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

This is my Tesla, but I've never worked for Tesla. Luckily, one of my accelerator students has Lily BL, and today I had the chance to interview her. I got to ask her what it was like to work at such a unique, cool company, what it actually took to get her there, and what advice she'd give to those of you watching who are interested in working at Tesla or other really cool tech companies. So let's go ahead and get into the episode. Lily, your career has taken you to Tesla, pg and e, Intel, and now the city of San Francisco. But your first full-time data job was at Tesla. What was it like working on data projects at Tesla? Uh,

it was nerve wracking and exhilarating at the same time. I was not sure what to expect because when I took the role on, it was blended between a couple of different areas. But as, uh, I worked more and more like day after day, I could see what their data needs were. They had data in multiple systems for multiple reasons, and it was just so much in volume that they couldn't keep track of how to look at it concisely. They had to go through embedded records to get an answer. So I think when I first got fired on, the word on the street was she's like an admin assistant to the district manager. If you have administrative stuff you can't do, just give it to her. Once the boss saw what I could do. It completely changed, and I was monitoring everything for data flow to determine what kind of visuals could be built. The scary part was like, I don't know, and the exhilarating part was, but I can figure it out.

That's awesome. And I'm glad to hear that. Uh, an employer like that was, you know, first off recognized your talents, but then second off was like, okay, Lily, Lily can, uh, do this stuff. Let's give her some, some more tasks. Did you feel like what, what you were doing, like was, was super cutting edge or did you feel like it was more like regular, regular tasks? Like, um, did you feel like challenged in what you were doing?

I did feel challenged in what I was doing because it had a lot of impact once it was completed. The technology and the how to itself surprisingly was very basic, so it was continuously searching for the specific thing that will fix this specific problem. And then gathering all the solutions to say, Hey, this is how you can improve your data situation overall.

That's interesting. And, and correct me if I'm wrong, uh, you know, Tesla is obviously a, a large company. I worked for ExxonMobil, a large company at these large companies. You hear this phrase, I'd never really heard it before, this word. Um, it's called disparate. Uh, and basically, or siloed, I think is the other thing that they said a lot at Exxon that data is siloed or that data is disparate. Basically, I think you kind of said something similar where, you know, at these large companies there's a lot of data, um, and there's a lot of systems, but the problem is this system doesn't necessarily talk to this system and this data is kind of stuck here. And you know, this data, they only enter it in an Excel, uh, database. So it doesn't really like integrate with anything else. And so it sounds like your job was almost like you were like data analyst glue. To try to tie in all these different data sets from this different systems. Did I, did I get that wrong, or is that kind of what you did?

Yeah, you're nailing it on the head. It was very interesting because a large portion of the actual engineering work was done inside of a software called Jira, which is meant for tracking, uh, the project. That's the data that was needed to be reviewed and the company's decision was not to view it. Outside of that, I would run validations in Excel to make sure the numbers it gave me were accurate to what I visualized, and so I had to actually learn Jake to be able to put together what I needed and I was limited by the visualizations preselected for project management. At the end of it, it was super cool because I kind of created a grid. Um, it was like a large, uh, standing rectangle. When you looked at it up and down. That was the information for the district. Managers. Managers. So each team and all of their staff. You could look at what they did and when they did it, what was still pending Vertically. Each team had a, a call. When you looked at it horizontally, those were all the KPIs my district manager had requested. So they were subject to some standards and it was so cool because I didn't know how to do that till I was done with it. Um, and so then I was like, yeah, this is what I wanted. Uh, but I needed some help from the management team because to make sure the data had integrity, I didn't set the conditions. So I did push for them to tell me, this is how this is defined. This is the threshold to, uh, determine this. And once I had them deliver to me, uh, some definitions, I used those decisions to build out the, the visual and they ended up loving it. 'cause I was able to color code it and I assimilated it to red light, green light, yellow light. So if it's marked green, don't worry about it. You don't have to look. If it's yellow, you kind of need to keep an eye on it. But if it's red, you need to go in and investigate. That was just relative to the data produced by the teams. Then there was the data produced by the hardware and that those were different systems. So then in those systems, I took that one to Excel and I was able to create a chart that had a threshold. So I had them, again, define what the threshold was, and let's say it was like 10. Once you got 10 of these things, the chart would go from being green. To now being red 'cause it crossed the threshold. So the division manager could look at all of these things at a glance and be like, oh, red is where I need to be, and figure out what happened. Um, and then, uh, separate from that, I was also, uh, building into Tableau, uh, master portfolio so that the division manager could just look in there at all of the teams and all of the things that were of interest to him because he would take that information back to the meetings with the rest of management. They would decide what would come next based on what was there. So like if a lot of equipment was failing, they would say, Hey, your team is under producing because you have 10 of these different kinds of machines and you're only putting out like half of the results we expected. Or based on the analysis, half of the machines were not available for one reason or another. So it's like, this is why our numbers are lower than what's expected. Per what is available. Only half is available, which you couldn't really tell any other way is they were kind of, uh. Digging constantly before we were able to build the visuals, uh, to determine, uh, what was really going on. So it really facilitated the manager to manage. And he was actually a really good manager, so he knew where the weak points were. He just was not a data person or a data analyst to be like, I need this, felt like this, and like that to get this. But he knew what he wanted, so it was a perfect partnership because I could build it and he could tell me if it worked or didn't work.

Lily, this is super cool. Thanks for sharing all of this. Um, I have so many places I, I want to go based off what you just told me. Uh, the first was, I had never heard of J Quill, but I had a chance to look it up here. So that's, that's Jira Query Language or Jira, I don't know how to say that, but for those who never heard of that, it's JIRA. Um, and it is owned by Atlassian, I'm pretty sure. Um, and it is like a project management software that a lot of, uh, software companies use to develop software. So you're, you were kind of looking at project data, um, which is, which is really neat. Uh, and it sounds like you were working like really close to these, you know, kind of higher up stakeholders who, you know, they need a bird's eye view of what's going on in their business. It's, they kind of, like you said, have like a gut feeling of this is, you know, maybe this part is struggling right here and I have a feeling why, but I'm not exactly a hundred percent sure. What it sounds like is you tied up a bunch of loose ends and you know, this, these disparate data sets, and you're able to create a data visualization, uh, that helps these managers see, you know, maybe what's struggling in the business, maybe what's doing good, um, what they need to worry about and what they need to maybe put their, their focus on. Um, so it sounds like you were almost giving them like supervision goggles to like look into their business and like actually see is everything, is everything going the way it's supposed to go because. You know, as someone who runs a business, I obviously do not run a business close to the scale of Tesla. Like one division of Tesla, I'm sure is a hundred times bigger than my business. Uh, but even now I have, you know, Trevor Maxwell helping me out with coaching. I have Isaac Ania who's helping with my community. I have, uh, a podcast producer and editor, and I don't know what's going on half the time. They're just awesome. Uh, employees doing a great job, but I do wanna be like, okay. How do I get above the business and like actually look down on it and see like, okay, what's going well and what's not. And it sounds like you were able to do a bunch of analysis to kind of produce that for these managers.

Yeah. And uh, one project, uh, that I was at hair away from completing, 'cause I was missing one definition, uh, which I think, uh, would have had a huge impact, is, um, I worked on their hiring process. So I would sit in on their meetings and see how they went through their hiring processes and would sit in on the interviews. And then I would also look at fubu. They had already hired because of the way they, uh, did bonuses there, the managers had to divide a percentage of bonus among all of the existing teammates. So I was able, based on watching the data flow, um, I was able to determine what the standards were. They already knew that they had tiers. Like we have engineer 1, 2, 3, 4, 5, a lead, whatever, a, a manager. Then I was able to zero in on what are the standards, uh, that this person needs to complete or being knowledgeable in, in order to ascend to the following tier, which translates to more money for the employee. And so then, um, we got, I got, I reviewed everything and I set it up, but what was missing was the metrics associated to each tier. And so I left it alone to not push a project. Uh, and hack the pay be incorrect. At the very end of the, uh, contract, the HR published the standards or the, the pay for each scale. So that was the missing piece I needed. But with that, it would've facilitated all of the yearly reviews of the management team to enable or to determine very quickly, oh, this person hit these projects and these projects are labeled within this category. So while they were working as an engineer too, their work. Function was actually an engineer, four or five. So they qualified for the bonus and potentially a promotion. Um, we were also very proactive there with kind of working with the, um, employees being more, um, uh, how do you call it? Um. It's not affirmative, but it's being more proactive about their findings and stressing their good works. So with so many people on the team, I don't know every single thing you did, so take the initiative and tell me, Hey, I completed these multiple things. That way it's fresh on my mind. So I didn't get to the see there. But after I would have completed that project with the raids, my goal would've been to work with the team one-on-one and have them pitch me. Their successes and then I could categorize it for them. Like, okay, what you said falls into this or into that. Do you agree or disagree? And then teach them how to make the argument for their good works better. Um, it's delicate to do in business, but it's like a negotiation. So you actually need to practice it in order to get it. And this particular company was open to that they wanted to hear. So, uh, that was like the icing on the cake for me. We didn't get to finish it, but it's something that would've been proactive for everybody. The company would've had a very, well, a very articulate staff, which is needed for problem resolution. And then with the market as it was constantly laying off and whatnot, this employee would have had the skills sharpened to then go right into another position if they were laid off and quickly get another role.

Hmm. Very cool. Um, while you're at Tesla, what tools did you use the most?

Um, I think I used Jira the most and excel for the validation. Um, I got heavier into the administrative side of, of the software because for Tableau and Jira I was bringing in add-ins to make them more functional for analytics. Uh, so for companies, uh, you have to connect the Tableau software to what, wherever your data is in the company. When you use Tableau as an individual user, you just connect it to your worksheet. You can't connect it to something else if you have it, but typically you just use a worksheet. So that was different. And it was a full host of security clearances. Um, so I did a little bit of the administrator stuff, but, uh, JIRA and Excel round my validations.

That's awesome. I think that's true. And, and you mentioned JQL. Is that kind of like SQL or how, how are those related?

Yeah, so it's very similar to the commands in sql, so that's why I was able to learn it pretty quickly. Uh, but then, um, some things are specific. It uses a lot of, uh, a lot more keywords than you would expect, and they're different. Um, the software itself does try to help you. Like it lets you click on buttons and produces the code for you. Uh, to an extent, but then you have to have modifications. So I would allow the software to allow me to click to build some of the stuff, but then I would review it and determine, oh, it still needs this functionality, or this, or this other group of people. And you would have to manually put that into the existing code to make it function

super neat. So it's basically SQL for Jira, and they try to make it a little bit easier for you to actually write the code. Um, okay. I'm actually not sure the answer to this question. How did you get this job at Tesla? I remember you messaging me when you got the job offer and you're like, Hey, these are the details. What do you think? Should I take this job or not? You know, it's one of the things I try to do with my accelerator students, but I don't remember off the top of my head. This was a couple years ago now. Um, how you ended up landing this job?

Yeah, I think it was through networking. Um, at the time I was an instructor for co-op and I had a cohort that I would teach in the evenings. One of my cohort students actually got hired by them about a month or so before I helped them finalize his work that they wanted him to see. So then about a month or so later, I got, uh, contracted by a recruiter on LinkedIn. I checked with him. It ended up being the same person that contracted him. So I think what happened is that I popped up for her in association to him. But she never said that. But that's, that's what we think the connection was. And so then she interviewed me. I actually was like number three or four, because three or four other people had said yes and then backed out. And so then it was really easy to consider, oh, you know what? I just won't take the job. You know, it just seems really hard. But I just kept saying, well, if the, if the manager wants to interview me, I'll be available. If the position comes back open. So after that is how everybody else, 'cause there was a lot of things that it required. And then I ended up not doing most of those things. Uh, so I ended up, uh, hanging in for the interview. And then in the, in the interview, um, he asked me some questions that I think everybody else struggled with and I answered very confidently, uh, because of the work that I had done inside of your bootcamp.

That's, that's awesome to hear. So, uh, what I was kind of hearing was. Basically you were, you were connected to the, to a right person, someone kind of similar, one of your peers, um, looking to inundate a job. Uh, and then you had a good LinkedIn because the other thing is, uh, on, on LinkedIn, right? Like you don't get recommended if you have kind of a crappy LinkedIn. So making sure your LinkedIn was up to date with all the right keywords, all those projects you had done inside the accelerator, I'm sure that helped, uh, as well. Yeah. You, you nailed the interview. Okay, that makes sense. So what advice would you give to someone. Who's listening right now who's like, wow, I wanna be cool like Lilly and work for a cool company like Tesla in the data space. Like what advice would you give them?

Um, I would recommend that they kind of determine what part of the data portions they like to do, and then after they figure out, oh, I like building the data structures, or the pipeline or the visualizations, dive into that. I do get, uh, a lot of requests for like, how can I kick off figuring out my data stuff and actually recommend them to your free content? 'cause I find it really helpful. I think you do a good job organizing. Okay. You gotta go get the data. Once you get the data, you gotta clean the data. Well, once you clean the data, you gotta figure out a quick way to deliver it. And also the visuals, how you build your visuals is gonna kind of determine what you can say, um, as the Bluff Fund, like right up front. I know these are things we do to build the projects, but those directly translate into the interview and also into working with, uh, people on site in the jobs. So if you can find a material that helps you hone the skills you naturally want or like inside of your data, uh, career, it will make it easier for you to get that and it will make you a natural to post it. 'cause you'll actually be excited about it. Like, oh, I had a hard time learning this particular function in Excel, but I nailed it. Let me show you guys how I did it. You'll naturally be like, oh, we had overtime with sql, but I figured this out and now I'm gonna post it. And people actually do look at it. They might not comment, they might not like, but recruiters and also other people, uh, interested in data will come and look at your projects because if you had an issue with it, likely someone else did too. So if you're constantly posting your projects and how you solve the problem. Uh, they will naturally gravitate to you. And one thing I always stress is to try to frame my projects into a problem and a solution. So the purpose of this project was to address this specific problem and here's the solution. Maybe they won't care for looking at the problem, but maybe they're interested in just a solution. But that's interesting. They'll go back and look at the problem and then read all of the work.

Interesting. So yeah, projects played a big role for you, it sounds like, like you really believe in, in doing projects and then posting them on places like LinkedIn to get noticed.

Yeah. And in the interview for Tesla specifically, uh, I think the question that sealed the deal for me was that, uh, the bus had asked me what I would do in inside of sql. So he asked me just the general stuff, like, you know, how would you get something to come up? What would you call the tables? And he goes, it was like his, his secret question. It was supposed to catch me off guard. He says, what if there isn't anything in there? Like you asked for it and it doesn't give, like there's nothing in there. What seat we're going to do then. And we, I had done a module, uh, to the bootcamp, uh, that you have. And I had picked the short data set instead of the large data set. And because I picked the short data set, my results were different. And in fact, missing. Mm. So I distinctly remember sitting there for like, what, what happened? Did I do it wrong? Rewatching the video, redoing the thing, and trying and trying until I got very frustrated. And then I realized, oh, I picked a different data set than he did. So our results are probably not the same. They're likely missing from mine. So then I manually went in and checked, and that was exactly what happened. So when this manager from Tesla asked me. I knew exactly what happened when that occurs. And so I was like, you get absolutely nothing. It's the most frustrating thing in the world. Uh, but it's good because you don't have to keep looking. There's absolutely nothing there. You, you're just gonna get a, and because I was so confident about it, having sat in the frustration, he laughed and then was like, I, I think that she will be able to figure out whatever she doesn't know and what she does know will benefit us anyways. And I think that's what sealed the deal. So as you're working through the projects and honing your skills. Think about what you experience. 'cause that's what's gonna make you shine in the interviews.

I, I love hearing that. I love hearing that the experiences you had inside the accelerator program, uh, worked out well for you in, in an interview. And it's interesting because, uh, I obviously try to design the accelerator and we're constantly updating it so that people have less and less problems, right? Like, we wanna try to make it as easy for people to learn data as possible. But the silver lining is when those problems happen. It puts you in a real life scenario 'cause you're gonna have problems when you get on the job. And figuring out how to solve those, figuring out what's going wrong, uh, is a skill. It's kind of a hard skill to teach. But it's a very valuable skill to have. So, uh, I love hearing that, you know, a lot of data skills. I'm curious here what order you learn them in, and if you have any tips for anyone who is learning these different data skills? Because there's a lot, right? There's Power bi, there's Tableau, there's Excel, there's sql, there's Python, there's R Like what order did you learn those in, and what advice would you give to someone else learning those? Sure.

Uh, I think the order I learned them in was first Excel and the Microsoft Office Suite. Uh, I actually was certified through them, um, to use Word in Excel. However, I didn't understand it as much as I would over time. So then with the Excel basic knowledge, I was able to navigate most data and then I realized everything's trickling into information systems. So when I realized that I went back to school and I got a degree that focused in information systems and there I was introduced to, uh, data visualizations where we used a variety of tools. Uh, the one that stood out the most to me was Tableau. So from there I joined an apprenticeship where they used that tool. 'cause it just was visually stunning. The rest of the stuff could get the things done, including Excel, but they were kind of grainy. But with Tableau. You could just floor somebody by just the visual alone. You wouldn't have to say anything. They'd just be looking at it for a while. So I was like, I'm really interested in that. So I did that. And while I was in that program, we also covered, uh, Python, uh, more basics and sql. And, uh, we also did, uh, presentations, um, of the findings. After I had, uh, those things under my belt, I discovered your bootcamps and then went back to square one with Excel. Was like, okay, this is how you use Excel specifically for data analysis, not the other stuff I was doing. So it redefined, like, it really sharpened what I knew how to do. And from there, uh, I went back into sql. A lot of the companies I worked for didn't use SQL as intensively as I expected. So I was more so, uh, using Tableau frequently. And then Power bi. Uh, power bi, um, is like a full stop shop. For analytics because it allows you to do the visual component. But to do that you need to be able to pull in data. To pull in the data, you need to understand like the, uh, the stakeholder request, and then also how to clean the data and it uses Excel. So, um, the skills were the same in all of the software you just clicked in a different spot. So throughout the software per uh. Phases or processes. What I was continuously sharpening was what is the data process independent of the tool. So if I had to start all the way over, the way that I would learn these in is Excel, uh, power bi and then uh, Tableau and last sql, unless it you are company that you're targeting does, is focused on sql, I would do Excel and then sql because if you understand what you're doing in Excel, like, um. V lookup, a next lookup, an H lookup. They're essentially joining data. So if you know how to join the data in Excel and you can articulate it, then you can look at any other software. Here's um, a sql, let me go ahead and join data here. This is how I do the joins in this software. Okay, now I have Tableau, how do I do the joins here? And you are specifically honing your skill for joining data, which is like the backbone for, uh, data analytics. And then that will parlay you into engineering if you want. Uh, but I would go Excel first and then whatever you learn in Excel, mirror it in whatever software you can get your hands on next. I did have the cases sometimes where I didn't have certain software, so I've had to wing it. Um, I did a lot of G docs and the, all of the Gmail suite documentation when for some time I couldn't afford the office software. So even if you can't get the most premium thing. Do what is affordable, but focus on the skill you're trying to sharpen and you'll be able to figure it out even if you've never used it before. I

think that's a really cool, uh, point there is like, you know, we use different software at different times, but really a lot of them do similar stuff. They get data from places. You clean data with them, you do some sort of aggregations or analysis or make some charts obviously, like SQL doesn't really make a lot of charts. Like a pivot table in Excel is pretty much just like a group buy in sql. Um, so there is a lot of, uh, overlap there. So that makes a lot of sense. So Lily, when you were trying to break into data, there's obviously a lot of data roles. Um, there's data analysts, there's business analysts, there's operations research, which is what I used to do at ExxonMobil. Um, and each one of those jobs, uh, is kind of complicated. They, they're all data analyst roles, but. They have different domains, they have different industries, they have different focuses. They may use different tools, they might have different vocab and, and customers. So one of the things I really love, um, that, uh, you sent me was like this matrix you made of a couple different, uh, data analyst titles and what you'd be doing slash what tools you'd be using based off of how experienced you you were. So tell me about this matrix you made. Why did you make it and, you know, what does it do for you?

So I wanted to share this, uh, with you and, uh, potentially to anybody trying to break into data or further career in data, because this is how I was able to do it. Uh, pretty much when you start at the beginning, you don't have a bunch of experience. Um, in my case, I just knew Excel, but not specific to analytics. So the way that you leverage, uh, the tool I gave you is that you kind of. Set up your goals by a five year plan. And the reason why is because by the fifth year of any profession, you're considered a professional 'cause you've been in it for five years, you have enough working hours to do this. At a professional level, you're not guessing anymore. You should know, uh, concretely what you're doing. So, uh, depending on what kind of analytics you wanna do, the matrix can kind of guide you to where you would start. Let's use me for an example. I started with Excel and I wanted to be a data analyst. My first data rules were not titled Under Data. So what I did is that I said, Hey boss, I know you want me to take care of these appointments. And it was clerical work, but it was, uh, handling a lot of data. So I said, Hey, you have an opportunity here, uh, to figure out why your patients are dwindling. So I took it upon myself to offer a project so that I can gain the skills I needed. So in that project I recovered about half a million dollars, uh, of lost payments because somebody clicked the wrong button. And there I secured my Excel experience. I secured, uh, the patients being able to return the company, gaining the money, uh, that had originally been lost because, uh, I used Excel. That's what I needed in order to begin to say, Hey, I have six months work. With Excel, I have a year's worth with office. Um, at the time it was very popular to use the Microsoft Office Suite. Uh, let's say you secure the the time you need with Excel. Now you can say, Hey, in Excel. I've also executed Pivot charts and VLOOKUPs so I can join data. I'm ready to go onto the next thing. Hey, boss. Uh. We have a lot of data in a lot of different places. We already are integrated with Microsoft, so we can use Power BI to pulling all the data sets into one location. Uh, can I get some time to be able to make that happen so that I can get you some support with your recording and then you start figuring that out? You might, when you, when you do this, you don't have necessarily somebody coaching you, so you need to rely on the bootcamps or the knowledge you already have that gives you the confidence that I can execute this. If you can't execute in the software you're reaching for, don't nominate yourself to do the project. In there, you do it 'cause you already know you have, you know how to use that software, but the company's just not implementing it. So then you would jump into Power bi. Maybe not its most advanced things, but just enough to get your feet wet so that you can figure out, this is how I use it, this is what I like. Once you get in there, you can be like, Hey boss. Uh. We're in here with the Power bi. We have these simple reports, but we have a lot of stuff inside of SQL as well. I was wondering if you can get me access, uh, to request permission to join them into the Power bi and that way I can access more data and goes from there. Right. Well, one of the visualizations in Power BI is a table, so you can actually organize and clean all of your data inside of Power BI and then export that sheet. Put it into something stunning like tablet like. It's hard because as you're working on it, it's not inherently clear what you're doing, but that's how you use the document I sent you. You look at the title you want, what software or what knowledge do I have now and what can I reach for based on my hidden skills that I can start to attribute to my career? And that's why you slowly grow it. Now, sometimes the companies will say, no, we don't need any work in Power bi. We just want it done in Excel. For me that translated to, I need to find another company because I really wanna grow more skills, uh, to get to the next level because I have five years to make it to that professional status. And if I hit five years and I don't have all the things in my tool belt, I gotta do more than five years. That's what I used to get into the next thing. Um, also, if you don't wanna grow your career, like you're happy with what you're doing, don't volunteer the projects or the software. Um, hone on what you, or focus your skills on honing what you already know and that will make you sharper and sharper with what you have.

Well, I think that's one of your skills is that you're really good at figuring out how to link business to data. Uh, and I think a lot of business and operations people kind of struggle with that. Um, so it's really cool that you were able to be like, Hey, I see this business need, uh, here's how analytics could help us, uh, in this case. Um, and I think that, you know, you've done that as well with building dashboards for, for stakeholders that aren't necessarily, uh, data experts. Um, I guess how do you have like an eye for where data can help these businesses and how do you, uh, help these maybe non-technical, non-data folks be excited and interested and ready to, to help with these data analytics projects?

Well, that's such a, that's such a good question. Um, because you kind of have to actively listen. So, uh, it's almost like speaking another language. Somebody can say, oh man, like in real, a real life example, a boss that I had said, oh, I just want this inside of Excel, and I'll be happy if we could just get it from where it is into Excel so that I can analyze it, I'll be happy. So I got it done. After it was done, they were so happy that they decided, I want, I wish everything can go in there. And I said, what? What you want, sir? Is a warehouse of data. Mm. So what he said is, I want everything in there, or I want this in Excel. But what they're asking for is an accumulation of data. They're asking for a pipeline. If you understand the data portion of that, you can translate the regular English into what that looks like in data. And that's how you can determine I can fix that or I can give you something to help you hit that goal. Or you can determine, oh, you know what, that's just outside of my reach. 'cause your Google and you have a bunch of data, I can't handle that much stuff. Like I need servers, I need a bunch of other stuff, but these portions I can handle for you. And that's how you determine I can do this versus I can't do that. I should offer you this 'cause I know I can execute that.

Lily, that is awesome. I think that is a superpower that, that you have. Thank you so much for giving a glimpse into what your career was like, telling us what it was like to work as a data analyst at Tesla and give us some good, uh, advice and feedback for. Trying to learn these data skills and trying to maneuver in our data careers. Is it okay if we put your, uh, LinkedIn in the show notes down below and if people have questions they can reach out to you? Sure. Okay. Awesome. Lily, thank you so much for coming on the podcast. It's so good to have you and, uh, good to catch up.

Thank you. Likewise.