About

“Julia, a data engineer from Atlanta, discusses her role in helping businesses harness the power of data. She emphasizes the importance of mentorship programs, particularly as gen AI reshapes data analysis. Julia's work with Girls Who Code and her initiative, Peach Core Data, aim to make coding and data concepts accessible to everyone.”

Ben (00:02):

Okay, awesome. So Julia, thank you for showing up for the SnoQap Spotlight. SnoQap is an undergraduate nonprofit that helps students think through writing, through creative writing on stem, politics, history, any subjects that they're interested in. What we do is we offer students who are currently in college a platform for them to write and then have it peer reviewed, edited, and then published on our site to give them a platform. So today on SnoQap Spotlight, we have Julia, who is a graduate from Georgia Tech, and I believe she's still based in Atlanta, Georgia, and she works in data analysis at different Fortune 500 companies. Did I get that? Did that pretty good?

Julia (01:08):

Yep. You got that. Good. I'm happy to be here, Ben. And yep, I'm based in the Atlanta area. I graduated from Georgia Tech with a bachelor's degree in industrial and systems engineering, and so I learned about how to make systems and processes work more efficiently. And since then I've gone, I've worked in data analytics, data science, and now I'm currently in a data engineering role with a Fortune 500 company.

Ben (01:33):

Cool. So data analysis and data analytics. So I'm not too into that world, but that means that you're pretty much working on the backend of logistics systems, right? You said data analysis and the processes, so you're kind of working on logistics side of things.

Julia (02:04):

Yeah, so I mean it all depends on the problem. So if you think of the classic industrial engineering problems, it's usually focused on some kind of manufacturing facility or distribution center. Then there's logistics problems you can answer also, how many trucks should we be sending and how fast are they going to be arriving, those kinds of things. So when I was going to school, there was all of these high level concepts, but when I really got into working, there was a big need for me to build my data analysis skills and to be able to use coding so that I could answer these big questions because there's tons of data out there, but if you aren't utilizing it or if it's not clean data, then you're not going to be able to answer these big questions. And so my first job out of school I was working in data science and analytics, and while I did have a strong technical base for my schooling, I knew all about databases and I'd had exposure to Python and Java.

(03:02):

It was really when I started working that I learned so much about coding and I really got to build my coding confidence and it's just been able to help me be really valuable for my companies. And when I was doing more data science and data analytics, that was more so answering the questions and then delivering the findings to the business. But I've moved more into a data engineering role because that's a lot more on the backend. So that's making the data pipelines so that data science scientists and data analysts can answer those questions. So I've really enjoyed moving into more of a supporting type role so that I can help other people be able to answer these big questions. So that's been a fun career transition.

Ben (03:50):

So when you started college, did you pursue this route right from the beginning or is this something that maybe you're kind going for another education path before and then you kind of stumbled into this? Or were you just always drawn to this side of things?

Julia (04:12):

I mean, I was always a nerd and I always liked math and different numbers, and so I knew I wanted to do engineering when I went into school, but it took me a little while to find industrial engineering because there's lots of different kinds. There's chemical engineers, there's mechanical engineers, and I would describe it as all engineers are just solving a different problem. Industrial engineers, they're typically making systems and processes work better, so it's more so designing a system that's going to work better. And that could be manufacturing, it could be distribution, it really depends. So a lot of different industrial engineers will go into consulting roles. And so I did take a bit of a different path going more into the data science, data analytics, data engineering route, but that's kind of just where my interests have drawn me and what I've enjoyed doing. But no, I did not know that I wanted to be an industrial engineer or even a data engineer when I started off at school. I just kind of took it one step at a time and I really loved my statistics classes and I think that was what started leaning me towards that direction.

Ben (05:18):

Cool. So it sounds like, okay, so you said you're on the nerd side, you're kind of more data heavy, more numbers heavy, and then it said that you kind of took a different route because a lot of people kind of go consulting. Would consulting also be data heavy or could that be different routes? All of the above. It could be numbers heavy, it could just be more processes. SOP docs. What makes you think that you took a different route?

Julia (05:55):

A lot of consultants, they have a lot of great knowledge, but they may not necessarily have highly technical skills where they're really going in and building different code bases or building different types of supporting infrastructure. And so I think they're more so giving advice and looking at the numbers. But I wanted to go more deep into making things happen, figuring out how to get data accessible, is this good data, and then getting the insights out of that. I think rather than going consulting, which would be a bit more of a high level type path, I decided to go more technical. So a lot of people will go to something, they'll get a degree more IT based to work in data engineering, but a lot of people will pursue a master's in data science and analytics so that they can gain new skills.

Ben (06:51):

Cool. So then you're in it, you're using the data to create different, I don't know, product offerings or it could be systems you're focused on doing things with the data where you would say the more common route could be mainly just taking the data insights, but you're kind of cultivating those data insights with the data.

Julia (07:19):

Exactly, and it's a large effort to harness the data and make it actually usable. So getting in there and making that happen is super valuable for whatever business I'm working for. And then it's really rewarding for myself when I can see something go from nothing to completion. So that's always been a very enjoyable part of my job.

Ben (07:42):

Yeah, interesting that you're more in the side of, I don't know, it sounds more active with the data of you finding insights with the data, putting certain things together. It seems very hands-on with what you're doing with the different data. I am curious if you've seen since you've started, have you seen a big difference as far as, because I'm sure you learned all these things in school and then it's like these textbooks, they may be kind of outdated as is, but then I am curious about your experience of after you graduated then gen AI came into play. So it's like how has that kind of shifted what you do on the day-to-day or has it?

Julia (08:36):

Yeah, that's a really good question. So I mean, I will say Gen AI chat, g pt, that wasn't a thing when I was in school. We could use Google and even now on Google and stuff all of the time just to see how have other people solved a problem. There's no need for me to reinvent the wheel, and I think people should always use all of the resources that are available to them. And I think Gen AI and GitHub copilot, all of that is a great tool for going ahead and getting the base ready. So I see it really expediting the development process. You'll still need experts in there to actually review the code and see what it's doing and figure out how to make it better. But anything that can make the development process quicker is a valuable tool, and that's how I see Gen AI being used in coding and development work.

Ben (09:28):

Do you think gen AI and then chat GPT has this kind of amplified how important it is for businesses to have, I don't know, somebody on their team to be looking at the data and then also putting it together? It seems like you said there's so much data out there, it's just how you use it that really matters. And I feel like Gen AI has made data so much more important for these businesses to where it's like, I don't know, maybe data used to just be something like a book on the top shelf of some office somewhere, but I feel like more so that data is becoming day to day within business operations. Would you agree with that? Yeah,

Julia (10:15):

Yeah, yeah, I would agree with that. I mean, if you even think about how these machine learning models were working, they're on a constant feedback loop where they're taking the data that you give to them and they're using that to improve themselves. So even thinking about data from that perspective and how we're using these tools and what we're getting out of them is something that we need to consider because we can't just expect that they're going to be working perfectly all of the time. And there's always going to be understanding the limitations of the tool and how it's working can help us get clarity on the results coming from it and figure out if that's something we can be competent in or what pieces do we need to look at and what do our experts need to have their eyes on so we can see if this will be better.

Ben (10:59):

Cool. Just one more broad question about your work and then I want to jump into some of the things you've done just as far as peer mentorship, but what is one business case or business project that you worked on very high level, doesn't need to be too deep, but what's one thing that I guess you're kind of able to fix or assist with a business with data?

Julia (11:30):

So I think one interesting project that I used to work on, so I used to do a lot of work with simulations and basically you can think of a simulation as, it's not a video game, but you can think of it, a small video game, a virtual environment that's going to recreate what we're seeing in reality. So I was doing a lot of simulation work with distribution facilities and trucks would be coming in, package would be sorted, and then they would go somewhere else. So there was a really strong need to understand what if scenarios. So if something breaks or something goes down, what kind of effect does that have on us? What is the cost of that? So that way we could figure out how we can mitigate those different kinds of risks. With simulation, you're able to run a scenario a hundred, a thousand a million times. That way you're able to get some confidence on what actions you're going to do. Whereas if you were to conduct a test in real life, you're spending every second on a test is an actual second. In a virtual environment, you're able to run it so much faster and you're able to do it a million times. It's literally impossible to conduct that in reality. So using simulation, I was able to give us confidence on what kind of decisions we should take in extreme scenarios.

Ben (12:51):

That's cool. So it's kind of like a fast forward button for any test project.

Julia (12:56):

Yes.

Ben (12:59):

So I know we kind of discussed a little about how things have changed since you're in school and then how you've kind had to adapt with the gen AI and then these new advancements in tech. Just since you've been working, how are you helping undergrads and then students stay up to date? Are you doing any mentorship or things like that?

Julia (13:25):

Yeah, yeah. I am doing some mentorship because one of the things I really found when I was starting out my career and even now is reaching out to other people is so great because when I started in my career, even though I had that strong technical base, it was very different going from a perfect classroom setting where the problem is presented to you to real life situations where there may not even be a clear problem statement, you may not know what tools you need to use and it's a lot more to pull together. So having coworkers that they didn't know the answer either, but they were pretty much like, I'll help you figure this out. That was so valuable for me. And so I've wanted to give that back to other women. And so I do mentor other women who reach out to me and basically I'm just a rubber ducky that they can talk to and I encourage them that they can figure this out and if they have any problems they need to talk through with me, I'm happy to do that. I think I've got a really strong general understanding of enough concepts to where I can suggest different tools or different strategies that may work for them, but overall it's just encouraging them that they can do this and they are capable and they are not alone. It's such a valuable thing. So I do like to offer that to other young women where I can.

Ben (14:46):

Yeah, I saw you did an event with Girls Who Code. Tell me a little about that.

Julia (14:54):

Yes, that was an awesome volunteer event. So that was a volunteer event that I had put on and I think we partnered with lots of women who were the mentor volunteers, and then lots of young women, I want to say it was middle school age was the average age of the attendees, but we used this tool called Ear Sketch and the whole idea is that you're learning to code through music. So they would use the tool called Ear Sketch and basically they would be building songs, but they would have a whole text editor on the platform where they would actually be coding. It wasn't drag and drop, it wasn't too simplified. They were able to get true exposure that I think was really, really valuable for them to demystify what coding is and kind of see that this is cool, there are different things you can do with it and understand how it's working.

(15:50):

So that was a really enjoyable event and I think I really got to see all of the volunteers. They had to go through the training as well. So they were women who maybe had never coded before, but they learned this platform and then they were working with the young girls to show it to them and the tool was actually built by Georgia Tech graduates and they had a great tutorial, so there was no downloading of software. It was all right there and you would just click through it. So it was really easy for people to jump into and you're making music, so that was fun. You have to have a nice end goal to keep it engaging. And so that was a great time.

Ben (16:35):

So you said, I do want to make note of that. You said it was Ear Sketch was the program?

Julia (16:42):

Yes, ear Sketch was the tool that we were using

Ben (16:44):

And that's available for if any of the readers want to go check it out, they can just go online.

Julia (16:51):

They Google, just Google Ear Sketch, maybe Georgia Tech and you would go ahead and find it. And then they have it in Python and Java and we just did the hour of code activity so in one hour you can go from nothing to coding a song.

Ben (17:07):

That's awesome. So on that event you said it was all women, just young girls, and then you also had moderators or volunteers who were also women who would kind of help team lead the groups. I think it's cool that they didn't have any coding knowledge, some of them. So some of the volunteers who were supposed to teach these girls how to code and lead the groups, was that intimidating for them or how did that work?

Julia (17:41):

It was, but women are always so supportive and amazing. So we basically just had training sessions. I had open office hours and the months leading up, and so I encouraged them to attend at least one, but as many as they needed to feel comfortable. And we would just go through the program we would be doing with the girls at the actual event and if they had any questions they would ask those there. And there's always error messages in code. So I'd go through some of the error messages, but overall it was such a great platform that there weren't too many issues and they were able to ask any questions they had so they were able to be confident as moderators.

Ben (18:20):

That's awesome. What about how were the young girls receptive? You said it was middle school age was generally the average. How did they respond to it?

Julia (18:31):

They had a great time. It was so fun. I think the youngest girls were maybe first or second grade, and then the oldest ones were in high school. So I mean it was all ages can get something out of it, but they really enjoyed putting in the different sound clips. And once you get it and you start moving, they are so quick at it and they can pick it up and they would start changing out different variables to put the sound clips in different places or to select different sound clips that they wanted. And they had a huge library of different artists in there, so they had a good time.

Ben (19:03):

And this was all virtual, right?

Julia (19:06):

Yep, this was a full virtual event.

Ben (19:08):

Cool. How many people participated? Volunteers and attendees?

Julia (19:14):

I want to say the total was about 60 people.

Ben (19:17):

Oh wow.

Julia (19:19):

Yeah.

Ben (19:20):

That's attendees and volunteers who were kind of helping guide the girls?

Julia (19:25):

Yeah, I think so. I think it was about 60 total. That would be volunteers and attendees. We did want to have a high amount of the moderators so that way the girls would feel like they were supported when we went into the smaller breakout rooms. And because it's easy to get lost in a pool of a large zoom call, so we had small breakout rooms so that way everyone would feel supported. And then me and some other people who were a bit more technical were hopping around into the breakout room. So that way if there was anything we would be there to help.

Ben (19:54):

Very cool. That's awesome that it was such a big event. It was virtual, it was all volunteer.

Julia (20:00):

It was nice virtual as well because we had some girls who were even up in Tennessee, so they were able to attend. So I liked that it wasn't restricted to a specific location. It wasn't any kind of transportation issues or logistics. It was whoever was available was just able to hop on and given hour of their time to learn some cool skills.

Ben (20:22):

That's awesome. Last question on that is how long did you prep for that? I know you said you had open door sessions to train the volunteers, and how long was that whole process?

Julia (20:36):

We probably spent from talking about it to the event, it was probably at least six months to get the whole thing going. And obviously it did ramp up the three months going up into the event. I think that's when we started doing the actual training sessions. And prior to that it was mostly planning and figuring out what activities are we going to do because it just started as a general idea of, hey girls, you code, we want to partner with this organization and do a cool activity. What are we going to do? So a big part of it was finding the tool, finding the activity, and figuring out how we were going to make that happen.

Ben (21:11):

That's awesome. Well that definitely sounds fun. I like that you put the whole event on. I like that it was all women based. I like that you taught everyone in the room something new. It sounds like everybody kind of left away with something, both the volunteers and the attendees. So I love that. Are there any kind of future looking initiatives that you have or is there any place that the readers can kind of keep in touch with you or what's next?

Julia (21:44):

Yeah, so it's since early stages, but I've started a group called Peach Core Data, and basically it's just going to be all things data from data analytics, data science up to data engineering. And the goal of this group is to help demystify different coding and dating concept or data concepts and bring it to the population. So if that's where you can find me, that's where I'm going to be active.

Ben (22:07):

That's awesome. Well, Julia, thank you again. I loved learning a little about your background and then hearing about some of the ways that you're giving back and I'm excited to see how Peach Core goes and yeah, thank you so much.

Julia (22:23):

Yeah, thank you for your time, Ben. It was a pleasure.

Ben (22:25):

Cool. See you. Bye.

Julia (22:27):

Bye.