Meeting Title: Brainforge Interview w- Uttam Date: 2026-02-19 Meeting participants: Godwin Ekainu, Uttam Kumaran
WEBVTT
1 00:01:53.080 ⇒ 00:01:54.080 Uttam Kumaran: Aye.
2 00:01:55.270 ⇒ 00:01:56.850 Godwin Ekainu: Hi. How are you, Tom?
3 00:01:57.440 ⇒ 00:01:58.730 Uttam Kumaran: Hi, how are you?
4 00:01:58.940 ⇒ 00:02:00.520 Godwin Ekainu: I’m doing good, how are you?
5 00:02:00.880 ⇒ 00:02:13.199 Uttam Kumaran: Hey, good. I’m sorry, I’m just gonna be off video. I am, I’m actually on vacation today, but I always feel very… I feel very bad rescheduling interviews, because I know you’re taking time out of your day, so…
6 00:02:13.200 ⇒ 00:02:24.169 Uttam Kumaran: I didn’t… I… you know… No, no, no, dude, you’re not sorry. I’m sorry, because in case it’s noisy, but, if you don’t mind, I’ll just be on video, but I wanted to say hi.
7 00:02:24.240 ⇒ 00:02:28.170 Uttam Kumaran: So you, you know, you know Demolade?
8 00:02:28.170 ⇒ 00:02:31.709 Godwin Ekainu: Yeah, yeah, I know, I’ve known the Naddy for about 5 years now.
9 00:02:31.960 ⇒ 00:02:33.880 Uttam Kumaran: Tell me about it, how do you know him?
10 00:02:34.000 ⇒ 00:02:36.940 Godwin Ekainu: So, basically, we met…
11 00:02:37.220 ⇒ 00:02:42.549 Godwin Ekainu: We said that we… we met at a hub, in our local community in Kaduna.
12 00:02:42.730 ⇒ 00:02:44.000 Godwin Ekainu: I think that was around.
13 00:02:44.000 ⇒ 00:02:45.000 Uttam Kumaran: Oh, nice.
14 00:02:45.000 ⇒ 00:02:53.720 Godwin Ekainu: I can’t really remember, so it’s called CoLab. I think about then, when I joined the community, we were doing data science-ish stuff.
15 00:02:53.830 ⇒ 00:03:09.359 Godwin Ekainu: Before then went into the analytics field, and later on, I went into the data engineering field. But we met at Colab, and Colab is a community of tech enthusiasts that’s in a city called Kadna in Nigeria, and…
16 00:03:09.470 ⇒ 00:03:13.220 Godwin Ekainu: We’ve known each other since then, so we are part of this,
17 00:03:13.420 ⇒ 00:03:22.930 Godwin Ekainu: group of, people who started out as data scientists, trying to get into tech and all, before everyone found their separate route, yeah.
18 00:03:23.890 ⇒ 00:03:25.020 Uttam Kumaran: Amazing.
19 00:03:25.710 ⇒ 00:03:28.660 Uttam Kumaran: How do you… and how do you… how do you pronounce your name again?
20 00:03:28.820 ⇒ 00:03:29.680 Godwin Ekainu: Godwin.
21 00:03:29.680 ⇒ 00:03:35.389 Uttam Kumaran: God bless. Beautiful name. It’s an awesome name. I’ve never, never seen it before.
22 00:03:35.390 ⇒ 00:03:36.680 Godwin Ekainu: Really?
23 00:03:36.680 ⇒ 00:03:39.800 Uttam Kumaran: Yeah, I don’t know, maybe you’ve never seen my name before either, so…
24 00:03:39.930 ⇒ 00:03:42.059 Godwin Ekainu: Yeah. At least two of us.
25 00:03:43.150 ⇒ 00:03:44.969 Godwin Ekainu: That’s true. That’s true.
26 00:03:44.970 ⇒ 00:03:53.020 Uttam Kumaran: Well, dude, I really appreciate you taking the time and, you know, submitting an application, and I’m so, so excited to chat today.
27 00:03:53.050 ⇒ 00:04:05.749 Uttam Kumaran: Tell me what Demolade has told you about the company, and I don’t, you know, of course, more than happy to repeat, but yeah, I would, like, tell me what he’s told you, and I can fill in some blanks, and of course, you know.
28 00:04:05.900 ⇒ 00:04:08.559 Uttam Kumaran: I would love to hear about your background after that.
29 00:04:09.180 ⇒ 00:04:15.269 Godwin Ekainu: Okay, so I didn’t really ask him much about the company, because I had to do more research about the company.
30 00:04:15.400 ⇒ 00:04:21.439 Godwin Ekainu: I know Demilh is a very busy person, so I didn’t want to bore him with more quite… with plenty questions.
31 00:04:21.470 ⇒ 00:04:41.080 Godwin Ekainu: But, based on the little, few I did… few research I did myself, and due to questions I asked them that day, my brain forge basically is a, consulting company, based in the US, but you have teams located across the globe, and you kind of help, businesses, in the U.S.
32 00:04:41.230 ⇒ 00:04:50.189 Godwin Ekainu: With your data and AI needs, so things like data engineering, analytics engineering, AI workflow automation, and AI,
33 00:04:50.430 ⇒ 00:05:02.830 Godwin Ekainu: kind of AI implementation for a company, helping them to grow. So you work basically on the consult… consultancy level, and based on my conversation with,
34 00:05:03.230 ⇒ 00:05:19.020 Godwin Ekainu: I wish on… last week, Friday, I think, yeah, we… he told me, basically, that, the team handles, individuals in the team handles, two… two, three projects at a… at a time, and the work, overlap with the U.S. hours.
35 00:05:19.190 ⇒ 00:05:32.150 Godwin Ekainu: So… you overlap around 4-5 hours for teams not based in the US, team members not based in the US, you overlap around 4-5 hours, so that you’re able to work with your colleagues across the company as you…
36 00:05:32.150 ⇒ 00:05:39.669 Godwin Ekainu: Consult with, meet with them, and also help them with, their needs based on specified hours or so.
37 00:05:39.910 ⇒ 00:05:43.439 Godwin Ekainu: Yeah, so, I think it’s pretty cool.
38 00:05:46.270 ⇒ 00:06:01.539 Uttam Kumaran: Amazing, I mean, I think you’re spot on. So, you know, you’re lucky you talked to Awash. Me and him, I sort of, do a lot of the core data engineering, so maybe I’ll kind of introduce myself. So, my name is Utam, I run Brainswords. My background is in data engineering.
39 00:06:01.650 ⇒ 00:06:20.210 Uttam Kumaran: I was a data engineer for a while. I worked sort of… kind of worked my way up the stack, you would say. Like, I worked in analytics engineering, then worked in BI, and then I led data teams, and then I started this business, you know, about two and a half years ago. So, our team is mostly engineers. I love engineers.
40 00:06:20.600 ⇒ 00:06:37.820 Uttam Kumaran: I wish my whole company could be engineers, but I will say I’m probably the most business person… business-y person here at the company now, which is kind of a shame, but I still get to do a lot of engineering work every day, and it’s been really great. I mean, you know, I really, really appreciate your background. I think
41 00:06:37.820 ⇒ 00:06:45.280 Uttam Kumaran: one, you know, your resume is great, like, I review a lot of resumes, and I think you did a really good job, being,
42 00:06:45.280 ⇒ 00:06:45.930 Godwin Ekainu: Thank you.
43 00:06:45.930 ⇒ 00:06:53.310 Uttam Kumaran: really being specific on the tools you use, and not worrying about if it’s, like, too technical.
44 00:06:53.810 ⇒ 00:07:08.890 Uttam Kumaran: I would say we don’t have any worry about that. Like, I actually want people to be more technical. Sometimes people… sometimes you read it, and you’re like, what am I even reading, you know? So, I think this is great. In particular, I think, you know, you mentioned you’re… you’re at this company, Queen Axe.
45 00:07:08.970 ⇒ 00:07:14.520 Uttam Kumaran: You know, a lot of the things that you mentioned there is stuff we do. GitHub, CICD.
46 00:07:14.860 ⇒ 00:07:20.779 Uttam Kumaran: dbt executions. You know, we use BigQuery, we use Redshift, we use Snowflake.
47 00:07:20.850 ⇒ 00:07:33.950 Uttam Kumaran: You know, we do a lot of, we use a couple of ETL providers, we also sometimes write our own pipelines, sometimes we do streaming, so I really feel like, you know, it’s sort of in line with a lot of what you said.
48 00:07:33.950 ⇒ 00:07:45.099 Uttam Kumaran: Of course, I think, you know, I’m sure part of what you worked on was also supporting customer-facing, you know, applications. I think for the most part in our business, it’s all internal reporting-related.
49 00:07:45.540 ⇒ 00:07:52.919 Uttam Kumaran: You know, so, in some sense, the SLAs are probably a little bit easier, meaning, like, we’re mainly supporting reporting workflows, right? So.
50 00:07:52.920 ⇒ 00:08:06.359 Uttam Kumaran: We’re taking data, landing it. I mean, a lot of what we’re doing is, like, snowflake, governance, right, so roles, permissions, and then… and then really just making sure that folks like Demolade, the other analytics engineers, can execute dbt.
51 00:08:06.650 ⇒ 00:08:07.570 Uttam Kumaran: Also…
52 00:08:07.700 ⇒ 00:08:24.020 Uttam Kumaran: You know, that’s a lot of what we do. I think, you know, additionally, on the AI side, there’s a lot of data engineering as well. So, probably something that Awash may not have mentioned is we’re actually also doing a lot of AI work. And AI work is great, but it’s super, super context-dependent, you know, I’m sure.
53 00:08:24.020 ⇒ 00:08:25.000 Godwin Ekainu: Yeah, yeah.
54 00:08:25.000 ⇒ 00:08:39.180 Uttam Kumaran: But a lot of those things are data engineering problems, like, which is great. It’s all things we’ve been doing always, anyways, right? And so, there’s a lot of opportunity there. I also saw that it’s great that you have used, you know, Clicked House as well.
55 00:08:39.580 ⇒ 00:08:44.700 Uttam Kumaran: Like, we have maybe one client on ClickHouse, but most of our stuff is on,
56 00:08:44.930 ⇒ 00:08:57.270 Uttam Kumaran: I was on Snowflake, I would say, but again, like, the thing that, you know, I just want to stress is, like, we… sometimes we walk into a client and we have an opportunity to dictate the tools.
57 00:08:57.490 ⇒ 00:09:00.160 Uttam Kumaran: many times we don’t, right? So it’s sort of like you…
58 00:09:00.160 ⇒ 00:09:00.640 Godwin Ekainu: Yeah.
59 00:09:00.640 ⇒ 00:09:14.609 Uttam Kumaran: we open the door, and we see what’s inside. So that’s, I think, you know, part of, like, what I think Demolade, you would say he really likes about the company, is, like, we get called when there’s a problem, and we walk in, and we figure it out, you know?
60 00:09:14.610 ⇒ 00:09:17.560 Godwin Ekainu: And so if that sounds something…
61 00:09:17.560 ⇒ 00:09:21.909 Uttam Kumaran: interesting to you, like, I think that’s… that’s a lot of what we do, yeah.
62 00:09:23.150 ⇒ 00:09:25.370 Godwin Ekainu: I mean, it’s very interesting,
63 00:09:25.840 ⇒ 00:09:36.349 Godwin Ekainu: Like you mentioned, my CV was… is very technical. Of course, I like to be a very technical person. I like to learn a lot, so I like to work on challenging stuff.
64 00:09:36.400 ⇒ 00:09:45.770 Godwin Ekainu: build great data product, data warehouse, and also… and that’s what drives my growth, that’s what drives my, career, basically.
65 00:09:45.770 ⇒ 00:10:01.760 Godwin Ekainu: So I like to do… I sometimes find it to where that, I… I know a lot of tools, and most times I reduce them because, it doesn’t… I don’t want to make it sound so fake. So it’s… when someone says, you know, redshift, you know, click out, they know snowflakes.
66 00:10:01.760 ⇒ 00:10:03.129 Uttam Kumaran: No, sorry.
67 00:10:03.130 ⇒ 00:10:04.440 Godwin Ekainu: calls, you call.
68 00:10:04.520 ⇒ 00:10:13.250 Godwin Ekainu: I mean, I was talking to a… I was speaking to one of my coach, recently, and I was like, I actually want a role where I can be able to do most of the things I actually practice.
69 00:10:13.250 ⇒ 00:10:29.439 Godwin Ekainu: amount to do roles where I’m hands-on data, and also hands-on with platform engineering and all, because those are, like, the two roles where I find myself really, really thriving. And they’re like, it’s going to be very difficult, but, I mean, you can find a small startup that will be able to
70 00:10:29.440 ⇒ 00:10:34.270 Godwin Ekainu: They can also… they can pitch to and can help support, basically, based on this role.
71 00:10:34.270 ⇒ 00:10:45.680 Godwin Ekainu: And I think that’s one of the reasons why I said to apply for Print Forge. It’s a consulting company, so you find the opportunity to kind of, work across various domains, across.
72 00:10:45.680 ⇒ 00:10:51.779 Uttam Kumaran: And your experience across a bunch of tools is actually valuable, right? Like, you know, I feel the same way, you gotta think about me, like.
73 00:10:51.920 ⇒ 00:11:07.350 Uttam Kumaran: It’s so… we work with so many tools, right? And yeah, it would be… it would be really tough to put it all on a resume, but that’s actually what makes us good at Brainforge, in that we all have a wide variety of experiences. And yes, maybe we never become experts in one tool, although, like.
74 00:11:07.410 ⇒ 00:11:13.340 Uttam Kumaran: I don’t know, I used Stokely for, like, 10 years, and I wish you, I’ve used it for a long time, so I would say we’re close to experts, but…
75 00:11:13.440 ⇒ 00:11:17.570 Uttam Kumaran: It’s the breadth that allows us to go into any situation and pattern match, right?
76 00:11:17.570 ⇒ 00:11:18.050 Godwin Ekainu: Yes.
77 00:11:18.050 ⇒ 00:11:32.489 Uttam Kumaran: Yeah, so… I guess my… one of my questions is, tell me about the teams that you worked on. Like, give me a sense of, like, what the structure was. Were you solo? Do you have other DEs? Are you working on, like, a full data, data, like, end-to-end data team?
78 00:11:33.740 ⇒ 00:11:49.409 Godwin Ekainu: So, depends on your company, to be honest. For example, at Quedax, when I joined Quedax, I… I was a solo data engineer, so the previous data engineer had left, and I was the one who came in to take over, basically, so I started at…
79 00:11:49.450 ⇒ 00:12:08.330 Godwin Ekainu: So I quit as a… I started out as a solo detail engineer for about 6 months or so. Currently I remember the timeline. So, while I came in, I helped fix some of their pipelines and helped redesign the dbt architecture, helped with their, ingestion, basically.
80 00:12:08.330 ⇒ 00:12:16.540 Godwin Ekainu: the… I… currently, we are now scaled up to about 3 data engineers on the team, and analysts across the organization.
81 00:12:16.540 ⇒ 00:12:31.019 Godwin Ekainu: And as data engineers on the team, we help support the analysts, with your, data… data requests, basically, so ingesting data from across various, APIs or… or sources, into the… bringing them into the warehouse.
82 00:12:31.020 ⇒ 00:12:39.219 Godwin Ekainu: then kind of build the models on top of dbt onto the U. We also use BigQuery. Credax, we use GCP mainly, so BigQuery,
83 00:12:39.700 ⇒ 00:12:53.750 Godwin Ekainu: TCP-based data tooling. So the team is basically work with Agile, so it’s not really structured… it’s structured in a way because, each member of the team knows what they’re supposed to do, so when a task comes in.
84 00:12:53.830 ⇒ 00:13:04.420 Godwin Ekainu: based on that specific domain, the person in charge already knows they are the ones who handle this. So, it depends on, kind of, requests.
85 00:13:04.420 ⇒ 00:13:13.909 Godwin Ekainu: So for, things related to platform, or things related to DBT, or things related to ETLing, I mainly handle that,
86 00:13:14.820 ⇒ 00:13:22.999 Godwin Ekainu: with, I mainly handle those requests, so when someone comes, they want to be the new data model, I help them with that. When someone comes on to help.
87 00:13:23.270 ⇒ 00:13:29.610 Godwin Ekainu: you ingest data from an API, also, or from an external source, I help them with that.
88 00:13:30.180 ⇒ 00:13:42.520 Godwin Ekainu: And also, when, trying to, architect a new pipeline, I mostly work with my team to design the architecture and try to see what’s the best path forward for that particular,
89 00:13:42.950 ⇒ 00:13:44.010 Godwin Ekainu: pipeline.
90 00:13:44.870 ⇒ 00:13:45.470 Uttam Kumaran: Okay.
91 00:13:45.860 ⇒ 00:13:57.689 Godwin Ekainu: Yeah, so that’s how I work currently at Credax. But I’ve also had roles where I’m all the solo data engineer, so working alone, building the architectures, designing the pipeline, building the pipeline.
92 00:13:57.690 ⇒ 00:14:06.069 Godwin Ekainu: maintain the pipeline, helping the team to scale based on their analytics needs. I think I’ll see later than, LexTeco.
93 00:14:06.230 ⇒ 00:14:09.650 Godwin Ekainu: And also, right?
94 00:14:10.020 ⇒ 00:14:17.970 Godwin Ekainu: I believe mainly the extra group, to be honest. Then other teams are usually very lean team, one or two data engineers, basically.
95 00:14:19.110 ⇒ 00:14:20.070 Uttam Kumaran: Okay, okay.
96 00:14:21.770 ⇒ 00:14:26.489 Uttam Kumaran: Okay, great. So, tell me a little bit about, like, what,
97 00:14:26.700 ⇒ 00:14:43.049 Uttam Kumaran: Yeah, you kind of mentioned, like, hey, I want to consider joining a consultancy so I can do multiple things. Like, tell me what’s next in your career. Like, do you want to go deeper on the technical side in one technology? Do you want to lead teams? Like, you know, if you could, if you could, wave a wand and…
98 00:14:43.110 ⇒ 00:14:49.620 Uttam Kumaran: the, you know, the next company you join is, like, hey, puts you in the right spot to grow, like, where would you want to grow?
99 00:14:50.390 ⇒ 00:14:55.080 Godwin Ekainu: So, currently, I would say I’m…
100 00:14:55.500 ⇒ 00:15:07.249 Godwin Ekainu: So, I want to be mainly on the technical side, so contributing to projects, helping the team with their business needs, help fix problems, learning a lot,
101 00:15:07.390 ⇒ 00:15:16.319 Godwin Ekainu: Well, I think that’s… maybe later in the future, I want to lead a team, but, I don’t think that’s currently… I’m at that stage to actually lead the team at the moment.
102 00:15:16.520 ⇒ 00:15:26.100 Godwin Ekainu: I actually want to just work, build my… RDP my technical skills, not only particular tool, but on a wide region tools, or,
103 00:15:26.300 ⇒ 00:15:37.650 Godwin Ekainu: It helps solve problems, basically, build data tools, build, data… create data solutions that helps business to scale, help the business to derive insights, basically.
104 00:15:37.650 ⇒ 00:15:50.259 Godwin Ekainu: And I, I, on the team part, or the lead on the team leader manager part, I think that might come later in the future, but currently, no, no, I’m… I’m not really looking at that at the moment.
105 00:15:51.460 ⇒ 00:15:52.560 Uttam Kumaran: Okay, professors.
106 00:15:54.370 ⇒ 00:15:55.150 Uttam Kumaran: Great.
107 00:15:55.340 ⇒ 00:15:56.949 Uttam Kumaran: Tell me what questions you have for me.
108 00:15:57.540 ⇒ 00:16:02.969 Godwin Ekainu: So, I think my questions are basically, you mentioned dew and…
109 00:16:03.190 ⇒ 00:16:11.450 Godwin Ekainu: I wish, handled detention, so… I noticed, Brainforce is actually a very small team, so how do you guys handle, projects around?
110 00:16:11.620 ⇒ 00:16:14.620 Godwin Ekainu: How do you guys, coordinate or collaborate?
111 00:16:15.950 ⇒ 00:16:22.569 Uttam Kumaran: Yeah, I guess it’s up to your definition, small, like, we have 25 people, so… It’s,
112 00:16:22.790 ⇒ 00:16:38.360 Uttam Kumaran: for me, it’s… I, you know, I started this company just on my laptop, so for me, that’s so many people. But, so, you know, we have people across data engineering, data modeling, BI, as well as product analytics, and then also on the AI side.
113 00:16:40.670 ⇒ 00:16:44.900 Uttam Kumaran: So, that’s what I wanted to…
114 00:16:45.610 ⇒ 00:16:50.179 Uttam Kumaran: That’s… that’s really, like, where I think we… we start to basically manage our teams with
115 00:16:50.300 ⇒ 00:16:58.479 Uttam Kumaran: several different types of people, and so we set goals for every single client and try to drive towards that. I think that’s the biggest thing that
116 00:17:00.020 ⇒ 00:17:11.339 Uttam Kumaran: that’s the biggest thing in the way we orchestrate. So, for one client, we have data engineers, we may have modelers, then as the client matures, we move into, AI and analytics, so…
117 00:17:13.780 ⇒ 00:17:14.849 Godwin Ekainu: That’s cool.
118 00:17:14.990 ⇒ 00:17:22.279 Godwin Ekainu: So, for projects, what would you say are, like, most challenging projects you’ve worked?
119 00:17:22.470 ⇒ 00:17:30.250 Godwin Ekainu: Help clients, or problems you have clients to solve, or you’ve helped clients to build, or…
120 00:17:30.360 ⇒ 00:17:34.969 Godwin Ekainu: basically, I’m just trying to get a sense of the kind of projects, we work…
121 00:17:35.450 ⇒ 00:17:39.179 Godwin Ekainu: you work on at Springfield for your clients.
122 00:17:41.570 ⇒ 00:17:43.530 Uttam Kumaran: Say that one more time, the last part.
123 00:17:44.570 ⇒ 00:17:49.810 Godwin Ekainu: So I’m just trying to get a sense of the projects, kind of projects you, you, help build,
124 00:17:50.090 ⇒ 00:17:56.320 Godwin Ekainu: like, what are the challenges kind of projects or problems you solve for your clients? .
125 00:17:56.320 ⇒ 00:17:57.700 Uttam Kumaran: Yeah.
126 00:17:57.700 ⇒ 00:17:58.770 Godwin Ekainu: Challenging.
127 00:17:59.510 ⇒ 00:18:08.059 Uttam Kumaran: Yeah, in terms of our projects, so we typically work with companies that are, like, anywhere from $20 million to a few hundred million in revenue.
128 00:18:08.180 ⇒ 00:18:10.890 Uttam Kumaran: And so these are clients that…
129 00:18:11.070 ⇒ 00:18:23.689 Uttam Kumaran: have a lot of data sources that we’re landing into site fields, or a data warehouse, and then building BPT models on top of. So one of the common challenges that people are dealing with is they just don’t have visibility into their business.
130 00:18:24.370 ⇒ 00:18:31.519 Uttam Kumaran: And so, they’re making decisions on spreadsheets, they’re downloading reports from the UI, and, like, it’s completely painful.
131 00:18:32.030 ⇒ 00:18:37.259 Uttam Kumaran: And these are companies that are trying to grow, right? So we work with companies that are really trying to grow faster.
132 00:18:37.520 ⇒ 00:18:43.859 Uttam Kumaran: And grow more efficiently. And so, one of the big things for us is just, like, how do we use data and AI to do that?
133 00:18:44.170 ⇒ 00:19:00.399 Uttam Kumaran: And so again, we have a lot of tools in our tool belt to help people do that, but the reason why our clients trust us is also because we have a lot of engineers that are… that love to explain. And so that’s the one thing that I stress to you and to a lot of folks that join, is like, this isn’t a job where
134 00:19:00.410 ⇒ 00:19:12.900 Uttam Kumaran: as an engineer, you sort of hide in the background, right? That’s also what I think, for a lot of engineers, they don’t like that anyways, you know? They don’t like being pushed to the back, and they like having that client experience, and that’s what we do here, like.
135 00:19:12.960 ⇒ 00:19:16.970 Uttam Kumaran: Everybody in the company, all the engineers, work with clients. Like, there’s not
136 00:19:17.360 ⇒ 00:19:20.559 Uttam Kumaran: project manager in between. We don’t have project managers, actually.
137 00:19:20.860 ⇒ 00:19:33.930 Uttam Kumaran: So it’s all… it’s all really, really amazing engineers that can manage their work. We all come together, use Linear to, like, put all of our tasks in one place. But I’m… I… I know that…
138 00:19:34.290 ⇒ 00:19:50.800 Uttam Kumaran: what that’s gonna happen, what that’s gonna do is gonna give the client more trust, you know, I’m talking to the person that is building the system, versus, like, oh, I’m talking to, like, a project manager, oh, I’m waiting for, like, XYZ meeting. We don’t do things like that. Like, we’re a team. Like, you call us, and, like, we operate and we move fast.
139 00:19:50.840 ⇒ 00:20:02.349 Uttam Kumaran: And that… but that also, that’s… that’s how we charge. Like, we’re… we’re… we… we’re not, like, we don’t do, sort of, dev shop-style stuff. We don’t do, like, oh, we’re gonna build you anything you want. Like, it’s a partnership.
140 00:20:03.940 ⇒ 00:20:17.319 Uttam Kumaran: Like, if they’re not good for us, and they don’t respect our work, we don’t work with them. And so, that’s all the things that, like, we’re figuring out, and we’re growing, and it’s working, you know? And so that’s a little bit about, like, that side of the world.
141 00:20:19.700 ⇒ 00:20:22.780 Godwin Ekainu: I think it makes us, especially on the,
142 00:20:22.880 ⇒ 00:20:38.570 Godwin Ekainu: engineers, communicating part, I think it’s very important, especially for a data role or an analytics role. It’s very important to be able to communicate with stakeholders, basically, guiding them on thought process.
143 00:20:38.860 ⇒ 00:20:51.720 Godwin Ekainu: From process you’re trying to follow, and what you’re building, on the expected results, and being open-minded with them. From my conversation with iOSH, and from the documentations, basically, around SpringForge.
144 00:20:51.760 ⇒ 00:21:09.569 Godwin Ekainu: You guys mainly do, you guys compute documentation, build a lot… write a lot of documentation, which is something I actually respect a lot, because I feel that’s lacking at a lot of places, so you go and you don’t find documentation on certain things ordinarily.
145 00:21:09.570 ⇒ 00:21:14.130 Uttam Kumaran: It’s really what makes or breaks, you know, great data teams, right?
146 00:21:14.130 ⇒ 00:21:14.850 Godwin Ekainu: Right.
147 00:21:15.200 ⇒ 00:21:19.199 Uttam Kumaran: That’s the… that’s the biggest thing that we want to… really want to do.
148 00:21:19.780 ⇒ 00:21:24.989 Godwin Ekainu: Yeah, I believe it’s very important to… Which is kind of great.
149 00:21:25.170 ⇒ 00:21:36.469 Godwin Ekainu: I think, for my last question, basically on, okay, base… basically me asking, if I’m to join, as data engineer, what kind of…
150 00:21:37.050 ⇒ 00:21:40.770 Godwin Ekainu: projects, would they come in to work on?
151 00:21:41.770 ⇒ 00:21:57.050 Uttam Kumaran: Yeah, I mean, we have, we have active clients that need help on establishing new data pipelines, so this is using, like, Prefect or Airflow, or even some of Snowflake’s new, you know, ETL products, like OpenFlow, to write pipelines.
152 00:21:57.170 ⇒ 00:22:03.050 Uttam Kumaran: We also have net new pipelines that need to be created and basically measured.
153 00:22:03.190 ⇒ 00:22:12.919 Uttam Kumaran: There’s also a lot of clients that were coming in, and there’s a lot of, like, documentation and cleanup work, because sometimes we come into class and have thousand plus DVT models.
154 00:22:13.180 ⇒ 00:22:24.309 Uttam Kumaran: And so it’s like, we have to walk in and sort of clean a lot of that up. And so the other thing I’ll sort of mention is that, like, a lot of our people sort of almost split between two different parts of the stack.
155 00:22:24.410 ⇒ 00:22:35.360 Uttam Kumaran: And so certainly, I think here, you’re going to have an opportunity not only to do a lot of data work on the DE side, but also a lot of, like, AE work, if you want to go there. So if you’re like, hey, I already wrote the
156 00:22:35.380 ⇒ 00:22:52.259 Uttam Kumaran: I wrote the code to land the data in RAW, maybe I’ll go ahead and just spin up the core models that sits on top of it, right? And that is something that, like, our separation of responsibilities here is a lot of… is a lot looser, because I don’t want it to be like, oh, I only do DE work, like.
157 00:22:52.290 ⇒ 00:22:55.049 Godwin Ekainu: And you have no appreciation. It’s… we’re a team.
158 00:22:55.070 ⇒ 00:23:01.040 Uttam Kumaran: So everybody learns how to do everything, it can be backup, so in case something happens, in case people off.
159 00:23:01.170 ⇒ 00:23:04.710 Uttam Kumaran: And then the last piece I’ll mention is we use AI so much.
160 00:23:04.840 ⇒ 00:23:20.130 Uttam Kumaran: And we use AI for everything from internal company work, our sales team uses a lot of different AI pieces, everybody in the engineer uses cursor, you know, to write code and help, and really, like, it’s not actually, like, as you know, it’s not… we’re not using AI to ship like slop.
161 00:23:20.250 ⇒ 00:23:22.060 Uttam Kumaran: But the things that we know.
162 00:23:22.190 ⇒ 00:23:40.110 Uttam Kumaran: we can just do fast fit. And then we’re still reviewing those code reviews and things like that, but something that, like, I know would have taken 30 minutes, I can now do really fast. And what we do is, our product for our client is the fact that we move fast, and they appreciate it, and they pay us more because of how fast we move it, right? So…
163 00:23:40.850 ⇒ 00:23:52.340 Uttam Kumaran: That’s a little bit about kind of where I think you would fit. Like, we have active clients right now that need further data engineering support, that need help writing pipelines, structuring data warehouses, cleaning up code.
164 00:23:52.570 ⇒ 00:23:57.569 Uttam Kumaran: And then it’s sort of, like, up to you, like, where you want to fit in here at the company, you know?
165 00:23:58.500 ⇒ 00:23:59.460 Godwin Ekainu: Cool.
166 00:23:59.730 ⇒ 00:24:02.920 Godwin Ekainu: I believe, it’s exciting, to be honest.
167 00:24:03.300 ⇒ 00:24:04.140 Godwin Ekainu: I…
168 00:24:04.430 ⇒ 00:24:10.080 Godwin Ekainu: I’m actually quite curious, to come in and see what Tian also tried to help out a lot.
169 00:24:10.180 ⇒ 00:24:19.630 Godwin Ekainu: And on the whole, analytics or data side, I believe, a data engineer, you’re also… you’re also supposed to be familiar with the data modeling side, which is…
170 00:24:19.790 ⇒ 00:24:31.319 Godwin Ekainu: where I notice multi-engineers don’t really set their eyes on, so they mainly just focus on building pipelines, but, the data modeling and the understanding the business context is…
171 00:24:31.460 ⇒ 00:24:35.640 Godwin Ekainu: really important, and which is where I feel, most people are lacking.
172 00:24:35.840 ⇒ 00:24:46.670 Godwin Ekainu: And also, I think over the past 6, past years, basically, I’ve mainly tried to deepen that knowledge on understanding business context and building models.
173 00:24:46.670 ⇒ 00:24:56.390 Godwin Ekainu: And on that news on the AI. I’m just quite… I’m quite curious on how you find AI, especially in data modeling. How has been your experience with it?
174 00:24:57.280 ⇒ 00:25:04.120 Uttam Kumaran: Yeah, I mean, for me, I think the biggest thing that we’ve been…
175 00:25:05.420 ⇒ 00:25:22.139 Uttam Kumaran: I think there’s two ways. One is, like, on the data modeling side, targeted is like, hey, like, we just landed, like, all this data for Shopify. Just build me, like, the base models, like a base orders table, things like that, right? So, that is really nice, because that’s all work that, like, you kind of brainlessly can do.
176 00:25:22.220 ⇒ 00:25:28.010 Uttam Kumaran: What I can’t help is, like, the nuances. Oh, this is UTC versus EST, like, some of that stuff, like.
177 00:25:28.720 ⇒ 00:25:38.169 Uttam Kumaran: it’s business context, right? So, like, a lot of that we figure out. The other piece is debugging. Like, hey, client flagged to me that, like, this…
178 00:25:38.570 ⇒ 00:25:50.020 Uttam Kumaran: this order wasn’t segmented properly. Help me, like, investigate. Again, something takes 30 minutes, an hour can now take 2 minutes. It’s not like… but the thing is, it’s speeding up something that we would have done.
179 00:25:50.750 ⇒ 00:26:03.270 Uttam Kumaran: It’s just doing it faster. And so, that’s a lot of ways we use it for data modeling. The other thing is we also put our best practices. So, people use, you know, like, what is the best practice their brain for it for modeling different things.
180 00:26:03.340 ⇒ 00:26:12.760 Uttam Kumaran: And so, that way… and it also… I think the last piece is this helps with documentation, though. You know how hard it is to, like, write documentation? It takes so long, it’s, like, brainless work, and…
181 00:26:12.850 ⇒ 00:26:21.509 Uttam Kumaran: AI is really, really good at that. So every time we put something, we really try to require, like, hey, make sure there’s documentations associated with all of this, you know?
182 00:26:22.450 ⇒ 00:26:33.100 Godwin Ekainu: Yeah, I mean, it’s really great, it helps hasting the work, the workflow, too, and I find it really great at, debugging, too, which you just mentioned. I… I believe.
183 00:26:33.100 ⇒ 00:26:37.989 Uttam Kumaran: Yeah, we give it CLI access to a lot of tools, like, we give it Snowflake CLI.
184 00:26:38.220 ⇒ 00:26:39.429 Uttam Kumaran: And so it can loop.
185 00:26:39.890 ⇒ 00:26:52.239 Uttam Kumaran: You see what I mean? So, we give it dbt Fusion, and we give it Snowflake, so it’ll create the table, it’ll modify the SQL, create the table, go to Snowflake, check the results, find the result is off, come back.
186 00:26:52.520 ⇒ 00:26:54.069 Uttam Kumaran: Right? And they’ll sort of loop.
187 00:26:54.240 ⇒ 00:27:02.289 Uttam Kumaran: But see, you know, for that, like, okay, we need to make sure we have the credentials set up, we have the roles set up, we know how to prompt the CLI, but…
188 00:27:02.520 ⇒ 00:27:14.999 Uttam Kumaran: Again, like, I think if you’ve been at companies that they don’t embrace this, we’re the opposite of this. Like, everybody at the company uses AI for as much as possible, and we’re a small company that’s punching
189 00:27:15.120 ⇒ 00:27:17.989 Uttam Kumaran: Up, you know, really, really hard because of it, you know?
190 00:27:18.210 ⇒ 00:27:22.790 Uttam Kumaran: And I don’t want our people who are really, really smart to be wasting time
191 00:27:23.230 ⇒ 00:27:27.290 Uttam Kumaran: doing things that could have been handed to AI, they need to spend more time with clients.
192 00:27:27.410 ⇒ 00:27:29.379 Uttam Kumaran: Spend more time on the hardest problems.
193 00:27:29.750 ⇒ 00:27:32.730 Godwin Ekainu: Yeah. You know, complicated architecture.
194 00:27:32.730 ⇒ 00:27:33.759 Uttam Kumaran: Things like that.
195 00:27:34.680 ⇒ 00:27:41.909 Godwin Ekainu: Yeah, I think… I mean, that’s true, that’s true. I’m… to be honest, I’m pretty excited about the role, and…
196 00:27:42.110 ⇒ 00:27:44.719 Godwin Ekainu: Hopefully, everything turns out great.
197 00:27:46.360 ⇒ 00:27:47.480 Uttam Kumaran: Okay, terrific.
198 00:27:47.770 ⇒ 00:27:59.059 Uttam Kumaran: All right, so I think the next step, you know, I would love to invite you, we do have a final presentation round. It’s a bit of a technical exercise, that will kind of accompany a little bit of a presentation component.
199 00:27:59.590 ⇒ 00:28:00.020 Godwin Ekainu: So, you know.
200 00:28:00.020 ⇒ 00:28:07.130 Uttam Kumaran: I would love to submit you for that, and you know, I do think that, like, I’ve really, really appreciated this conversation. I think
201 00:28:07.140 ⇒ 00:28:20.870 Uttam Kumaran: you know, that’s sort of the final piece that we need, but, like, this would be really amazing. Like, I appreciate your energy, and again, sorry I’m camera off today, but, it was really, really, really, really amazing to meet you, and thanks for the great questions.
202 00:28:20.940 ⇒ 00:28:22.230 Uttam Kumaran: I think it’s,
203 00:28:22.320 ⇒ 00:28:29.519 Uttam Kumaran: it’s… I sometimes don’t… don’t often get people that are asking kind of great questions about in-depth, so this is really, really great.
204 00:28:29.990 ⇒ 00:28:35.080 Godwin Ekainu: Yeah, I truly enjoyed the conversation. I’m sorry for taking time off your vacation.
205 00:28:35.080 ⇒ 00:28:46.370 Uttam Kumaran: Oh, dude, no, no, no. Well, dude, not a false shot. No, I’m here for you. Like, it’s what I say, like, you know, for me, when I wake up, it’s my clients, because without clients, there’s no company.
206 00:28:46.840 ⇒ 00:28:47.230 Godwin Ekainu: That’s the team.
207 00:28:47.230 ⇒ 00:29:02.619 Uttam Kumaran: Like, I’m way down somewhere. Like, my priority is not important, you know? So our team is our product. So, I think that’s what you’ll find in everybody we meet. A lot of people are surprised. They’re like, how do you get all these people? And, like, they’re all amazing, and I’m like.
208 00:29:02.810 ⇒ 00:29:16.759 Uttam Kumaran: I care a lot, like, I worked as an engineer in many places that didn’t care about me, and I hate that, and you know, so we try to be at… in the ways that we can as a small company, we try to show up and be present, so…
209 00:29:16.940 ⇒ 00:29:18.679 Uttam Kumaran: We try really hard.
210 00:29:19.690 ⇒ 00:29:25.810 Godwin Ekainu: Yeah, I think that’s really great. I think that’s one attribute that will make the company grow very well.
211 00:29:26.050 ⇒ 00:29:27.969 Godwin Ekainu: This is particularly, to be honest.
212 00:29:30.170 ⇒ 00:29:33.980 Godwin Ekainu: Hopefully, I get to work with you, fully.
213 00:29:34.120 ⇒ 00:29:36.529 Godwin Ekainu: And for verification.
214 00:29:36.710 ⇒ 00:29:45.839 Uttam Kumaran: Definitely, definitely. Okay, Godwin, it was really, really nice to meet you. Where are you, where are you calling from, by the way?
215 00:29:45.840 ⇒ 00:29:49.590 Godwin Ekainu: I’m currently in United Kingdom. Where are you okay?
216 00:29:49.950 ⇒ 00:29:51.400 Godwin Ekainu: Birmingham City.
217 00:29:51.790 ⇒ 00:29:54.789 Uttam Kumaran: Okay, okay. How long have you been in Birmingham City?
218 00:29:55.090 ⇒ 00:29:57.079 Godwin Ekainu: I’ll say 2 years.
219 00:29:57.300 ⇒ 00:30:00.329 Godwin Ekainu: Nice months. Three months or so.
220 00:30:00.910 ⇒ 00:30:02.160 Uttam Kumaran: How is it? Cole?
221 00:30:02.160 ⇒ 00:30:08.150 Godwin Ekainu: Yeah, it’s… yeah, pretty damp. EQA is generally cold.
222 00:30:08.150 ⇒ 00:30:11.510 Uttam Kumaran: Are you getting a UK accent yet? I can kind of hear it, maybe.
223 00:30:11.850 ⇒ 00:30:14.100 Godwin Ekainu: Really, I don’t think I have that.
224 00:30:16.610 ⇒ 00:30:18.499 Uttam Kumaran: I don’t think I have that yet.
225 00:30:19.520 ⇒ 00:30:26.860 Uttam Kumaran: That’s funny. Okay, man. Alright, well, I hope you’ll hear from us very, very soon, so I appreciate the time today.
226 00:30:27.260 ⇒ 00:30:29.890 Godwin Ekainu: Thank you very much. I’m looking forward to it.
227 00:30:30.390 ⇒ 00:30:32.500 Godwin Ekainu: Thank you, sir. I’ll talk to you soon. Bye.