My Career in Data Season 4 Episode 13: Jayakumar Ramalingam, Staff Software Engineer and Cloud Ar...

Hello and welcome. My name is Shannon Kemp and I'm the chief digital officer of Data Diversity and this is my career in data, [music] a data university talks podcast dedicated to learning from those who have careers in data management to understand how they got there and to talk with people [music] who will help make those careers a little bit easier. To keep up to date in the latest data management education, go to data.net/subscribe. Hello and welcome to my career in data, a podcast where we discuss with industry leaders and experts how they have built their careers. I'm your host Shannon Kemp and today we're talking to Giaumar Roma Lingham from Sirius XM. Today we are joined by Jakumar Roman Lingham, a staff software engineer and cloud architect at SiriusXM. And normally this is where a podcast host would read a short bio of the guest, but in this podcast your bio is what we're here to talk about. Jay, hello and welcome. >> Hey Shannon, thank you. Thanks for thanks for having me. >> Thanks for being here. I really appreciate it and I'm excited to hear your story here. So tell me, you are a staff software engineer and cloud architect at SiriusXM, a satellite streaming company. And as a staff software engineer and cloud architect, it's a very long title, I love it. What is it that you do? I work in Sirius in the personalization area. It's like where I design and build software system that supports personalization experience and recommendations. Uh like so many should be aware like Sirius is something that you uh listen from the car but it's more than a car. Uh you have multiple digital platforms like mobile and uh and like multiple connected devices. So that's it. Sirius also has Pandora. So that's where I work on the personalization area about it. So my role as a staff is more like you know the personalization where it's as personalization it is. So something that we create relevant data to the [snorts] users. So obviously it's highly datadriven because you collect large volumes of information and generate multiple signals from the users to make the customer experience better. Right. So my role is uh kind of both directly involving in the engineering contributions of it across multiple systems and component as well as architecturally support multiple systems. So how the data is being collected from multiple system how it flows through the systems and eventually how we are able to serve the recommendations to the end clients. >> Oh that is so cool. I I love that. And I I mean I want to don't want to admit how much I rely on those personalization systems. I love when some I get great recommendations based off of the platform getting to know me. Um it makes my life so much easier. So that's very very cool. You're the first person I've talked to who uh who helps design and use that that data. Um so so what data are you looking at you know and how are you uh working with that data in your day-to-day job to decide you know and to to make those personalizations. Uh yeah so like personalization as I was saying data is the center of it right like so you collect uh various relevant information across the systems like there are like uh you can't even imagine like the number of datas that is been uh the as an organization collects from the users. So we have to collect those data information. So that collection part itself like involves multiple pipelines where uh you create like multiple pipelines to get those information available to the recommendation systems on different timelines right like some can be like bad some can be like near near line something like that. So uh and then once I have those pipelines created those datas for me and then use those data how you make those data more recommendable like the one good thing or bad thing about recommendation is there is no like a certain output like uh you cannot say hey this is the expected output or something. So it serves well for some it may not for some. So uh it's so [snorts] my work doesn't end with just serving the best recommendation. is also like you know uh collecting a feedback of it like looking at the user engagement of it and then uh make sure how you improve and also reuse those for the training purpose of it and then there are multiple aspects so where you kind of the system you have to make sure it evolves continuously as well I also create like use data for creating multiple dashboards that's not just metrics that we use within the system that's like used widely to give a real impact on the how the user engagements are like how the business impacts are there are like you know uh multiple metrics also I try to work having said uh doing all this an obvious uh thing that keep in mind is like you know the data governance and the security aspects of the users. So we we do uh maintain the privacy aspects of it not just like you know scraping multiple datas. So with all that you know like kind of uh end to end in simple terms it's know it's like a continuous loop where multiple pipelines to collect it process it create a user experience within the defined governance and then uh and then you know continuously eving on the feedbacks yeah throughout the data. >> Oh very very very very nice. Yeah you know um I I I listen to a lot of music. I love music and I love when I get suggestions of new music to listen to and try cuz I don't know that I would reach out and try otherwise like I I get fixed into my routine, right? But I love it when I'm and when I get suggestions uh and I like it, right? And I like the opportunity to to thank you for what you do. [laughter] Um >> and one more thing to add is now that we are on a podcast series exam is one of the largest network of the podcast not just the music. So it has multiple aspects of it like you know sports talk a lot of things there a lot of podcasts you know very good great podcasts >> very cool I love it. All right so yeah so we'll be sure to listen to the podcast here. [laughter] Um okay so Jay tell me um so let's back it up a bit. Let's talk about how you got into doing such cool things. Um >> tell me is this when you were very young uh say six years old what was the dream did what did you want to be when you grew up what did you say to yourself I'm going to become a staff software engineer and cloud architect what was the dream >> yeah [laughter] uh to be honest I didn't even know that the role existed back then so when I was really young not even young like even to some extent I was I I never had like you know a fixed uh fix the profession or something like uh someday watch a good movies [laughter] or see some inspirational characters and oh no I want to be that that profession you know so it keeps changing uh so I would not say I was like you know very specific about the preparation but eventually once like as I started growing up like getting near to the college I still you know I kept uh took the common stream where multiple options are available because being the going to be a first graduate of the family I didn't had a push or like you know guidance where this is what I'm going to do. So I kept my options open until I uh went to the college. I mean right before college and right before college that's where I decided like options was like you know way the medicines or like engineering back then. So I chose engineering for the practical reasons. [laughter] [clears throat] It's uh humorously you can say like you know the time to market. So I want to get my career soon. So start my career soon. So I uh chose computer engineering and then once I start it was no looking back you know I was so so fond of it and I was so uh got addicted to the coding aspects of it like back then coding was not fun. So [laughter] >> so it's not it's not as helpful as now it is so you have to do it not I was even doing without some editor's help. So but then I was closely aligned and then you know then then no looking back. So that's how we eventually got into the computer science engineering. >> Oh, so it was a choice then. Yeah, [laughter] your choice. Yeah. Oh, that's very nice. Uh so then what so you major in engineering? So um what's the [clears throat] first job out of college? >> Uh yeah the straight away I went to like a big consultancy back in India. So into a fintech. So as I was seeing I was I had enough exposure to the coding aspects of it but [laughter] it's totally different like uh the first opportunity I got you know like I was working on a fintech like the financial transactions management wow and I was like wow of course coding is not just a part of software engineering and there are like many aspects to it so slowly getting involved to it like you know we think or we as we start the career we think like oh I have certain data then how I'm I'm going to serve it to the customer. That's all we think. But eventually you know you know uh behind the system like uh how you see those transactions providing those like you know the business value or the fraud detection multiple things that happens behind uh that gives a lot of insights and slowly getting uh the web scale or the distributed large number of scale uh that informations that we try to I got into. So slowly getting exposure into that. Uh the same consultancy I also moved to another retail the Home Depot side of it and then I was the pricing and merchandising area. So again another uh high dataentric information. uh but uh I was fortunate enough to you know like get exposed to the big data Hadoop that's that's when it was evolving and that gave me a lot of experience you know not just the distributed system like you know so we were already the pricing merchandising side of things are done but with the big data something that was not possible earlier uh with the amount of compute and storage options that it gave uh you know like the volumes of volumes of data that was able to process and gave a giving a proper insight for the pricing really really uh you know was um a big learning curve for me and that's where like more getting towards the data side of it and getting learning the pipelining the the batching and then those things uh that's where you know got learned to the from the software engineering to a data side where you uh handle huge number of data which eventually before that would have been running for months or something but we are doing it for like few hours and then getting a lot of insights out of it and lot of benefits out of it. So that was one transition that I made uh that pricing and then also I've uh for some quite some time I also worked on the automotive part of it where I was uh real time price report generation of vehicles just based on images like you know you have to do a lot of complexity and then like commonly available data and then you lot of fun stuffs all the way and but all these were like you know I somehow felt it is like you know I have enough exposure but it's uh I wanted to a product side of it where I can get into the real user experience side of it like where I have impact on the real direct user rather than like the those so far I had some indirect uh exposure so that's when I got the opportunity to the serious XM so that's where I got to another side of like you know uh getting to the training aspects of it and the agentic aspects of it like more learning uh and then working closely with the you know the data as well as the user centric information. So that really was exciting partic that is very nice. It's not the first time either that I've heard somebody you know have that curiosity and the passion like lead them into a direction of wanting to uh make a difference with the customers and and in and managing data that way. And it's interesting like you say getting into the agentic stuff. Um how are you keeping up on the tech as you go through your career? I mean uh you know you like you say when you started out the coding is was so different from what it is today right and now you're working with agentic AI. Um how do you keep yourself up to date and learning and um you know is it are you learning after you take a job? Are you always learning or how what's been your kind of your process there? >> Uh yeah, that's one of the great aspects of this career right like software engineering there you can't never stop learning keep because things keep on evolving. So learning is something you have to do uh simultaneously because things can't imagine things that we are doing today uh was even uh feasible like like even just few few years back right so uh to keep update is like um uh as soon as I see something new uh like I try to understand the basics of it rather than you know mastering against it. So always some knowledge helps what it is rather than you know staying away from it. So that really help when I get a chance to get it uh whenever I get a chance to have uh go at it so that it's I already have some basic information that I'll run out of it won't and then you know immediately going that curiosity goes and then you can go ahead and uh immediately switch whenever it's possible. >> Nice. I like it. [music] Entrylevel analyst to executive data leaders, data delivers the [music] most comprehensive training in the industry, led by experts who are actually doing the work. Upskill with ondemand courses, earn exclusive certifications, and join a global community of data pros committed to driving real [music] change at datarcity.net. >> What's your favorite way to learn? Is it reading? >> That also keep evolving. I remember back in days like where I have to you know run behind books [laughter] and like get keep track of books and then learning from books and then eventually you have like you know the lot of courses and stuffs but now I now it's more of a creative learning right like you don't you have to you know learn uh from a book or something you can directly have [clears throat] lot of options to ask questions directly so now how I learn is like I keep myself with the tech updates but whenever there is a questions I I'll be like hey what how they are solving it what is they are doing it how this problem is I always uh you know closely relates to some of the real world problem that I'm seeing or facing not just from my work even from outside I do something personally also in software also any other things that I um visualize I try to learn like hey how things are getting done like I know uh at least tech side trying to be you know we cannot be masterable but I can at least be jack up for me so that's how I keep uh you know like um keep updating whatever that's coming new and try to learn yeah >> oh very nice so what's been your uh biggest lesson so far in your career >> uh biggest lesson is like like we are talking the obvious lesson is you know never stop learning and you can't you know especially in the industry where like if the data or tech or something. So that is one of the key aspects of it and uh other thing is obviously is the communication aspects of it right from a individual aspects as well as you know the project aspects from individual uh you got to keep your communications both the vertical up the layer or the horizontal even with the nonext because the more [clears throat] you communicate the more you understand the problem. So it's not about you know building a perfect solution. Actually to me to be honest there is no perfect solution. So uh because the more you communicate you know like sometimes you communicate with the product or the customer they're like no we don't even care about that information. So so it's always uh you know the communication helps you to see the problem as a holistics view right like rather than certain focus that's how because sometimes some datas are easily available we are not even aware of it. you are trying to you know generate it or like you know complicate it but through communication that's where we listen and oh these datas are readily available or like you know we don't even have to do this so those sort of information really helps and as a personally as part of communication you ask a lot of questions that's where you kind of learn like oh okay oh this uh this can be done like this this can be like this so that is uh [snorts] one of the big lessons you know that uh uh I learned and obviously as part of tech as I and learning and curiosity and being creative also one of the things that needs to be there. It's that is my favorite commonality and uh amongst the data community and you know everybody who I come across is I love that curiosity. I love the the questioning it's just it helps I think everyone uh that in the community is like has such an open mind like you know I want to learn teach me how why you think the way you think and how and then I'm going to write a solution to to help make your life better. Right? [laughter] It's it's just awesome. Um so so tell me Jay um I mean you worked with data pract your practically your whole career you know so what is your definition of data? >> Yeah that's a good question like for me data is a obviously representation of something right something that happened or like something but obviously the same data can be like represent different things based on the context different context. So it's currently what I'm working on the data that is that was something yesterday is not totally uh different what it is today. So uh so those are like basically it's a representation of something but by definition uh I mean we would have been exposed to the structured and unstructured data right like structured is something where you have it in your like a table or like a known format storage and then it flows across multiple system and then you deliver to the customer but there are uh unstructured data also like you know not just the text format like any other format. So I think uh with this AI tooling so we have the leverage now to you know process those unstructured so you don't have to be uh totally reliant on structural flow so there is unstructured flows where you can that also helps you to give some sort of representation that I was saying like uh so uh but necessarily it's not about you know having lot of data into it's about like exactly finding out what those data is and then finding a better context and quality to include it. So uh to leverage a greater insight. So that is the definition aspects of it like the structure and unstructured of it. Uh but as an uh like as an organization like you know we keep uh growing as a data like huge number of datas. So I think um uh yeah so you know the more we democratize data uh as within the governance so that's how you know it helps the representation part of it. >> Yeah absolutely so tell me do you see the importance of data management and the number of jobs working with data increasing or decreasing? I you know I start when I started the podcast I said over the next 10 years but now it's moving so fast [laughter] like with AI the introduction of AI that just sounds so far but just you know even in the next year the next few years you know where do you see the job market going data >> u yeah definitely I cannot answer for 10 [clears throat] years obviously but yeah I think uh I still see it should increase definitely because uh the way things are going on you know obviously the the more technical transformation the companies are doing obviously we're going to end up with lot of data I know like you know there are AI tools >> [snorts] >> uh so I think definitely the responsibility of the data engineer or like any person working with the data will change uh but I think the there should not be any disc disturbance within the like the number of uh people that's going to work you know so the reason being I still these tools AI or anything as a enabler not not yet a replacer. So with the enabler it gives provides more opportunities right like even back then as I was saying you know in adoo it provide lot of opportunities to you know process with the huge amount of data where that was not an option before that now again similarly now we have opportunities where companies were like worried oh we don't want to explore it it might take months it might take too much effort but now I think with the amount of productivity you have with because of these tools so companies obviously going to open more opportunities oh we can explore this time to market is not that bad. The investment is not going to be bad. We can try out. Uh so that obviously is going to create more opportunities at least in the some next few years. So that I see the opportunity is definitely going to be increased but the responsibility aspect of it the day-to-day activities of it that will change you know that um but obviously the performance also is going to be improved. So and obviously more roles is going to happen. Definitely it's going to open up. >> Uh I agree. So what advice then would you give to people looking to get into career in data management? Um yeah I I would not say there is like you know one path to data management like see I myself was entered into the computer science and then software engineering and then I'm translating to big data and uh so but I should be like you know as we are discussing earlier the curious aspects of ATP are really into like the data or something uh just you know jump right into it like you know understand the basic concept of it. So now we have lot of enablement so you don't have to like you know master everything. So it's kind of understanding the basic concepts and then you know trying to leverage what we have. So it's uh more of no you know not sticking to any particular tool or something like not dwelling on any tool like you know uh try to understand the concepts and then uh gain those even I would also get into the practical knowledge of it like I was saying think of a real world example and get your hands into it. if if not possible at least get into the the work that others are doing and then you know have a exposure to it so [snorts] that because I see even in some of my friends who are nontechnical getting into data so but at the same time I see some people you know like oh you know it may not suit me I'm not the expert on that like know with the with the amount of enablement that we have you don't have to expertise yourself and then jump into data so [laughter] I think you can have some as long as you have the problem solving skills and you have the creativity and uh you have the problem solving ability that should be uh good enough and then uh once you start learning the basic concept and gain some practical knowledge that should be a uh straight away a good option to jump into any data or like start your career in data. >> Uh I couldn't agree more. I mean, that's why part of why I this podcast, right, is so people can hear all the different ways you can work with data and the different paths um to into a career in data. There's so many different paths and and ways uh to get into it because as you say, as long as they're curious, willing to learn and constantly learn, you know, and have that problem solving skill, then that's >> um so important. And you know the communication you mentioned earlier too. I mean it's so >> yeah it's it's so important. It's part of why we offer our communication class and our training program cuz it is so important part of such an important part of it. >> Yeah because we have to explain you know like it's not it's not about working you have to even to understand a problem you have to communicate with the multiple people and then to solve the problem you have to come. So that is also comes as a key skills as part of the data. >> Yeah. Oh, such good advice. So Jay, thank you so much for taking the time and sharing your story with us today. >> Oh, thank you. Thanks for having me. Thanks for the opportunity to express ideas. [laughter] Yeah. >> Yeah. And um we'll uh you gave us your LinkedIn to post. Um so we'll get that posted if you people want to reach out and say hey and learn some more and just meet up. Um, again, that's a great part of the community is just the the outreach. So, thank you so much. >> Thank you. Yeah, I'll be happy to, you know, address and if anyone wants to reach me. Thank you. >> Oh, thank you. And to all of our listeners out there, uh, if you'd like to keep up to date in the latest in data management education, you can go to data.net/subscribe. Until next time, stay curious, [music] everyone.