visuals, uh, to

123

determine, uh, what was really going on.

124

So it really facilitated

the manager to manage.

125

And he was actually a really good manager,

so he knew where the weak points were.

126

He just was not a data person or a data

analyst to be like, I need this, felt

127

like this, and like that to get this.

128

But he knew what he wanted, so it

was a perfect partnership because

129

I could build it and he could tell

me if it worked or didn't work.

130

Avery Smith: Lily, this is super cool.

131

Thanks for sharing all of this.

132

Um, I have so many places I, I want

to go based off what you just told me.

133

Uh, the first was, I had never

heard of J Quill, but I had

134

a chance to look it up here.

135

So that's, that's Jira Query

Language or Jira, I don't know

136

how to say that, but for those who

never heard of that, it's JIRA.

137

Um, and it is owned by

Atlassian, I'm pretty sure.

138

Um, and it is like a project management

software that a lot of, uh, software

139

companies use to develop software.

140

So you're, you were kind of

looking at project data, um,

141

which is, which is really neat.

142

Uh, and it sounds like you were working

like really close to these, you know,

143

kind of higher up stakeholders who,

you know, they need a bird's eye view

144

of what's going on in their business.

145

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

like a gut feeling of this is, you know,

146

maybe this part is struggling right

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

147

not exactly a hundred percent sure.

148

What it sounds like is you tied up a

bunch of loose ends and you know, this,

149

these disparate data sets, and you're

able to create a data visualization, uh,

150

that helps these managers see, you know,

maybe what's struggling in the business,

151

maybe what's doing good, um, what they

need to worry about and what they need

152

to maybe put their, their focus on.

153

Um, so it sounds like you were almost

giving them like supervision goggles to

154

like look into their business and like

actually see is everything, is everything

155

going the way it's supposed to go because.

156

You know, as someone who runs a

business, I obviously do not run a

157

business close to the scale of Tesla.

158

Like one division of Tesla, I'm sure is

a hundred times bigger than my business.

159

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

Maxwell helping me out with coaching.

160

I have Isaac Ania who's

helping with my community.

161

I have, uh, a podcast producer

and editor, and I don't know

162

what's going on half the time.

163

They're just awesome.

164

Uh, employees doing a great job,

but I do wanna be like, okay.

165

How do I get above the business and like

actually look down on it and see like,

166

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

167

And it sounds like you were able

to do a bunch of analysis to kind

168

of produce that for these managers.

169

Lily BL: Yeah.

170

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

hair away from completing, 'cause I was

171

missing one definition, uh, which I think,

uh, would have had a huge impact, is,

172

um, I worked on their hiring process.

173

So I would sit in on their

meetings and see how they went

174

through their hiring processes and

would sit in on the interviews.

175

And then I would also look at fubu.

176

They had already hired because of the

way they, uh, did bonuses there, the

177

managers had to divide a percentage of

bonus among all of the existing teammates.

178

So I was able, based on watching

the data flow, um, I was able to

179

determine what the standards were.

180

They already knew that they had tiers.

181

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

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

182

Then I was able to zero in on what

are the standards, uh, that this

183

person needs to complete or being

knowledgeable in, in order to ascend

184

to the following tier, which translates

to more money for the employee.

185

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

reviewed everything and I set it

186

up, but what was missing was the

metrics associated to each tier.

187

And so I left it alone

to not push a project.

188

Uh, and hack the pay be incorrect.

189

At the very end of the, uh, contract,

the HR published the standards

190

or the, the pay for each scale.

191

So that was the missing piece I needed.

192

But with that, it would've facilitated

all of the yearly reviews of the

193

management team to enable or to

determine very quickly, oh, this person

194

hit these projects and these projects

are labeled within this category.

195

So while they were working as

an engineer too, their work.

196

Function was actually an

engineer, four or five.

197

So they qualified for the bonus

and potentially a promotion.

198

Um, we were also very proactive there with

kind of working with the, um, employees

199

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

200

Um.

201

It's not affirmative, but it's being

more proactive about their findings

202

and stressing their good works.

203

So with so many people on the team, I

don't know every single thing you did,

204

so take the initiative and tell me,

Hey, I completed these multiple things.

205

That way it's fresh on my mind.

206

So I didn't get to the see there.

207

But after I would have completed

that project with the raids, my goal

208

would've been to work with the team

one-on-one and have them pitch me.

209

Their successes and then I

could categorize it for them.

210

Like, okay, what you said

falls into this or into that.

211

Do you agree or disagree?

212

And then teach them how to make the

argument for their good works better.

213

Um, it's delicate to do in business,

but it's like a negotiation.

214

So you actually need to

practice it in order to get it.

215

And this particular company was

open to that they wanted to hear.

216

So, uh, that was like the

icing on the cake for me.

217

We didn't get to finish it, but

it's something that would've

218

been proactive for everybody.

219

The company would've had a very,

well, a very articulate staff, which

220

is needed for problem resolution.

221

And then with the market as it was

constantly laying off and whatnot,

222

this employee would have had the

skills sharpened to then go right

223

into another position if they were

laid off and quickly get another role.

224

Avery Smith: Hmm.

225

Very cool.

226

Um, while you're at Tesla, what

tools did you use the most?

227

Lily BL: Um, I think I used Jira the

most and excel for the validation.

228

Um, I got heavier into the

administrative side of, of the

229

software because for Tableau and Jira

I was bringing in add-ins to make

230

them more functional for analytics.

231

Uh, so for companies, uh, you have to

connect the Tableau software to what,

232

wherever your data is in the company.

233

When you use Tableau as an

individual user, you just

234

connect it to your worksheet.

235

You can't connect it to something

else if you have it, but typically

236

you just use a worksheet.

237

So that was different.

238

And it was a full host

of security clearances.

239

Um, so I did a little bit of the

administrator stuff, but, uh, JIRA

240

and Excel round my validations.

241

Avery Smith: That's awesome.

242

I think that's true.

243

And, and you mentioned JQL.

244

Is that kind of like SQL or

how, how are those related?

245

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

the commands in sql, so that's why I

246

was able to learn it pretty quickly.

247

Uh, but then, um, some

things are specific.

248

It uses a lot of, uh, a lot

more keywords than you would

249

expect, and they're different.

250

Um, the software itself

does try to help you.

251

Like it lets you click on buttons

and produces the code for you.

252

Uh, to an extent, but then you

have to have modifications.

253

So I would allow the software to allow

me to click to build some of the stuff,

254

but then I would review it and determine,

oh, it still needs this functionality,

255

or this, or this other group of people.

256

And you would have to manually put that

into the existing code to make it function

257

Avery Smith: super neat.

258

So it's basically SQL for Jira, and

they try to make it a little bit easier

259

for you to actually write the code.

260

Um, okay.

261

I'm actually not sure the

answer to this question.

262

How did you get this job at Tesla?

263

I remember you messaging me when

you got the job offer and you're

264

like, Hey, these are the details.

265

What do you think?

266

Should I take this job or not?

267

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

to do with my accelerator students, but

268

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

269

This was a couple years ago now.

270

Um, how you ended up landing this job?

271

Lily BL: Yeah, I think it

was through networking.

272

Um, at the time I was an instructor

for co-op and I had a cohort that

273

I would teach in the evenings.

274

One of my cohort students actually

got hired by them about a month or

275

so before I helped them finalize his

work that they wanted him to see.

276

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

uh, contracted by a recruiter on LinkedIn.

277

I checked with him.

278

It ended up being the same

person that contracted him.

279

So I think what happened is that I

popped up for her in association to him.

280

But she never said that.

281

But that's, that's what we

think the connection was.

282

And so then she interviewed me.

283

I actually was like number three or

four, because three or four other

284

people had said yes and then backed out.

285

And so then it was really easy

to consider, oh, you know what?

286

I just won't take the job.

287

You know, it just seems really hard.

288

But I just kept saying, well,

if the, if the manager wants to

289

interview me, I'll be available.

290

If the position comes back open.

291

So after that is how everybody

else, 'cause there was a lot

292

of things that it required.

293

And then I ended up not

doing most of those things.

294

Uh, so I ended up, uh,

hanging in for the interview.

295

And then in the, in the interview,

um, he asked me some questions that

296

I think everybody else struggled

with and I answered very confidently,

297

uh, because of the work that I

had done inside of your bootcamp.

298

Avery Smith: That's,

that's awesome to hear.

299

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

300

Basically you were, you were connected

to the, to a right person, someone

301

kind of similar, one of your peers,

um, looking to inundate a job.

302

Uh, and then you had a good

LinkedIn because the other thing

303

is, uh, on, on LinkedIn, right?

304

Like you don't get recommended if

you have kind of a crappy LinkedIn.

305

So making sure your LinkedIn was

up to date with all the right

306

keywords, all those projects you

had done inside the accelerator,

307

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

308

Yeah.

309

You, you nailed the interview.

310

Okay, that makes sense.

311

So what advice would you give to someone.

312

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

I wanna be cool like Lilly and work for a

313

cool company like Tesla in the data space.

314

Like what advice would you give them?

315

Lily BL: Um, I would recommend that they

kind of determine what part of the data

316

portions they like to do, and then after

they figure out, oh, I like building

317

the data structures, or the pipeline

or the visualizations, dive into that.

318

I do get, uh, a lot of requests for

like, how can I kick off figuring

319

out my data stuff and actually

recommend them to your free content?

320

'cause I find it really helpful.

321

I think you do a good job organizing.

322

Okay.

323

You gotta go get the data.

324

Once you get the data,

you gotta clean the data.

325

Well, once you clean the data, you gotta

figure out a quick way to deliver it.

326

And also the visuals, how you

build your visuals is gonna kind of

327

determine what you can say, um, as

the Bluff Fund, like right up front.

328

I know these are things we do to build

the projects, but those directly translate

329

into the interview and also into working

with, uh, people on site in the jobs.

330

So if you can find a material that helps

you hone the skills you naturally want or

331

like inside of your data, uh, career, it

will make it easier for you to get that

332

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.