Meeting Title: Brainforge Data Engineer Interview Date: 2026-02-05 Meeting participants: Selenge Tulga, Uttam Kumaran
WEBVTT
1 00:02:33.080 ⇒ 00:02:33.750 Uttam Kumaran: Hello!
2 00:02:33.750 ⇒ 00:02:36.270 Selenge Tulga: Good morning. Hi, how are you?
3 00:02:36.430 ⇒ 00:02:37.950 Selenge Tulga: I’m good, how are you?
4 00:02:38.310 ⇒ 00:02:39.450 Uttam Kumaran: Good to meet you.
5 00:02:39.900 ⇒ 00:02:41.320 Selenge Tulga: Yeah, same.
6 00:02:41.870 ⇒ 00:02:43.289 Uttam Kumaran: How’s the week going?
7 00:02:44.100 ⇒ 00:02:49.929 Selenge Tulga: It’s pretty good, right? The last week was freezing, this week is sunny, yeah, it’s chewing me up.
8 00:02:50.250 ⇒ 00:02:52.309 Uttam Kumaran: Where are you, in the world?
9 00:02:52.850 ⇒ 00:02:54.890 Selenge Tulga: No, I’m in Austin, Texas.
10 00:02:54.890 ⇒ 00:03:00.759 Uttam Kumaran: Oh, okay, me too. Okay, great, yeah. It’s, it’s… yeah, it’s weirdly still so cool.
11 00:03:01.270 ⇒ 00:03:08.710 Selenge Tulga: Yeah, it is last… yeah, I think it’s last, weekend. I went to the, the day-to-day Texas conference.
12 00:03:08.710 ⇒ 00:03:09.769 Uttam Kumaran: Yeah, yeah, yeah.
13 00:03:10.010 ⇒ 00:03:12.320 Selenge Tulga: It was a breezy weekend, oh god.
14 00:03:12.500 ⇒ 00:03:15.380 Uttam Kumaran: I know, this was, like, what, 2 weekends ago? Yeah, it was, like.
15 00:03:16.420 ⇒ 00:03:20.600 Uttam Kumaran: I’ve been here, like, maybe almost 4 years, and this is the coldest.
16 00:03:20.970 ⇒ 00:03:25.350 Selenge Tulga: Yeah, this is the coldest end. And I, moved from New York State.
17 00:03:25.590 ⇒ 00:03:27.819 Selenge Tulga: Okay, me too. I moved from New York City.
18 00:03:28.150 ⇒ 00:03:30.670 Selenge Tulga: I moved from the Rochester, New York, right?
19 00:03:31.000 ⇒ 00:03:33.430 Uttam Kumaran: I thought it was so poor cold there.
20 00:03:33.430 ⇒ 00:03:47.219 Selenge Tulga: Yeah, it is… but they said, oh, it’s a freezing season, and I’m just… are you joking me? But it was freezing, right? But there’s no snow, it is just… it is so slippery, yeah. It’s no joke, it was a freezing, yeah.
21 00:03:47.400 ⇒ 00:04:05.920 Uttam Kumaran: Yeah. Well, amazing. Thank you so much for taking the time. I appreciate, I appreciate your interest, and yeah, I’m super happy to tell you more about Brainforge, and excited to learn a bit about your background. Yeah, maybe I’ll just give you a brief intro.
22 00:04:06.360 ⇒ 00:04:14.039 Uttam Kumaran: My name is Utah, and I run Brainforge. We’re a data and AI consultancy. There’s about 20-ish people on the team now.
23 00:04:14.550 ⇒ 00:04:29.409 Uttam Kumaran: We’ve worked with almost 30 or 40 different companies, implementing various parts of the data stack. So, my background is in data engineering. I worked as a data engineer for a number of years. I led some data teams, then I’ve worked as a…
24 00:04:29.440 ⇒ 00:04:33.830 Uttam Kumaran: leading product at a data company before starting, Brainforge.
25 00:04:33.850 ⇒ 00:04:52.939 Uttam Kumaran: And my business partner, Robert, really his background comes mostly from strategy, analytics, product analytics. So, kind of both of us combined to kind of create, Brainforge, where we’re almost like a, like a full-stack, you know, data consultancy. So, we do everything from…
26 00:04:53.180 ⇒ 00:05:04.999 Uttam Kumaran: warehousing, ETL, modeling, BI, all the way up to strategy, recommendations and implementation in some places of those recommendations.
27 00:05:05.110 ⇒ 00:05:22.030 Uttam Kumaran: Most of our team is, you know, engineers in one form or another, and we built the company, leveraging AI really, really heavily. And so, in the last, you know, year and a half, we’ve actually been able to go to market with a lot of what we’ve learned in building an AI.
28 00:05:22.030 ⇒ 00:05:22.390 Selenge Tulga: company.
29 00:05:22.390 ⇒ 00:05:38.110 Uttam Kumaran: and actually start selling that as a service. And so we actually have a few clients additional to a few data clients that we’re actually building agentic systems for, Agentic workflows, AI applications, and then more, increasingly, we’re finding people that ask for both.
30 00:05:38.110 ⇒ 00:05:44.139 Uttam Kumaran: They want us to build a data stack, and then they want us to be able to query that through natural language.
31 00:05:45.630 ⇒ 00:05:59.540 Uttam Kumaran: And so that’s… that’s sort of the company right now. We typically work… most of the clients we work for are, like, mid-market or enterprise, so it’s usually a minimum of, like, $20 million in revenue up to… we have a few companies that we’re working for that are a few hundred million in revenue.
32 00:06:00.060 ⇒ 00:06:04.020 Uttam Kumaran: And so, these are oftentimes folks that
33 00:06:04.020 ⇒ 00:06:14.800 Uttam Kumaran: They themselves don’t have the internal talent, or they don’t have the internal expertise to build a stack and do that in a reasonable amount of time. And it’s all companies that want to grow.
34 00:06:14.800 ⇒ 00:06:24.490 Uttam Kumaran: So we’re not typically coming into situations where they… they’re cutting costs, or they’re, like, everything’s going great. Usually there’s some issue preventing them from growing.
35 00:06:24.490 ⇒ 00:06:27.390 Selenge Tulga: And for us, that’s data and AI.
36 00:06:27.390 ⇒ 00:06:28.340 Uttam Kumaran: And so, if…
37 00:06:28.340 ⇒ 00:06:28.960 Selenge Tulga: Hmm.
38 00:06:28.960 ⇒ 00:06:38.490 Uttam Kumaran: There’s a whole host of things that you need to do to grow a company, but if data and AI is the thing that they need, we can totally serve that.
39 00:06:38.900 ⇒ 00:06:40.930 Selenge Tulga: But yeah, that’s, like, a bit of my background.
40 00:06:40.930 ⇒ 00:06:50.680 Uttam Kumaran: It’s been a crazy, like, two and a half years building the company. We’re, like, a completely bootstrap company, so we’ve, like, slowly built our way up,
41 00:06:51.110 ⇒ 00:06:58.750 Uttam Kumaran: But yeah, that’s… I would love to hear a little bit about what interested you in applying, and your background, and yeah, please feel free.
42 00:06:59.010 ⇒ 00:06:59.910 Selenge Tulga: Yeah.
43 00:07:00.380 ⇒ 00:07:03.039 Selenge Tulga: It is the first time, I think…
44 00:07:03.750 ⇒ 00:07:17.309 Selenge Tulga: you know the band fake, correct, Ben? It is… here, before that, I worked with the band as well, as a contractor, and I asked, about, oh, I need a job, and do you have any freelance, and…
45 00:07:17.310 ⇒ 00:07:24.080 Selenge Tulga: He said, oh, I don’t have any job for now, but I will tell you. And after that, he forwarded me your… the posting, and yeah.
46 00:07:24.080 ⇒ 00:07:24.670 Uttam Kumaran: Oh, great.
47 00:07:24.670 ⇒ 00:07:35.939 Selenge Tulga: It is, yeah, I’m a data engineer, and the 70 years of, I said, industry experience, because it is… I began my career as a software engineer at a real big company.
48 00:07:36.370 ⇒ 00:07:50.660 Selenge Tulga: And, there, I have a strong recommendation in the large scales, and after that, because our, companies, it is just, Oracle, and it is just always keep it transaction, right?
49 00:07:51.060 ⇒ 00:08:05.000 Selenge Tulga: It is so painful to join this old thing, and all, because it is, 6… 16,000 employees, but we have just 6, software engineers, and we just need to do everything.
50 00:08:05.000 ⇒ 00:08:05.690 Uttam Kumaran: Yeah, yeah, yeah.
51 00:08:05.690 ⇒ 00:08:10.039 Selenge Tulga: They build database to meet the clients and all things, and…
52 00:08:10.140 ⇒ 00:08:24.490 Selenge Tulga: But if there need any other reports, we need to build in our… sometimes it’s a Laravel framework, sometimes it’s a deal frame, sometimes it’s a JavaSpring. It is so hard to build
53 00:08:24.500 ⇒ 00:08:39.439 Selenge Tulga: another reports again and again. They just need to change one column, we need to change in the code, right? And this is the first time, oh, okay, we need to figure that out. And this is the reason why I translated the data engineering
54 00:08:39.980 ⇒ 00:08:57.000 Selenge Tulga: And, yeah, I built… personally, my intention was, okay, just build… just start to use the Power BI to… to build the reports. Then I need to clean the data to warehouses to work with that, right? It is… yeah…
55 00:08:57.160 ⇒ 00:09:14.379 Selenge Tulga: And after that, I work at… I worked there 6 years, and 3 years half as a software engineer, and the founding data engineer, and I hired two junior data engineers. Then I… okay, I will pursue my way in data science.
56 00:09:14.380 ⇒ 00:09:17.050 Uttam Kumaran: And this is the reason why I came in New York.
57 00:09:17.250 ⇒ 00:09:17.770 Uttam Kumaran: Cool.
58 00:09:17.770 ⇒ 00:09:21.549 Selenge Tulga: And I finished my master’s degree in data science.
59 00:09:21.780 ⇒ 00:09:32.850 Selenge Tulga: and the machine learning, and… but I realized, okay, that there is still foundation, right? Because a lot of the people can work with the kind of the fancy.
60 00:09:32.850 ⇒ 00:09:42.780 Selenge Tulga: the ML models and the data sets, but they can’t clean the data, they don’t understand how the data system works, and in… and…
61 00:09:43.000 ⇒ 00:09:57.569 Selenge Tulga: Also, I, worked in… as a data science intern in the motion lab, but it was the pure data engineering work, because they need to build their warehouses from the sensors, and
62 00:09:57.870 ⇒ 00:10:04.450 Selenge Tulga: even I pursued a data… even I pursued data science, it’s all turned to the data engineering side.
63 00:10:05.250 ⇒ 00:10:23.630 Selenge Tulga: Yeah, and in my most recent role, before that, okay, before that, I also attended, Zach Wilson’s data engineering bootcamp. I just wanted to see how my data engineering experience is different from his expertise. I learned a ton, and I’ll also,
64 00:10:24.190 ⇒ 00:10:26.460 Selenge Tulga: Expanded my networking there.
65 00:10:26.460 ⇒ 00:10:27.130 Uttam Kumaran: Cool.
66 00:10:27.260 ⇒ 00:10:45.110 Selenge Tulga: And I, found my first role through this bootcamp, and it was also a consulting company, but more focus is BI side. They are, focused on more Power BI side. They work with, multiple companies and how to automate their dashboard
67 00:10:45.110 ⇒ 00:10:47.639 Selenge Tulga: using a Power BI, but…
68 00:10:47.640 ⇒ 00:10:54.869 Selenge Tulga: the core, most painful point was they just uploaded all the data into Power BI,
69 00:10:55.600 ⇒ 00:11:06.310 Selenge Tulga: And they did all the data cleaning, data transformation in the Power BI. And if they need another Power BI, they just need to upload again, and they create metrics again.
70 00:11:06.310 ⇒ 00:11:07.210 Uttam Kumaran: Yeah, yeah.
71 00:11:07.500 ⇒ 00:11:10.559 Selenge Tulga: Power BI is… it is not sustainable, right?
72 00:11:10.560 ⇒ 00:11:10.880 Uttam Kumaran: No.
73 00:11:10.880 ⇒ 00:11:15.419 Selenge Tulga: You don’t know where the tin changes. We need to check all measures.
74 00:11:15.420 ⇒ 00:11:16.280 Uttam Kumaran: Yes. And…
75 00:11:16.280 ⇒ 00:11:27.910 Selenge Tulga: And this is the, end. I introduced the MT, okay, it’s a snowflake, it is a DPT, and you can see the… all the histories here. It is like a git, and.
76 00:11:27.910 ⇒ 00:11:28.440 Uttam Kumaran: Yeah, yeah, yeah.
77 00:11:28.440 ⇒ 00:11:35.259 Selenge Tulga: They’re all things here, and… now it is UGG, and I think I spent…
78 00:11:35.660 ⇒ 00:11:39.800 Selenge Tulga: Almost 4 months to build all these,
79 00:11:40.290 ⇒ 00:11:46.799 Selenge Tulga: the warehouses for the multiple clients, now their… their work is so smooth. Okay, okay, great. Yeah.
80 00:11:46.900 ⇒ 00:11:47.480 Selenge Tulga: Yeah.
81 00:11:47.730 ⇒ 00:11:58.979 Uttam Kumaran: So tell me about, like, yeah, tell me about what you’re looking for next, like, based on your experiences, like, what have… what has been giving you, like, a lot of energy, like, what do you want to do next, and then…
82 00:11:59.110 ⇒ 00:12:06.189 Uttam Kumaran: My follow-on question would be, like, where are you still trying to learn, you know? Like, where do you want your career to go?
83 00:12:06.470 ⇒ 00:12:18.880 Selenge Tulga: Yeah, it is, I want… I’m looking for a team I feel I can contribute meaningfully, right? I want to, I have an impact on my team and a product, and…
84 00:12:19.070 ⇒ 00:12:20.120 Selenge Tulga: It is…
85 00:12:20.360 ⇒ 00:12:27.279 Selenge Tulga: Honestly, I’m not pursuing this big-sized company and afraid of the laid off or something like that, right?
86 00:12:27.280 ⇒ 00:12:29.140 Uttam Kumaran: I agree, I agree.
87 00:12:29.140 ⇒ 00:12:29.999 Selenge Tulga: Yeah, it is…
88 00:12:30.000 ⇒ 00:12:37.669 Uttam Kumaran: Well, one is, like, you get put on, like, such a small thing, and you have no, like, skill set that can go anywhere else, right?
89 00:12:37.670 ⇒ 00:12:38.240 Selenge Tulga: Yeah.
90 00:12:38.240 ⇒ 00:12:42.439 Uttam Kumaran: That’s how I felt when I was, like, leaving my last job. I was like.
91 00:12:42.870 ⇒ 00:12:59.110 Uttam Kumaran: what do I do? I either go back to a startup, and I was so tired, I don’t know if I could do that, and I was like, or I go to a big company, but it’s so boring, and yeah, I… back then, I didn’t think about how much layoffs, but it seems like it’s, like, every week, and…
92 00:13:00.100 ⇒ 00:13:00.750 Uttam Kumaran: I also…
93 00:13:01.240 ⇒ 00:13:08.039 Uttam Kumaran: a lot of the people that I beat from those big companies, they go to the big company, and then now they’re, like, lazy. They’re like…
94 00:13:08.040 ⇒ 00:13:10.959 Selenge Tulga: Yeah, same, because they’re just walking, yeah.
95 00:13:11.160 ⇒ 00:13:20.329 Selenge Tulga: Yeah, man. Because first, my… this railway company is… even if it’s a big company, I feel my impact, right, because we are just six engineers.
96 00:13:20.330 ⇒ 00:13:21.080 Uttam Kumaran: Yeah, definitely.
97 00:13:21.080 ⇒ 00:13:36.060 Selenge Tulga: you can… yeah, and after that, I also work with the band, and Ben is also… it is… consulting companies are beautiful. They are not target… sometimes it’s targeting big companies, but sometimes it’s the mid companies. Yeah. And my, recent,
98 00:13:36.220 ⇒ 00:13:56.330 Selenge Tulga: the project, we work with the, door construction company. They don’t have any… the software engineers or anything like that. They are just pure… the, door company. They are tracking the… the time data in a cookbook, Intuit cookbook, and, it is called the FieldWire CRM, they’re,
99 00:13:56.330 ⇒ 00:14:00.269 Selenge Tulga: And… all other data in a Google Sheet.
100 00:14:00.520 ⇒ 00:14:01.160 Uttam Kumaran: Yeah, yeah, yeah.
101 00:14:01.160 ⇒ 00:14:07.600 Selenge Tulga: And they are just doing financial, all these things in a Google Sheet, and there’s no…
102 00:14:08.090 ⇒ 00:14:11.629 Selenge Tulga: way to the higher the columns, they are, the most, they have.
103 00:14:11.630 ⇒ 00:14:12.060 Uttam Kumaran: Yeah.
104 00:14:12.060 ⇒ 00:14:19.390 Selenge Tulga: of the Google Sheets and the share, a lot of the people, right? And there, I, implemented all…
105 00:14:19.570 ⇒ 00:14:25.070 Selenge Tulga: They’re, they wanted to help from us, and we built a whole system for them.
106 00:14:25.520 ⇒ 00:14:28.960 Selenge Tulga: And this is so beautiful to see how their work is
107 00:14:29.290 ⇒ 00:14:41.079 Selenge Tulga: the improving, right? They… they don’t need these 15 different sheets, and and also, I use my software experience to build, the web app.
108 00:14:41.080 ⇒ 00:14:53.839 Selenge Tulga: to make changes, and they are now seeing all their data in a… looks like a Gantt chart, and they all track their, data in a one… the field, and if they need to change anything in a…
109 00:14:53.900 ⇒ 00:14:59.059 Selenge Tulga: the CRM, they just need to change in a using app, and it’s,
110 00:14:59.170 ⇒ 00:15:02.930 Selenge Tulga: using, API and the changes in a…
111 00:15:03.540 ⇒ 00:15:07.689 Selenge Tulga: feels like. It is so beautiful to see, and how my…
112 00:15:07.910 ⇒ 00:15:12.070 Selenge Tulga: Because I worked it alone for this project, just my manager and I,
113 00:15:12.300 ⇒ 00:15:18.900 Selenge Tulga: And just one people have to change the Twinnish, people’s work, right?
114 00:15:18.900 ⇒ 00:15:19.240 Uttam Kumaran: Yeah.
115 00:15:19.240 ⇒ 00:15:20.879 Selenge Tulga: So, the copy, and they can…
116 00:15:21.000 ⇒ 00:15:25.500 Selenge Tulga: Yeah, that’s beautiful. This is… that’s… that’s the…
117 00:15:25.690 ⇒ 00:15:32.329 Selenge Tulga: the thing, and I wanted to… the team, I can contribute meaningfully and feel the impact.
118 00:15:32.630 ⇒ 00:15:42.129 Uttam Kumaran: Yeah. Yeah, that’s… I think that’s really great. I mean, a lot of our companies, they’re bringing us on because they have an issue, but we’re contributing directly to them growing.
119 00:15:42.460 ⇒ 00:15:46.820 Uttam Kumaran: And again, even if it’s, like, we work with some companies that are, like, $100 million in revenue, but…
120 00:15:47.090 ⇒ 00:15:53.489 Uttam Kumaran: they’re still, like, they’re having the same basic problem. So it’s actually less about, like, are they big companies?
121 00:15:53.590 ⇒ 00:16:05.749 Uttam Kumaran: for us, we really value our work, and so in order for us to be able to afford to have great people on the team, I can’t go to this… we don’t… we no longer work with startups and small businesses because.
122 00:16:05.750 ⇒ 00:16:08.669 Selenge Tulga: it’s very difficult. They’re often really, really…
123 00:16:08.670 ⇒ 00:16:25.850 Uttam Kumaran: like, they just don’t have a lot of money, and they’re oftentimes, like, one month they’ll be really excited, next month they’re like, our business is on fire, we need to pause. And so we grew our business in a way to try to go higher in the market, but what we’re finding is that the problems are the same, or actually easier.
124 00:16:25.850 ⇒ 00:16:26.389 Selenge Tulga: For the company.
125 00:16:26.390 ⇒ 00:16:31.449 Uttam Kumaran: Because at a big company, it’s a lot of, like, politics, and it’s a lot of, like.
126 00:16:31.590 ⇒ 00:16:36.450 Uttam Kumaran: they just don’t even… nobody cares. And so when we come in, we care so much that we just…
127 00:16:36.830 ⇒ 00:16:46.670 Uttam Kumaran: push forward, you know, really, really fast. And we’ve deployed… yeah, so we do a lot of, like, Snowflake, dbt, Omni,
128 00:16:46.780 ⇒ 00:17:06.060 Uttam Kumaran: you know, we do a lot of Amplitude mixed panel product analytics work, we use Mother Duck for a lot of things, we use all the ETL tools, so when we come in, we make recommendations on every part of the stack, and we bring in the tools in order to do the job, typically. And it’s been awesome. It’s been really, really impactful on all the clients that we’ve…
129 00:17:06.119 ⇒ 00:17:07.320 Uttam Kumaran: We’ve worked for.
130 00:17:07.650 ⇒ 00:17:11.760 Selenge Tulga: Yeah, because most of… I… it’s just my perspective, but…
131 00:17:11.980 ⇒ 00:17:17.880 Selenge Tulga: After work with this multiple… a lot of the clients, they… I just feel they don’t know what they want.
132 00:17:17.880 ⇒ 00:17:18.570 Uttam Kumaran: No, they don’t.
133 00:17:18.579 ⇒ 00:17:20.009 Selenge Tulga: We just need to find it.
134 00:17:20.010 ⇒ 00:17:20.819 Uttam Kumaran: Yes, so…
135 00:17:20.829 ⇒ 00:17:21.219 Selenge Tulga: Yeah.
136 00:17:21.220 ⇒ 00:17:23.799 Uttam Kumaran: So ultimately, they need to trust you.
137 00:17:24.020 ⇒ 00:17:26.220 Selenge Tulga: Because we know what we’re doing.
138 00:17:26.260 ⇒ 00:17:30.539 Uttam Kumaran: And so the problem is actually not getting them to understand everything we’re doing.
139 00:17:30.540 ⇒ 00:17:31.040 Selenge Tulga: I think it’s actually.
140 00:17:31.040 ⇒ 00:17:32.449 Uttam Kumaran: Getting them to trust us.
141 00:17:32.520 ⇒ 00:17:51.970 Uttam Kumaran: Part of the way you build trust is you show your expertise, you show that you care, right? And we’re not just, like, camera-off engineers. Like, everybody in our company is super, super nice. It’s FaceTime with the client. Like, nobody… we don’t do anything that’s, like, dev shop or any, like, staff augmentation work. It’s all, like, relationship building. And then.
142 00:17:51.970 ⇒ 00:17:52.560 Selenge Tulga: They tried.
143 00:17:52.560 ⇒ 00:18:08.929 Uttam Kumaran: us. They know that we’re really, really good at what we do, and that we’re gonna move the ball forward. And then, yeah, it’s a discussion, it’s a partnership between us and them. It’s not, you guys go do this, it’s like, we come to the table with ideas, they come to the table with ideas, we agree, and then we all go do our job, you know?
144 00:18:08.930 ⇒ 00:18:09.959 Selenge Tulga: That’s it for you.
145 00:18:09.960 ⇒ 00:18:10.550 Uttam Kumaran: Yeah.
146 00:18:10.650 ⇒ 00:18:16.210 Uttam Kumaran: Any other questions I can answer, or any… anything that, you’re interested in hearing about?
147 00:18:16.210 ⇒ 00:18:18.310 Selenge Tulga: Yeah, I just now,
148 00:18:18.710 ⇒ 00:18:24.990 Selenge Tulga: It is 20 people, how many data engineers do? It is… it is, do you have separate teams, or…
149 00:18:25.570 ⇒ 00:18:29.740 Uttam Kumaran: Yeah, so we have 3… we are… our company’s broken up into 3 services, so we have…
150 00:18:30.580 ⇒ 00:18:35.849 Uttam Kumaran: our data analytics service. We have our data service, we have our strategy analytics service.
151 00:18:36.360 ⇒ 00:18:42.830 Uttam Kumaran: and we have our AI service. So, data service includes DE and analytic engineers.
152 00:18:42.830 ⇒ 00:18:43.790 Selenge Tulga: Mmm.
153 00:18:43.830 ⇒ 00:18:48.459 Uttam Kumaran: So that’s… so on that team, there’s 5 of us right now.
154 00:18:48.620 ⇒ 00:18:49.620 Selenge Tulga: Mmm…
155 00:18:49.620 ⇒ 00:18:50.380 Uttam Kumaran: On…
156 00:18:50.380 ⇒ 00:18:56.499 Selenge Tulga: difference this, D and analytic engineering roles? Because sometimes it’s just… they look the same, right?
157 00:18:56.500 ⇒ 00:19:00.830 Uttam Kumaran: Yeah, so most of our DEs also do AE work.
158 00:19:01.170 ⇒ 00:19:06.780 Uttam Kumaran: For the most part, you’ll see in our company, people are, like, Bridging the gap into
159 00:19:07.220 ⇒ 00:19:10.960 Uttam Kumaran: different disciplines, typically. Just because, again, we try to get great people.
160 00:19:11.350 ⇒ 00:19:21.259 Uttam Kumaran: oftentimes, people want to grow. Like, they’re like, I’m doing DE work, I want to learn AE stuff. So, we have… so, on the… on that side, most of the people are kind of within both.
161 00:19:21.440 ⇒ 00:19:27.080 Uttam Kumaran: So, but the DE side is all… ETL setup, data warehouse setup.
162 00:19:27.630 ⇒ 00:19:27.980 Selenge Tulga: Yeah. Interesting.
163 00:19:27.980 ⇒ 00:19:34.530 Uttam Kumaran: ingestions, like anything around ingestion, setting up warehouse, setting up dbt structure.
164 00:19:34.640 ⇒ 00:19:37.230 Uttam Kumaran: We do a lot of work with, like, okay, we have to go
165 00:19:37.340 ⇒ 00:19:42.990 Uttam Kumaran: We have to go call external APIs and, like, figure out structure, and then AE work is all dbt modeling.
166 00:19:42.990 ⇒ 00:19:44.100 Selenge Tulga: And Mark’s…
167 00:19:44.100 ⇒ 00:19:45.350 Uttam Kumaran: development, yeah.
168 00:19:46.160 ⇒ 00:19:50.300 Selenge Tulga: It is, omni’s, ingestion.
169 00:19:50.770 ⇒ 00:19:52.460 Uttam Kumaran: Omni is on the BI side.
170 00:19:52.590 ⇒ 00:20:01.369 Selenge Tulga: Oh, BI side, right? I have an experience with the Tableau in the, is it more like Tableau or Paul BI? It’s more like Looker.
171 00:20:01.700 ⇒ 00:20:02.780 Selenge Tulga: Looker.
172 00:20:02.780 ⇒ 00:20:04.700 Uttam Kumaran: Yeah, it’s kind of similar to Looker, yeah.
173 00:20:04.700 ⇒ 00:20:08.060 Selenge Tulga: Mmm, got it. But it’s, yeah, it’s similar.
174 00:20:09.020 ⇒ 00:20:14.010 Selenge Tulga: What, how heavily use AI in your work? It is.
175 00:20:14.010 ⇒ 00:20:23.300 Uttam Kumaran: Yeah, everybody in the company uses AI, for as much as humanly possible. We have really, really pushed.
176 00:20:23.480 ⇒ 00:20:26.839 Selenge Tulga: the adoption of AI in the business, and I built the company.
177 00:20:26.840 ⇒ 00:20:27.480 Uttam Kumaran: company.
178 00:20:27.860 ⇒ 00:20:34.690 Uttam Kumaran: mostly this fast because of AI. Without that, I don’t know how long it would have taken, or if it would have… if it would have worked.
179 00:20:36.140 ⇒ 00:20:51.889 Uttam Kumaran: So, this is not only engineers, like, everybody in the team uses Cursor, for engineering work, but our sales team is using Cursor and various AI tools to do their job, operations is using various AI tools,
180 00:20:52.140 ⇒ 00:20:59.710 Uttam Kumaran: Everybody in the company has different ways of using AI to advance their job, and we also built an internal platform.
181 00:20:59.850 ⇒ 00:21:14.309 Uttam Kumaran: So we have an internal platform that we built that’s, like, has a ton of AI features that helps everybody from delivery people to, again, sales, operations, go-to-market, legal, that they can all do things from…
182 00:21:14.750 ⇒ 00:21:30.789 Uttam Kumaran: summarizing transcripts to modules to do different activities. So, it’s sort of, like, no, it’s actually more of, like, a requirement now that if you work here, you have to use AI to do your job. There’s not, like, a debate, at this company about that, which is great, because…
183 00:21:30.790 ⇒ 00:21:32.000 Selenge Tulga: Yeah, it’s great.
184 00:21:32.000 ⇒ 00:21:41.870 Uttam Kumaran: it’s… a lot of companies we go to, they’re still, like, worried, or, like, they don’t know how to use it. We’re, like, the opposite. Whatever the opposite of that is.
185 00:21:41.870 ⇒ 00:21:42.420 Selenge Tulga: Yes!
186 00:21:42.420 ⇒ 00:21:47.910 Uttam Kumaran: We’re pushing, really pushing the limits on what’s possible in every single part of the organization.
187 00:21:48.340 ⇒ 00:21:55.770 Selenge Tulga: Yeah, it is just remembering, if I’m stuck in some problems, just need to browsing Stack Overflow, and finding similar.
188 00:21:55.770 ⇒ 00:21:56.220 Uttam Kumaran: Yeah.
189 00:21:56.220 ⇒ 00:21:57.360 Selenge Tulga: Just to fix that problem.
190 00:21:57.360 ⇒ 00:21:57.820 Uttam Kumaran: Yeah.
191 00:21:59.570 ⇒ 00:22:02.100 Uttam Kumaran: But what that allows us to do is deliver faster for our clients.
192 00:22:02.100 ⇒ 00:22:03.530 Selenge Tulga: Yeah, so fast, yeah.
193 00:22:03.530 ⇒ 00:22:05.410 Uttam Kumaran: deliver faster, deliver more, and that actually.
194 00:22:05.410 ⇒ 00:22:05.880 Selenge Tulga: actually means…
195 00:22:05.880 ⇒ 00:22:08.730 Uttam Kumaran: We can… We’re better priced, we’re a better product.
196 00:22:09.450 ⇒ 00:22:27.560 Uttam Kumaran: other consultancies, and so that’s why, that’s how we compete, is that we don’t use… we don’t, like, one-shot things and just throw it over, but it’s… we all… we’re all experienced people, so we can use AI in the ways… in the pieces that we knew were gonna just take, like, a couple hours. Now you can use AI to help you do that, you know?
197 00:22:28.500 ⇒ 00:22:32.430 Selenge Tulga: Yeah, we don’t need to stop, just ask. Just ask the right questions.
198 00:22:32.430 ⇒ 00:22:33.150 Uttam Kumaran: Yes.
199 00:22:33.790 ⇒ 00:22:35.530 Selenge Tulga: is… Listen.
200 00:22:35.880 ⇒ 00:22:39.380 Selenge Tulga: Yeah. Do you have, other questions for me?
201 00:22:40.100 ⇒ 00:22:51.380 Uttam Kumaran: No, I feel like I really… I like your background. I think you kind of get the story of, like, the things that we’re trying to do. I feel like I understand sort of what type of impact you’re trying to make.
202 00:22:51.380 ⇒ 00:22:56.009 Selenge Tulga: You know, next steps on our side is I would love for you to sort of talk to.
203 00:22:56.010 ⇒ 00:23:01.500 Uttam Kumaran: Someone on our data engineering team, and sort of get into our more formal process for interview.
204 00:23:02.710 ⇒ 00:23:17.240 Uttam Kumaran: it’s a, like, a three-step interview. You know, there’s an initial call with Awash, who leads our data service, another person on the data team, and then there’s typically, like, a final presentation, you know, exercise.
205 00:23:18.360 ⇒ 00:23:23.429 Uttam Kumaran: But I would love to sort of move you forward and, you know, have you go talk to Awash. Yeah.
206 00:23:23.430 ⇒ 00:23:27.890 Selenge Tulga: Yeah, thing. It is… do I… what should I need to expect? It is…
207 00:23:28.040 ⇒ 00:23:31.610 Selenge Tulga: How about… is it technical? Do I need to…
208 00:23:32.330 ⇒ 00:23:33.680 Uttam Kumaran: Yeah, so.
209 00:23:33.680 ⇒ 00:23:37.299 Selenge Tulga: SQL problem, or Python, what it looks like?
210 00:23:37.420 ⇒ 00:23:47.589 Uttam Kumaran: Yeah, so for both of those calls with our team, it will be partly, like, behavioral… people will… they will ask you about, like, what your technical expertise is.
211 00:23:47.590 ⇒ 00:23:48.060 Selenge Tulga: But it’s not…
212 00:23:48.060 ⇒ 00:24:04.090 Uttam Kumaran: like an exercise. The last interview is an exercise. I can have Rico send some details, in an email from my team about… about what that contains, but it’s a simple SQL exercise. It shows a little… it’s a little bit… it’s both, like, technical about
213 00:24:04.090 ⇒ 00:24:11.570 Uttam Kumaran: Understanding data structures, but it’s actually a lot about, like, your presentation skills and your ability to communicate.
214 00:24:11.570 ⇒ 00:24:13.619 Selenge Tulga: You know, what it is you’re doing, cause…
215 00:24:13.620 ⇒ 00:24:20.480 Uttam Kumaran: 50% of our job is actually just, like, communication. We do a lot of data work, but it’s actually really important for us to get
216 00:24:20.810 ⇒ 00:24:25.059 Uttam Kumaran: across to the client in an organized manner on, like, what we’re doing, right? So…
217 00:24:25.060 ⇒ 00:24:27.459 Selenge Tulga: That’s part of, like, what the exercise…
218 00:24:27.460 ⇒ 00:24:28.130 Uttam Kumaran: for.
219 00:24:29.140 ⇒ 00:24:35.719 Selenge Tulga: Yeah, now, this AI need to push us to more communication, right? We need to ask the right questions, we need to…
220 00:24:35.720 ⇒ 00:24:38.269 Uttam Kumaran: Yes. We need to understand.
221 00:24:38.270 ⇒ 00:24:45.589 Selenge Tulga: the root of the problems, and then can ask the right questions. Yes. And if you are wrong there, just… boom.
222 00:24:45.590 ⇒ 00:24:46.210 Uttam Kumaran: Yes.
223 00:24:46.210 ⇒ 00:24:47.090 Selenge Tulga: all wrong.
224 00:24:47.090 ⇒ 00:24:47.620 Uttam Kumaran: Yeah.
225 00:24:47.620 ⇒ 00:24:50.759 Selenge Tulga: Yeah, got it, that’s… yeah, that sounds nice.
226 00:24:51.040 ⇒ 00:24:51.820 Uttam Kumaran: Okay.
227 00:24:51.820 ⇒ 00:24:52.900 Selenge Tulga: Thank you. Perfect.
228 00:24:52.900 ⇒ 00:25:00.219 Uttam Kumaran: Yeah, if you have any other questions, please feel free to send me a note on LinkedIn, or send me an email, more than happy to answer anything, so…
229 00:25:00.220 ⇒ 00:25:02.919 Selenge Tulga: Yeah, thank you. Okay. You thumb, right? Is it…
230 00:25:02.920 ⇒ 00:25:04.780 Uttam Kumaran: Yes, and how do you pronounce your name?
231 00:25:04.920 ⇒ 00:25:05.810 Selenge Tulga: Celine.
232 00:25:06.650 ⇒ 00:25:09.660 Uttam Kumaran: Okay, awesome. It’s really great to meet you. Yeah, talk to you soon.
233 00:25:09.660 ⇒ 00:25:10.729 Selenge Tulga: Have a great day!
234 00:25:10.730 ⇒ 00:25:11.390 Uttam Kumaran: You too, bye.
235 00:25:11.390 ⇒ 00:25:11.970 Selenge Tulga: Bye.