Transitioning from Data Engineering to Data Science in Big Tech (Sundas Khalid) - KNN Ep. 91
Updated: Mar 29
Today I had the pleasure of interviewing @Sundas Khalid . Be sure to check out her YouTube Channel! Sundas is a data science leader and a passionate advocate for diversity in the workplace. Sundas currently works at Google as Sr. Analytical Lead, leading Google Search Engine analytics. Prior to Google, Sundas was at Amazon where she received awards for her work as a data science leader driving large-scale experimentations and data science initiatives. Sundas is the first woman in her family to graduate college, her journey is one of persistence and resilience, and has been featured on Forbes and Google's Data Analytics Professional Certificate on Coursera.
As the first female in her family to graduate university, she is an advocate of women's education and workforce diversity, and actively participate in diversity, equity & inclusion within and outside of Google. In her free time, she enjoys public speaking and creating educational content on various platforms on data science, career and financial literacy. In 2021, Sundas help women of color negotiate $1.4M in job offers. In this interview we dive into how Sundas made such an incredible career transition from taking some time off through data engineering and eventually into data science.
[00:00:00] Sundas: I think, like, a sign of a bad manager is like, when you, all you do is like, you spend your time in your one-on-one on like project updates. Like that's not a good use of your one-on-one like use your one-on-one to develop your career. So I think it's very, very important to have a good manager.
And if you find a good manager, like follow them, like if I found like I would follow them, but he is still at Amazon. So I wanted to leave Amazon, but like I would, if you have found a good manager, like just follow your manager, because a lot of your work quality and your experience at work would be influenced by who your manager is.
[00:00:43] Ken: Today, I had the pleasure of interviewing Sundas Khalid. Sundas is a data science leader and a passionate advocate for diversity in the workspace. Some does currently works at Google as a Senior Analytical Lead, leading a Google Search Engine analytics. Prior to Google, Sundas was at Amazon where she received awards for her work as a data science leader, driving large scale experimentation and data science initiatives.
Sundas is the first woman in her family to graduate college. Her journey is one of persistence, resilience and has been featured on Forbes and Google Data Analytics professional certificate on Coursera. As the first female in her family to graduate university, she's an advocate of women's education in the workforce and diversity and actively participates in diversity, equity and inclusion within and outside of Google. In her free time, she enjoys public speaking and creating educational content on various platforms on data science, career and financial literacy.
In 2021, Sundas helped women of color negotiate over $1.4 million in job offers. In this interview, we dive into how Sundas made such an incredible career transition from taking some time off through data engineering and eventually into data science. I loved my conversation with her and I hope it helps you as well.
So, Sundas, thank you so much for coming on the Ken's Nearest Neighbors Podcast. I'm really glad I could bring you on. I think we met a while ago, actually on Clubhouse. I heard you speak about your experience working at multiple FAANG companies and to me. I was like, I have to have this person on and you just started YouTube. We're not just started. You've been doing it for a little while now.
[00:02:15] Sundas: No, no, no. I'm still a baby. I'm still a YouTube baby. So I'll take that.
[00:02:20] Ken: You have been growing at, you know, incredibly well there and I'm just happy we could have this conversation. You could talk about your journey and some of the lessons you learned along the way.
[00:02:28] Sundas: Well, I'm happy to be here. Yeah. I've been watching your YouTube channel. You're awesome. So super excited to be on your channel today. Cause I've been learning a lot from you and I'm happy to share whatever I can from my career journey for your community.
[00:02:45] Ken: Incredible. So one thing I'd like to do to get all the listeners kind of acclimated to the new guests is start with little story time. So how did you first get interested in data or what was your first data experience like? You know, was it something like pivotal, like one moment they got you really interested in the field or was it sort of a slow progression?
[00:03:06] Sundas: So for me, it was definitely pivotal because I did not grow up with a computer or a mobile phone.
So and nobody like in my family was in tech. So I had no idea that like people can actually work from their computer. So I didn't even know that this domain actually existed where people can work from their computer. And that is such thing called code. So data was like, not even in my kind of like my viewpoint when I starting my career.
So a little bit of backstory, I actually have a six year gap between my high school and college. So when I started all over again, I decided to go for business because that was to me, the obvious choice, because I was really good at accounting and math and all of that. So I went to the business school and in the business school, one of the things that my advisor told me was that take all the classes because when you start, I went to university of Washington.
And when you start there, they basically encourage. You take all the classes like there's marketing, finance, information systems and supply chain. So that way you kind of get the idea of what you actually enjoy doing, because you have to pick a specialization within the business degree as well. So I ended up taking all these classes during my first or second quarter off the business.
And one of the classes that I took was information systems. And that class basically covered databases, SQL, data manipulation, data analysis. And for me, like that was the aha moment. Like I loved business, but I, well, I liked business, but I loved, loved, loved the data class. And for me, like, that was pretty apparent at that point, like, this is exactly what I want to do.
So I started kind of like pivoting my path to go more into the data domain. And at that point, this was back in 2013-2014, and I didn't even know like, what are the job families is in there because like, if. It was actually 2012 when the Harvard Business Review relieves that articles like the data scientist, the sexiest job of 21st century.
And that's when the data fields actually started booming. So when I was getting into it was like still pretty brand new when it was like still getting defined, like was all over the place. But I kind of like started pivoting my path and created my own way into the data science field. I didn't drop the business major.
I kept the business major because I was too close to the graduation. I transferred. So I went to a better college and then I transferred. So it was only two years left, but I ended up taking a lot of classes outside of the business school. It's specifically into database management and SQL and as well as like online classes. So kind of like supplemented my business education, but the data courses and stuff.
[00:05:46] Ken: That's awesome. And so can you talk just a little bit more about more specifically how you've made that pivot? So you started taking more of these classes you know, did you start looking at like internships or did you start just applying to these different jobs or what, like, what was kind of the stepping stone until you landed your first job in that data domain?
[00:06:06] Sundas: Yeah. So I think the, I think a few things that I like to mention here. One was when I started, I actually started with a blank resume. So regardless of the data, job family, my resume was blank because I didn't work during that six year gap between high school and college. So when I got introduced to the data jock family, basically that this is what I wanted to do.
Then I had to kind of like build my project portfolio. And at that point there was this student organization called bits consulting, business information, technology consulting, which was at university of Washington and what they did, basically, they would consult smaller and midsize smaller companies like the companies that are just starting out and nonprofits.
In the greater Seattle area. And they would basically provide consulting that is more around like data projects and data analysis and database management. So I got involved in that organization, started working on a few projects that kind of like basically helped me build that. Well, like points where my resume.
And then I started applying for internships and I struggled quite a bit with internships because even though having that on my resume, the I still struggle because there was a six year gap and like, explain why that exists. Allegedly. And there was no data analyst internship or data scientists internship at that point.
So I eventually ended up going to this networking event where Amazon was present and they were recruiting for financial analyst intern. And at that point I didn't consider that because like I was, I knew I was didn't want to go into finance, but the person who was talking to was from Amazon, he kind of convinced me that this is more of like a data analyst internship than a financial analyst.
So he basically suggested me to give him my resume and I eventually ended up getting a call. Got that internship, ended up doing that for about eight months. Yes, he was right. Like it was more at more around like the data analyst type of work. Like I worked in the team, which was Amazon traffic, which basically supported different marketing teams.
Like how customers enter the site and what do they do after they entered the side? What do they interact with? What is their, what is our marketing spend versus what our ROI that's how I kind of got introduced to that data. John family in the industry. Like I can keep going on this because there's a lot more stories, but I think you have questions.
[00:08:36] Ken: The first thing you're the second person who's got done a consulting club at school that has been able to build an incredible resume point from it. Right. My friend, Tina long has also did something similar and she directly attributes that experience to what helped her land a job at one of the Fang companies as well.
If you can. You know, find internship experience. If you can't find the specific ways to that are more traditional and building experience. There are often a lot of really great ways that the barrier to entry is significantly lower and you kind of choose your own adventure. And I love seeing that sort of recurring theme.
that to me is incredible. You know, we didn't talk offline about your, the six year gap you took. So I don't know if you're comfortable covering it, but you know, outside of that, how can someone. If they do have gaps in their resume, how does someone navigate that in the interviewing space? Like what, what can they do to approach that scenario?
[00:09:34] Sundas: So I think that depends on your personal comfort. How much you want to share. Legally you don't have to share anything. Like if somebody asks you like you have a six year gap why did you do that? Or what? I don't think they can ask you directly that I think if anything. That actually, when you have like, made that pivot, but the, after a gap you have like gone back to school or gone back to industry, it actually shows that the strength and the determination that you have, because it's very hard to start all over again.
So I don't see it as a weak point, but rather it's like how you communicate it. Let's say if that's one of the interview questions you don't even have to, like, I personally don't go into detail. I just say like and so I started from scratch again, but like here I am, this is what I have done to build my portfolio, to show that I am really, really interested in here.
And I'm talking to you today. So again, this goes back to this goes back to like your comfort level. You don't have to share it, but if you do have a gap, like I actually see it as a strength rather than a week.
[00:10:37] Ken: Yeah, I really liked that. And the way you frame that, I think a lot of people who are transitioning into the field from quote unquote, unrelated careers, they, they look at their, you know, their past or whatever it is as this weakness.
And I don't think it is. I think just as you said, the signaling around that is that, Hey, if you were willing to go back and do a degree, if you were willing to put together this crazy, like really solid portfolio and put the time. And who cares what you did before. It's actually really impressive that you could go from doing XYZ to this new role and to Excel in it.
I really liked the way that you've described that. I'm very interested in hearing kind of what's next in the story, and now you you've got my interest.
[00:11:23] Sundas: Yeah. Awesome. So I think we stopped at the that internship. So basically what ended up happening after that? So I interned my internship was supposed to be six months.
At that point I think Amazon used to do very long internships and thank God that has changed. Like I ended up doing six months, eight months because the team needed additional sports. So the last two months I was actually taking school and doing my internship full-time but the team was super supportive or like letting me do that.
So one of the big things, I was like, I have to take six year off from six, six months off from school. So that added some sort of gap. But what that did for me that six months, like helped me basically be alive, that what I enjoy doing and I got to network a lot during that time period. And. I, my project was more focused on like providing insights on it from the marketing standpoint.
So at that point, like Amazon smile program launched, so I was doing, I did, my project was on Amazon smile. Like how, what kind of customers it's attracting and what is the marketing ROI? Because Amazon did not do any marketing spend on Amazon smile because the whole idea. Customers go directly to the site.
So my project, there were 40 interns in my batch. My project got the highest score among all the interns that presented, which was a big deal for me because I went to a community college and then I went to university of Washington. In my head, like there was no way in hell I can compete with this Ivy Leagues interns who were from like Harvard, Yale and all of those schools.
But like, for me, like that was the first moment when I realized like, I'm actually really good at this and this is, I want to continue doing it. So problem was that at that point, like they were giving me a return offer, but for a full-time financial analyst and I was pretty clear with my manager that I, that's not what I want to do.
But they are some rules, whatever that is, they couldn't change that. They couldn't change that to our data at all. So the networking that I did during my time in, during the internship, I basically landed two data interviews in a completely different teams. And I interviewed with them basically went through the full loop.
And I also took like a bunch of courses outside as well. Prepared me in like for like a data engineering interview. So I ended up interviewing with those two teams, got the job offers from both of them. And at that point, my team find out, found out that I got offers from two different teams for data engineering roles.
And that was the point they were like, okay, we're going to take you in like join our team as a data engineer and then we'll help you and we'll support you in whatever you need. So I ended up accepting my, the internship team offer because like I was pretty firm a little bit, the there, the team, I was pretty familiar with the data and what the domain is all about.
So that kind of like gave me a leg up in terms of when I started as a data engineer too, because I already was pretty familiar with what the team worked with and what was important, what was not did did engineering pretty much learned everything on the job? My senior teammate was super awesome. He taught me a lot.
So did that for two years. By the end of two years, I did a project that was more research focused. And the point when I realized, like I'm done with data engineering and I want to go more into the data science. So the next team that I went to was more data.
[00:14:49] Ken: Yep. Before we get to that, I mean, like, in my mind that was just a legitimate, I don't know if it was intuitive or what it was too, but it's sort of a masterclass in what to do and leverage.
Within an internship, right? So a lot of people they're thinking about the full-time role, but the easiest way to land a full-time role is to land an internship and get an offer. After that. I mean, the interview process for internships is the bars significantly lower. I mean, it's still difficult, but it's the bar is significantly lower.
And if you do like a pretty good job, You know, what percent of interns to, they generally make an offer to, you know, from your batch, do you know, off the top of your head? I would imagine it's fairly high. Right? you don't have to actually give a number. I, you know, I'd say in a lot of companies, it's almost 50% and most of the ones I did, the rate was around that.
And so I really like how it's like, okay, you go into this internship and you make the most of it. So many people with internships, with school, with all these things they go in and they just sort of think that having the internship or getting the degree is what will be enough to get next level outcomes.
And that's just not the case. Right? You went in you network effectively and you were able to get exactly what you wanted because. Went out and got it. Right. But you leverage the resources of the company and did that effectively. Well, I will ask something I think is really important is how do you network within an organization?
Let's say someone is starting their new internship. What do you do to create those connections that were so meaningful and like really pay dividends for you in the long run?
[00:16:29] Sundas: Yeah. So during my internship, like typically in in general, like working full time as well, I think one of the best ways to build a network is started with your team.
Like your, like your people that you work with every day. I think it's important to build connections. Outside of the project work that you do. So I like to do a lot of coffee chats. I like to do a lot of one-on-ones with people that I work with. And in those one-on-ones in those coffee chats, I like, we don't talk about work.
We talk about personal things. We talk about our career or career goals and aspirations when we were there, we want to go. So things like that. And we like basically connect with people who you work with at a deeper level. And in addition to that, like, if there are, let's say a specific, so one thing that I recently did is I went to meet with more.
And I've been like working remote at Google for the last two years. Networking has been hard. So I've been using my manager, my senior manager, my director to kind of connect me with people. That would be would be like w would be like in my domain and kind of like helped me expand my network that way.
So my director recently connected me with somebody and I'm having chat with them next week. So you lose your people around you and then use them as a resource to build. Networks. So it's basically kind of like a three, one person connects you to the next and the next. And th the other thing that really, really helped me specifically, like in-person was like these off what is the right word word? So specific clubs such as like women, so that when I was at Amazon, there was a club called women at AWS or women in engineering or women in data science or specific things like that, where I would go and kind of like build my network that way.
So there's like a lot of opportunities that are there to build on. But I think it's up to the person to kind of take advantage of that, but I would agree that it's definitely harder in that a more boreal it's something that I'm still struggling with to kind of figure it out. So if you have any tips, I would love to take it, your, your POV on it.
[00:18:36] Ken: I'm trying to think. I mean there's something that I always found really useful is the internal, like architecture is the companies have, so you have a org chart. You have. A lot of information on people through the network or through whatever platform that companies are using. And it's really easy to find people who might be interesting to talk to you.
A lot of the sort of chat messages are pretty interesting as well. I do remember when I was at one company, a massive company, unfortunately, 100 company turns they direct message the CEO, and I think that they were terminated pretty quickly. So don't do that. But you can through direct chat or through email or through a couple of people, you know, a couple of degrees of freedom, reach people that are significantly higher than you, and might be interested in talking to you if you have a good story.
So I will say don't necessarily. I know Bezos isn't necessarily there right now, but don't when you're an intern at Amazon.
[00:19:44] Sundas: That would not be a good look.
[00:19:49] Ken: Great story though.
[00:19:50] Sundas: I do want to mention like how we met, like you and I met through clubhouse and I think like that is like so many opportunities and platforms now for like working professionals that kind of like give you access to that network. Like, I don't think you and I would have met.
If it wasn't for a clubhouse or some of the other people that I've met over the last two years. So I think there's a lot of, a lot of platforms that are enabling professional networking. So taking full advantage of that is like very, very, very, very important, and can definitely help you expand your network like you and I are talking to them, like we're doing this recording like that wouldn't have happened.
We weren't connected through a social media platform. So I think like how we, how we network, how we connect, it's kind of like evolving, but this new age, internet and remote work and social media and all that.
[00:20:41] Ken: Yeah, I a hundred percent, I think that's such a valuable point. And if you feel like, you know, I think a lot of people on those platforms on LinkedIn or on clubhouse, or I guess Twitter has spaces now, too, they feel like they can't contribute to the conversation because they haven't had enough experience or whatever it is.
And I don't think that people are being fair to themselves in that circumstance asking good questions or sharing your experiences, even if they're fairly limited, are ways that you can contribute to the conversation. And a lot of the podcast episodes that I do including this one. I am not the expert in a lot of these things.
Like I haven't worked at any fan companies. Right. You've had a lot of experience in multiple ones and you can tell that story a lot better than I can. So like, my job in this circumstance is to hopefully ask good questions so that you can tell myself and the audience thing. They might not know before, and anyone can do that.
You know, someone who hasn't even worked as a data scientist could probably do that in an effective way. So I'd say that if you are thinking about these platforms and engaging, that is one very effective way to do it. And one way that you can make a meaningful contribution. Through your quote-unquote lack of knowledge rather than what you know, and what you bring to the table.
Sorry. So my I'll end my soapbox rant there, but I am interested now in how you went from that data engineering to the more data science role.
[00:22:09] Sundas: Yeah. I'm happy to share that. So basically I did data engineering for two years and. At that point, I started feeling pretty comfortable with the data engineering domain and I wanted to try something else.
And at that point I did a project that was more data to research. That's basically what intrigued me. And I was like, okay, this is what I exactly I want to do next. I want to do like more research. I want to do like more analysis. I want to do more like data science maybe work ended up talking to different teams.
Finally found this team and this manager of who I met. I explained to basically that manager, like what, where I have been, how I've kind of paved my path and this is what I want to do next. Like, would you give me an opportunity to join your team as a data? And make that transition from data engineering to data science and in his team, he had research scientists and applied scientists at that point.
And they had, they needed somebody to do some data engineering work. So it was perfect opportunity for me. So I ended up making that transition. I did the data engineering. But in addition to that, I started learning from my teammates from all the work that they have been doing, then my manager I think I talk about in my video as well.
My manager was like super awesome. Like we used our, one-on-one more of like an educational session where I would come prepared with questions and then my manager would kind of guide me. Okay. This is how it's. If he doesn't have that answer, he'll like point me to the right resources, such as books and online logs and stuff.
So that, for me, like that time period, that six months was like heavy learning. And eventually I knew enough at some point that I got, I started getting projects. And even when I got the. It was still like, I didn't know everything. So I would like learn and then I will go to work and I would apply it.
So basically that's how it ended up happening. Learn on the job pretty much.and when I joined that team data scientist job family did not have. But I think one year into that role, that's when the official data scientist job family came into existence. And my manager converted myself and two other teammates, including Deliana who is big on LinkedIn.
She and I were, we were on the same team. So...
[00:24:36] Ken: I just did her podcast. She was out here and I went, we had an Airbnb and I was on it. It was awesome. Yeah. Super cool.
[00:24:44] Sundas: Awesome. So Deliana myself and one other, one other teammate of ours got converted to the data science job family, and they don't working there for about three years. And then I decided to leave Amazon, but I'll pause here. Yeah.
[00:24:59] Ken: Well, so there's two things that I really wanted to highlight about what you just described there. So the first is that you realize, do you like data science or more of the research really? Things through experimentation. You learned that you liked to data engineering from taking this breadth of classes and understand, and then stumbling on the information systems and the way that you were able to find these things out about yourself effectively was trying new things and experimentation.
And I think that everyone should take these, that into consideration. I mean, a lot of people think that they'll like data science before they even try it. Right. And in my mind, it's like, you should try. These different things, you should try these different domains and there's ways to prototype or do this without too much effort.
You need to take an online class, do whatever it is, but that the aspect that you have to get your hands dirty and that you have to do this experiential learning to figure out your own interest in some things is really important. There, there are things that on the surface I'm like, Oh, I'd love that. And then I try it and it is not the case.
Right? The other thing is I loved how you sort of advocated for yourself and you asked about certain roles on certain teams. A lot of people are very scared to do that. They're scared to ask their boss or tell their boss what they want or to have any inclination about what the future of their career might be.
And in my mind, that's the exact wrong thing to do. Like your boss's job is to make you do the best work possible. If you're working on things that you're very passionate about and excited about, you're going to do better work. And so their goal is to match you with the things that they can or point you in the right direction.
And it's awesome to hear you had such a good manager that was able to help you do that. And it obviously, I think paid dividends for him as well. And the quality of work that you did. I mean, everyone on your team that you just described as like a superstar, so it must be doing something right. If shouldn't give that guy like a future creators, like a creators metal, or something along those lines.
[00:26:57] Sundas: Yeah, manager is very, very important and yes, that team was awesome. That manager was awesome. Like I would work for his name is Thursday. I would work for him any day because that's how awesome he was. So like managers job, like if the people management you'd develop people. So like, I think all of these things are really important that you're working with your basically your reports to like figure out what they want to do and like help them get.
I think, like, a sign of a bad manager is like, when you, all you do is like, you spend your time in your one-on-one on like project updates. Like that's not by good use of your one-on-one like use your one-on-one to develop your career. So I think it's very, very important to have a good manager.
And if you find a good manager, like follow them, like if I found like I would follow them, but he feel that I was on. So I wanted to leave Amazon, but like I would, if you have found a good manager, like just follow your manager, because a lot of your work quality and your experience at work would be influenced by who your manager is.
[00:28:01] Ken: It's funny in the last podcast. That's exactly what, one of the person Rashad Nieves Becker did his manager went to a different company at a significantly higher role. He was looking to advance to a more manager, actually a director level position. And he went to work for his old manager at a significantly higher.
Level than he was in his current company. And that's the power of those relationships, right. They know what you're capable of. It's like they've been interviewing for you for three years or something. Right. They know what they can expect. They know what you're capable of. And that's so much better in my mind unless you're doing a terrible job than someone assessing you for what does it maybe eight hours total and a couple of technology, the technology assessments I would trust the, your, your old manager's judgment over that. So now tell me about this transition out of Amazon. So you're an Amazonian at this point in time, you've had a lot of experience. There you go. And you know, obviously there's a lot of culture around that, you know, there's, there's.
You know, there's we just described it. You're an Amazonian. Right? What is it like to go into a new organization with also a very strong culture? You know, where there are more similarities than differences or what was that transition like for you?
[00:29:22] Sundas: Yeah. So for me I think a couple of things to mention here, like, first of all, like why I decided to leave Amazon, despite having such a great experience.
One was Amazon was my first out of school experience and I definitely wanted to get more industry experience. So it was like back of my mind, all of is that I will try another organization and other times. The second is like probably have heard of the golden handcuffs. So, which is basically, I'll explain for people who don't know, basically when you start, you're given an equity, a new, higher equity that grows.
And then at the end of four year, you basically hit a cliff, which is like you take downward dip in your total compensation. So from money standpoint, it kind of makes sense to kind of switch at that. So that was the second thing. The third thing, the manager that I really, really enjoyed working with, he actually ended up leaving the team and I had a new manager who was also good, but for me, like all of those three things combined kind of like push me to look outside ended up making that transition to Google.
Like it wasn't easy. I failed the Google interview twice and this was my third time trying it. And in my head I was like, okay, The one, get it this time. Like this is not meant to be but ended up getting the offer and joined Google and three months into, it was three months. Like first three months I was in person and that's after three months, that's when the pandemic cuts and everything went from well.
But in terms of the culture, different, like, yes, the culture is very, very different between the two organization. Google is all about like collaboration and one of the things that I did notice is like how nice everybody is. Like when I started when I, when I started one of the phrases that I kept hearing is like, I love that.
Like, I was new at that point. My teammate introduced me to somebody else in the New York office. And they were like this some days she just joined, joined two weeks ago. And then they're like, where are you from? Like, I'm from. Love that I was like, why do they love that? I don't get it. Like, what's there to love about me living in Seattle.
Like, but like everybody was like that. I eventually idealized, like, that's the culture. Basically. Everybody is like super nice. And I feel like I like, that's like one of my common phrases. Like if you're talking to me in person, like, I'll be saying like, love that, love that all the time. So people are like phenomenal.
The people that are like super nice. And I also love the aspect about Google. That is way more. But I work with my coworkers. I feel like I'm working with friends. Like I know them at a personal level. I know their kids' names. They know, they know mine, so it's like kind of like, I feel connected with them a lot more outside of work as well.
So yeah, the transition was amazing. I've been at Google for about two years super happy with the whole transition. I really liked the culture and the collaboration and. All the perks, which is not a lot, but working from home, but the culture of the work-life balance has been like super awesome transition from my last company.
[00:32:32] Ken: That's awesome. And so, you know, you've obviously worked again at two of these large technology companies. Now I have sort of two questions revolving around that. The first is, you know, what are some of the benefits of working in a large company versus another one that like, I talk a lot about, like, why I enjoy sort of maybe like more of a startup culture personally.
I see obviously the benefits of working at a large company as well. And then the second is when someone's approaching. Interviewing or trying to land a job at these companies, what are some things that they can do? So let's start with that first one, you know, like what are some of the benefits, obviously you described that work-lifeand compensation and those types of things, but other things that people that might not know about that are, that are massive benefits of working at larger organizations.
[00:33:17] Sundas: Yeah, I will tell you what I will tell you like my unique perspective and what specifically made me excited about working at this big organizations. When I started so I am brown, like I'm not your, I'm not your average white person. Okay. I come from a specific background. And when I was interviewing when I was initially starting, but now it's interviewing different companies.
One of the things that immediately stood out to me, like the diversity that specific companies had versus the good companies. And after working at Amazon at a big company where there were a lot more people who look like me, I felt like I belong there. Like it's kind of like, felt like very natural.
For me to kind of like work at a bigger company where I have access to a lot more people who look like me and and as well as like their mercy. So that was for me, like one of the biggest, biggest factors when I was deciding, cause I did have like offers from bunch of other companies. Named. But yeah, so that was like a big factor.
Then the other benefits I would say is like the, again, like you mentioned, the compensation, it's great. Like that stock has appreciated quite a bit for majority of the big tech companies. So if you started at a time when like four years ago, like stock has appreciated quite a bit. So that means like your total compensation grows as the company stock value grows.
So the money is obviously there. The other factor is that the opportunity to hop around. So let's say. I'm working in a, in a team when my first thing was more around the traffic and I kind of bored, kind of got bored too, working on the traffic team and doing like marketing and marketing kind of like analytics.
I don't have to leave the company to try a new domain, to try a new business. I just had to switch team. So there's like that advantage there. Like if you get, if you get bored, if you want to try something new, you always have the opportunity to kind of like hop around, but then. Without like going to that full interview.
Well, there is full interview, but then without a lot of hassle that you would typically have to go through with the external job searching. So that's definitely a big, big benefit. The last one is the learning that you probably have heard already, like a lot of these. Companies end up working with like, what is the latest and greatest?
And if you are working in, like, let's say, we're talking about data science, like, what is the latest in data science? Like the possibility is that you might end up working on that. Technology. And that means you kind of like keep your skills up to date and the companies do pay, basically pay you to like attend conferences and courses and to kind of like, keep your skills up to date.
But the industry trends. So I see those things as a benefit and obviously like worklife. It's not consistent across all teams and companies, but like for me, like having that work-life balance is super, super important because then I can do things like YouTube and things like other things that I enjoy doing outside of work.
But yeah, not saying like smaller companies and midsize companies are not great, but like, for me, like these has been like bigger, the attraction factors for joining and staying in.
[00:36:41] Ken: So I have sort of a up question related to that. And it's related to the tooling, that large company, a large tech companies use.
So since there's more infrastructure a lot, it sounds like a lot of the tools. Significantly more polished and packaged. So let's say I'm doing a project I'm using all these open source things. I I'm like creating my own data pipelines. I'm doing all these things. How is it like in a larger company? Is it you know, I know that for example Meta has their own data infrastructure suite.
Microsoft has. They don't code their dataset. Well, from my experience, the data science stuff work, but they don't code. They just use their like built-in GUI data science tools. Right. Is that a common theme or is that something that is specific to teams or, or as you know, or for example, at Amazon where you recommended, Hey, you should use this suite of tools to be able to do this, or, you know, where's the freedom versus like required to something.
[00:37:46] Sundas: Yeah. So I'll answer your first question in terms of like the structures and how like these, the, let's say we're talking about the data drop family in bigger companies, you will see that trend where basically there is a data engineering team who is managing the data infrastructure. Basically building those pipelines for you, then you'll have data scientists, data analysts and the rules like this, where the task are like more kind of like spread out versus like for, let's say a smaller startup, like where if they are only, they only have headcount to hire like two people or three people for the data role, then that means like, those people would have to like be involved in pretty much like all lifestyles.
All of a data, data science project. Yes. There is opportunity to learn there. And I guess like that being goes back to the personal preferences, like what you want to do and what you enjoy doing. So yeah, like as the company grows, you will see like, this is like in a more structure of fashion. The different components of a data project lifecycle is like built out into different teams and different organizations in terms of the tooling.
The only thing I would say here, like depending on which company you end up in, there are like specific companies are invested in post specific tools that they invest in. For example, Amazon was like very heavy on. Microsoft would be super heavy on like using Microsoft cloud pro cloud, basically software and stuff.
So the company, the company is like very, could be very different. But, but yeah, like so that means like when you switch from one company to the next company, you'll have to learn the different infrastructure and how to work with that. So for me, like I am, and they also have like a lot of in-house tools that you have to kind of learn.
And yeah. So when I was at Amazon, I was working with. AWS software. And then here, I'm working with a lot of like Google cloud stuff. So like, and next somebody I go to like, they're going to have their own repository of tools that I would have to learn and use for my day to day job.
[00:39:50] Ken: Incredible stuff. Awesome. I mean, that makes a lot of sense. I almost completely forgot about how every company that I described except for meadow is a cloud provider. So yes, I am sure.
[00:40:02] Sundas: What Meta uses, like now I'm curious.
[00:40:06] Ken: I'm sure they don't outsource it because they have, they have to have so much compute, right? I mean...
I'm actually surprised they don't offer, like a lot of cloud products. I mean, they see that seems to be good economies of scale, a good economy of scope, but who knows? So can you talk a little bit about the, you know, sort of the application process and how someone would approach or, or give themselves the best chance at landing a job with.
[00:40:41] Sundas: Yeah, I can give, share some things that I specifically did. So basically having a solid portfolio speaks for itself. So understanding the role is very, very important because you would like when we're talking about data science, you would learn that like a goal, one role specifically for company X does not match a hundred percent.
Y so you would have to kind of like understand what rule you are. And what are the requirements and kind of basically align your portfolio with that. So that way you kind of like increasing your chances for getting a callback for that role that you are targeting. And w one of the things that people can use is like LinkedIn, that there are a lot of people who are, who have like, shared what they do on LinkedIn.
Let's say if you were targeting Amazon data scientists, you can look at Amazon. Profiles on LinkedIn and see like what projects, what their, where are their qualifications and what they're doing currently depend on, of get an idea. So aligning your portfolio, your experience to the job description, I would say like, that's a big, big, big factor.
Second is getting a referral. So when you get a referral, you basically increase your chances of getting a call back and that's what you want. And I think a lot of people don't know. and I would highly encourage, like built, started building your network. Like here's one thing I would like to say that like start building your network V head in advance.
So you're not reaching out to people for the first time when you actually need that Afro, because that just seems very transactional. So build that relationship. So when it's time to get that approved there, you were able to like add. Because you already have a history with them get a referral. And the third thing, which is, I think is very important.
Like you align your portfolio, you build your network, you've got to call back. Now you actually have to pass the interview. And if you have already have you probably already have heard that the interviews are intense. So make sure that you're spending a lot of time preparing for an interview, create interview as a project in itself where you are spending time because doing your day-to-day job is one thing being able to solve.
In a limited timeframe, under pressure setting is a completely different skillset. So, and that takes muscles to build muscle, to build. So you will have to spend some time kind of like practicing your technical skills as well as your soft skills before you actually go to that interview and do the interview with that company.
You can do all the groundwork, but if you're not good at what you good at interviewing, you're probably not going to get the offer. So all of those three boxes have to be checked in order to kind of like increase your chances. Getting, getting a job offers that being said like big pack is not everything.
Like there's amazing companies out there. Which probably don't have that rigorous often interview process. So I wouldn't just limit, I think there's this fascination around big tech. Like everybody wants to work at big tech. Like I would encourage people to like expand their search and like look outside as well.
And don't just like set your target to like, this is what I want to do in big tech is the only place I would be successful if I was. Yeah.
[00:43:55] Ken: I think the three things that you described are relevant to. Anyone applying for a data scientist, a data engineering position, you know, in particular, I think the first one is really relevant in my mind is doing research on the people and understanding what got them in something that a lot of people ask me is related to if they should get a master's in data science.
Right. And I haven't taken, I I've done one master's degree and it was a computer science. I haven't, I can't do research on every single one. Different programs and the can, should I take this one from Georgia Tech? Should I do this one from X, Y, Z? And my advice is almost always, either reach out to alumni of the program and ask what they thought and what their experience was, or look at their exit opportunities.
Find a bunch of people who have that and see where they ended up. Right. Are those places you want to end up? Maybe it's a good fit, maybe at the results, speak for themselves. And so. In my mind doing that type of research is a prerequisite, or is going to be valuable in almost any scenario in this space and understanding and learning about the people and outcomes, especially related to reaching out to people.
The thing you just described, a lot of the people who I know that work at Google or meadow or Amazon, or any of these large companies, one of their biggest pet peeves is when people do what you just described, which is reaching. Right when they have an interview coming up next. Right. I think that there's this feeling, which is.
Which is not a good feeling that someone is interested in. You just for where you work. They're only asking you questions about Google. They're only asking you questions about Amazon. They're only asking you things about not who you are, but like the company you work for to get information on that.
And I think that, you know, that's not a position anyone wants to be in or was feeling, and hopefully this interview doesn't ever feel like that. But, you know, I think if you're approaching. People who work at these companies, just like you said to further and advance, and you're not just asking stuff about the company you're asking about their career journey.
You're asking about their experience. That's something that gets you. It can help enhance her relationship with that person rather than, than just keeping it as a transaction, which we never want in any relationships that we have. So I really, really I love what, what you were saying there, and hopefully I did what you were describing a little, some justice by, by expanding, by expanding there a little bit.
You know, I would like to move on to talk about your YouTube journey and your experience there. And then also on clubhouse, you know, you've been really good at being public in these spaces. And I want to learn more about kind of your incentives to do that. And you know, how you've been able to grow in this space.
[00:46:51] Sundas: Yeah, I will first talk about like why I have a public public presence. Like I have a great job. Like I make a decent amount of money, but the reason I decide to put myself and it's not easy to put yourself out there. The reason I decided to put my self out there because I want, because I am the first female in my family to graduate from a university program.
And I'm the first female in my family to actually be working in the corporate environment. And I want future. People and people, people who are entering the data fields and train the tech field. I want them to see me and know that like it's possible just because it hasn't been done before. Doesn't mean it can't be done now.
And it doesn't mean there's space for you. That's like one of the biggest reasons for me to kind of like take space. On social media, whether that is like on Instagram and YouTube. The second thing is like, I love teaching. Like I was tutoring when I was like, I was a tutor when I was in high school. I was like teaching Matt to my peers, basically other students.
When I started back again, like went back to university, I was teaching, I was a tutor for information systems and they were like 300 students there who would like come during my office hours. Ask me questions. So I love teaching, like in my other life, I always like to say that in my other life I'll be a teacher.
Like I love teaching. And with platforms like YouTube and Instagram, I feel like I have a platform to teach people from my experience. And that is like one of the, like the second biggest factor for me to kind of like be here and like teach people what the learnings that I've had from my experience, like to share it with everybody.
So I can basically contribute. That's my way of contributing. Yeah, so like I started YouTube by thing like above. Eight months ago. It was like driven by the lonely, less loneliness in the pandemic, basically, which turned into Ms. Turned into what I have today. I think I have like about like 50k people following me, which is crazy to me.
I don't know. Like, I don't know what I'm doing. Yeah. I don't know. Well, I know what I'm doing, but like I did not expect my saddle to grow. This basketball. It's like, it gets me really excited, which makes me realize like people actually find valuable what I have to say and makes me want to keep going.
But I still have a lot, a lot, a lot to learn. And watching you, watching Tina, watching double who runs coding visits, I feel like I'm going to get there at some point, but like, I still have like a lot of learning to do from like all the amazing YouTubers who are talking about data science. Well,
[00:49:30] Ken: I think a lot of that is the fun you described it at your work.
A lot of the enjoyment comes from learning on the job and doing those types of things. I look at content is very similar and it, you know, I really love. Sort of your onus for starting to do this and telling your story because you do have an incredible story. I mean, you know, you took a lot of time off, you went from community college to normal organization to interning at one of the largest companies in the world to interning at probably one of the most sought after companies in the world after that.
And you're able to do that, you know, in a relatively short period of time after you made up your mind. I think for anyone like that story is inspirational because it suggests that Hey. Even, you know, after the like traditional time period for me to do something, you can do it outside of the traditional channels, which might be like the Ivy League institutions you described, you can do this and have this incredible success.
So I love that. And you know, the reason that I created the podcast is so I can help tell stories like this. I mean, my story is not nearly as interesting, but the beauty is that I can bring in people who have these incredible and inspirational storiesand hopefully, hopefully help inspire other people that are, that are tuning in.
So that really just, it kind of warms my heart because it's exactly the purpose of, of why I do a lot of this as well. So I really, really enjoy that. Yeah. Also one of the, one of the videos I really love is where you're talking to your parents, you're kind of explaining to them what you do. Can you talk a little bit about the sort of backstory around that?
[00:51:08] Sundas: So I think at that point I was just like experimenting with YouTube. You can probably see like how poor the lighting is. Cause I was like working with the bring light in my basement and I was like still experimenting and I was like, it would be a cool idea to like have my parents on my panel and kind of like ask them.
Like if they understand what I do for a living and honestly, like, they have no clue, like, but the video turned out to be so funny. Like I watched it today. Like, I laugh every time I watch it. Basically they don't understand. And I don't know, like how you feel about like, how your parents' reaction would be, if you were to ask them what you do.
They think like I just sit on a computer. So there are times when I can take their calls. They're like, you you're probably ignoring us. And I've like, tried to explain it to them, like what I do, but I think I'll just leave it at that. Like they just know I work from the computer and do something with the data and my bad things.
I'd make a lot of money. It's okay. You can think that way, but my family doesn't really understand, but I do. There was actually, I was featured on Forbes. Like my story was shared on one of the Forbes article and I shared that with my family and I like look up. And like, there was no reaction because they didn't know how to react.
So at this point, like I'm kind of like numb through this idea of like having some sort of reaction and having somebody understand what I actually do for a living. So it's okay.
[00:52:30] Ken: I love that. Yeah. They don't, they don't feign to understand. So my dad, after I did my masters in computer science, he would tell everyone I did a master's in.
And so all of his friends just like calm, they have to fix their computers and stuff. And I'm like that. That's not what I do. Like, it's very different. I'm not, I think I might've gotten them straightened out now, but at least he, you know, he understands that I work with computers in some sense. I think it's funny.
He does also get to see the, like perks of my work in terms of sports. So we go to these events to go to these things, but. I find it really funny, like a generational thing. So both my parents are doctors, right? So there is no, no concept of technology and the growth and the innovation that's coming.
I love my parents, but they, they do struggle a little bit with with the my dad's actually not that bad, but my mom is, she's a lost cause with the technology, but I think I would hope for most parents, what's important is that you're happy that you enjoy your work and. You know, you can support yourself, which my parents were very worried about when I decided to go back to school again.
But I think that I've proved them wrong, hopefully.
[00:53:48] Sundas: Yeah. I think there's a generational gap. I think the future generation, like our kids would actually know what we do. Hopefully we'll know what they do cause I know how fast things are following, unless we ended up on TikTok and then we'll going to have a hard time understanding what's happening.
[00:54:05] Ken: Exactly well. So I only have one last question and you know, I think it's, I think it's really relevant to a lot of the audience. I mean, even me, I felt like when I was getting into this field that I was behind everyone that I had to catch up. You took some time off and when you started, I can imagine it probably felt like.
I behind the curve. How do I catch up? How do you deal with that feeling and effectively, how do you catch up to your peers? How do you, well, I mean, if your peers are unimportant, how do you create that own path, your own path to succeed in this field? Even if you know, someone is switching careers when they're 30 or something like that.
[00:54:44] Sundas: Yeah. You know, I felt that a lot in my initial initial years off my career, but I felt like people who. People that I work with. They're just like 20 years old. I'm like, what am I doing? Like, I'm like still behind. So like I did let that affect me at some point until my mentor told me something that kind of like changed my perspective.
One is that you should always be comparing your progress to basically your journey, basically where you work and where you are now. That's your competitor. It's not the other people's progress, not, not other people's status on there. They basically journey is your comparison, like compare your progress to yourself.
So like, whenever I'm feeling like I'm behind, I look at where I was 10 years ago and I'd remind myself like all the success and progress that I've had. And that gives me a lot of peace of mind then. Satisfaction. And there is a concept of imposter syndrome. Like I'm sure you have heard of it. Like, yes, that does stay with me.
But I think over the years I have kind of like kind of like. Figured out a way to kind of like manage that and not let that take over my basically consciousness and like, hold me back from like taking risk and taking like big career decisions and things like that. So yeah, like don't compare like everybody listening, please don't compare yourself to somebody else.
Compare yourself to yourself. We're all kind of like paving our own path and we're all on our own journeys to kind of get to where Albert we are. We are destined to be. So that's my biggest tip is like, don't compare yourself to somebody else.
[00:56:35] Ken: One of the most important things too, is the timeframe you described. So you just said 10 years ago, I was doing this and now, you know, look how far that I've come. It's a very human thing to not be able to notice, change over a short period of periods of time right yesterday to today. Am I doing better than I was yesterday?
I dunno. Right? Even a week ago, what have I accomplished in that period? Once we start getting to a month or we get to a year, we get to five years, you know, like, Oh my goodness. You know, five years ago, I had zero social presence. I was in grad school, I think for the first time. It didn't have a job yet, or I was doing anything meaningful.
And I look now and I say, wow, I've built my own business. I've worked at a couple of really cool companies. I've had great experiences. I've created a forum where I can talk to other really incredible people at the time. I mean, there's, there's so far that I've come a natural period of time. And if I had asked myself, then if I would achieve something like this, I probably would've said, no, that's crazy.
And so. You know, a little bit of effort over a long period of time can go an incredibly long way. And I think that that's such an awesome message that you've described there. So with that, how can people learn more about you? How can people get in touch with you? If that's something you're open to I'd love to also know what you're working on, right.
[00:57:58] Sundas: Yeah, so the best way to get in touch with me, or basically follow along my journey is through YouTube and Instagram, as well as be infested in my story, you can read my article on Forbes as well as I shared, and I shared a detailed physical. Are you in, on my website as well. So leave it at that in terms of what I'm working on right now, like at, you may not work or outside.
[00:58:25] Ken: Oh, I am like outside of work.
[00:58:28] Sundas: So, so far, like I think I trying to live through this pandemic. I'm not doing anything exciting. I am just like continuing my. Basically dirty that I started. I want to make sure that I'm posting consistently and I'm posting quality content. And I also post a lot more funny, funny stuff on Instagram and some educational stuff there.
Yeah, so I have like some personal family stuff going on. So my goal is to just like survive that and as well as like keep doing things that I'm doing at my day job and as well as things like. So nothing, nothing exciting, nothing amazing. But my goal is to like, just be,
[00:59:13] Ken: I think that there's something very exciting about just being in general.
So, Sundas, thank you so much for coming on the podcast. I really enjoyed this and I can't wait until we share a stage again, or we talk again about something in the future.
[00:59:26] Sundas: Yeah. Thank you so much for having me. It was a pleasure.