Meeting Title: Brainforge-Team-Meeting-and-Intros Date: 2024-02-26 Meeting participants: Jack Tomei, Uttam Kumaran, Agustin, Patrick Trainer, Ryan Luke Daque
WEBVTT
1 00:00:52.650 ⇒ 00:00:53.570 Patrick Trainer: Hey?
2 00:00:54.560 ⇒ 00:00:55.550 Jack Tomei: You know.
3 00:00:56.020 ⇒ 00:00:57.600 Patrick Trainer: How’s it going? What’s up, Jack?
4 00:00:57.740 ⇒ 00:00:59.929 Jack Tomei: What’s that good to meet you.
5 00:01:01.370 ⇒ 00:01:05.329 Jack Tomei: hey? Hey? How’s everyone doing?
6 00:01:06.730 ⇒ 00:01:08.479 Jack Tomei: Not doing well, too bad.
7 00:01:08.990 ⇒ 00:01:14.230 Uttam Kumaran: Okay, let me message fine. But one sec.
8 00:01:15.030 ⇒ 00:01:19.500 Patrick Trainer: Joe. Know you could use like an iphone camera as your
9 00:01:19.510 ⇒ 00:01:33.349 Patrick Trainer: yes, I did use it a couple of times, because I don’t know where to put it
10 00:01:33.720 ⇒ 00:01:36.109 Agustin: looking for it now, but trying to
11 00:01:36.120 ⇒ 00:01:41.020 Agustin: gonna try and use that as as the mount, because otherwise, yeah, like, how the hell do you mount it?
12 00:01:41.380 ⇒ 00:01:45.300 Agustin: But there’s this feature on it. It’s called Desk View.
13 00:01:45.450 ⇒ 00:01:49.800 Patrick Trainer: right? And it uses like the ultra-wide camera, so it can see you
14 00:01:50.070 ⇒ 00:01:56.529 Patrick Trainer: and your desk like while you’re writing like. So if you’re like drawing something, it’s like super sick.
15 00:01:59.110 ⇒ 00:02:10.380 Uttam Kumaran: I gotta try. II feel like I just found my like this logitech web camera, I added, like my in my box of cables that everybody has. So I just found a camera, and I’ve been using that.
16 00:02:10.500 ⇒ 00:02:15.139 Patrick Trainer: And then there’s there’s another feature, too. Where?
17 00:02:15.150 ⇒ 00:02:16.429 Agustin: Shit? What is it?
18 00:02:18.190 ⇒ 00:02:20.219 Patrick Trainer: You can.
19 00:02:21.910 ⇒ 00:02:23.280 Patrick Trainer: Oh, I just lost it.
20 00:02:25.640 ⇒ 00:02:26.649 Agustin: I’ll think of it.
21 00:02:28.750 ⇒ 00:02:29.740 Patrick Trainer: I’ll think of it.
22 00:02:30.000 ⇒ 00:02:37.040 Uttam Kumaran: Okay, okay, cool. I. So I guess this is the whole basically whole squad. Some people are
23 00:02:37.320 ⇒ 00:02:57.240 Uttam Kumaran: a little bit veteran like Ryan, who’s been with me for a bit? And then some people I’ve known for a bit, and we just started working together like pad, and some people are all like new friends, Jack and Augustine. So it’s really nice to have everybody here. This is sort of like the kind of like brain force team that exists right now. Some other folks
24 00:02:57.310 ⇒ 00:03:18.359 Uttam Kumaran: some others really smart data folks I’ve worked with here and there will probably pop back up into the fray as we like move forward. But basically everybody here is on the team. Currently. So one first thing off the bat, just wanna say, I really appreciate everybody’s time and appreciate trusting me and like beginning to work on stuff and everybody here.
25 00:03:18.360 ⇒ 00:03:24.840 Uttam Kumaran: you know I I’m not a very good interviewer. But I do. If you’ve worked for me, you know how
26 00:03:24.840 ⇒ 00:03:51.739 Uttam Kumaran: kind of fast and directly I try to work and how much I’m really trying to build this company to be really valuable, not only for the clients we work, but for the people internally. And so everybody here is someone that I’ve I’ve really trusted. And we’ve done work together so far. And I’m really excited to kind of get everybody together. Basically I’ve as I told everybody like Brainforge started as just me quitting my last job and basically saying like, Can I try and make money on the Internet?
27 00:03:51.770 ⇒ 00:03:53.570 Uttam Kumaran: and I,
28 00:03:53.600 ⇒ 00:04:21.320 Uttam Kumaran: I feel lucky to have kind of made a bunch of friends in the industry and otherwise, and have the wherewithal to kind of go get some clients to actually begin to do data work for them, not only in the capacity that we’ve done where we’ve been individual contributors on, you know, a larger company. But can we basically be like navy seals? Can we walk into any situation that involves data? Basically break it down and then say, like, Hey, who do we have on the team that can go tackle it. Our ability to kind of be specialized and our pace. And
29 00:04:21.320 ⇒ 00:04:45.079 Uttam Kumaran: you know we work on Dvt. And a lot of modern tools. Pretty much is the reason why we can get, you know, the money in the door that we get, and then continue to go work on cooler projects. And so one of the things that I you know I shared with Ryan early on. But kind of like I kind of thought about when I started. The company is kind of just thinking about like company principles. And if you go to like the announcement channel, basically, you can see
30 00:04:45.100 ⇒ 00:04:50.620 Uttam Kumaran: those original principles that I wrote down when I started the company. This was like kind of in like
31 00:04:50.840 ⇒ 00:05:03.780 Uttam Kumaran: July, after I like had any amount of time to kind of just like, sit and think about what I’m even doing. But basically, this is kind of the current principles of the company. These are kind of gonna evolve over time. But overall.
32 00:05:04.160 ⇒ 00:05:06.489 Uttam Kumaran: the biggest thing is wanted to kind of
33 00:05:06.520 ⇒ 00:05:13.610 Uttam Kumaran: the cou couple couple of major things are increasing prices and working with more interesting and clients over the time.
34 00:05:13.730 ⇒ 00:05:23.519 Uttam Kumaran: working and delivering and communicating like a massive agency. Right? It’s just the 5 of us and some people more hours than others. But there’s no reason we can’t compete with
35 00:05:23.530 ⇒ 00:05:31.979 Uttam Kumaran: some people’s previous employers like Deloitte, or or otherwise. So you know, the I really feel like, we can act
36 00:05:31.990 ⇒ 00:05:49.970 Uttam Kumaran: like some of these larger agencies, not only data, but consultants or agencies. Development firms develop on world class technology and then find a way to, you know, work less and automate internal processes over time. I think that’s a big thing that a lot of companies don’t talk about is, I want all of our time to be spent either
37 00:05:50.180 ⇒ 00:05:59.149 Uttam Kumaran: bringing on new clients, you know, delivering for existing clients, or just like living our lives and like gone outside and and hanging out.
38 00:05:59.190 ⇒ 00:06:00.559 Uttam Kumaran: I don’t think
39 00:06:00.630 ⇒ 00:06:12.880 Uttam Kumaran: this has to be a company where we’re in meetings all day. And we’re talking about the work. I think this is gonna be really about doing the work and then making sure we can get cooler work and more lucrative work over time.
40 00:06:13.160 ⇒ 00:06:30.529 Uttam Kumaran: and so that’s kind of the gist. So I know all of us are kind of working on multiple different clients. Some people are working on internal stuff. But basically, I would just love to go around the Horn, and I’ll give a little bit of intro. And then, if folks just want to kind of go around the Horn, so basically, like I,
41 00:06:30.830 ⇒ 00:06:36.010 Uttam Kumaran: I have a lot of ideas for the company, and that’s bands with the full stack of data. So from
42 00:06:36.150 ⇒ 00:06:50.210 Uttam Kumaran: bringing in deep data, Etl, the way into modeling all the way to Bi, all the way to analysis, and some stuff adjacent, like data listings, data providers, also like testing and things like that. So
43 00:06:50.610 ⇒ 00:06:54.860 Uttam Kumaran: I, when we started this, I was basically doing the whole thing. And
44 00:06:55.120 ⇒ 00:07:09.169 Uttam Kumaran: I was starting to not sleep, and pretty much my life was getting destroyed. So I was like, Okay, I need to like, call in for reinforcements. And so what do I do? I I’m connected with a lot of amazing people, and then I pretty much put a bad signal out to try to find
45 00:07:09.170 ⇒ 00:07:25.609 Uttam Kumaran: really awesome folks. And so that’s who all of you all are. And basically around the Horn, I think we have a lot of people that fit into all those different slices of the pie from like data engineering all the way to modeling, to Bi, to really heavy analysis work. So if you guys just want to go around the Horn and just talk a little bit about
46 00:07:25.610 ⇒ 00:07:48.480 Uttam Kumaran: just brief like 30. Second, about your background kind of like kind of stuff you’re working on and Brainforge, and then anything else. Maybe, Pat, if you wanna start also, like where you’re located, where where are you from? What’s your major color? Yeah, yeah, no, I’m but I’m Patrick. I’m down here in New Orleans, Louisiana.
47 00:07:49.020 ⇒ 00:07:58.399 Patrick Trainer: Well, I’ve been at Brainforge for like 2 weeks so that’s it’s been been going well previously. I was at a company out in New York.
48 00:07:58.440 ⇒ 00:08:01.430 Patrick Trainer: Working, remote, called secure
49 00:08:01.470 ⇒ 00:08:12.300 Patrick Trainer: was there for about 2 and a half years, made it through 3 rounds of layoffs. And so like, that’s my kind of like Perk of, I’m probably gonna put that on a resume like
50 00:08:12.710 ⇒ 00:08:15.380 Patrick Trainer: 1, 2, 3 rounds.
51 00:08:15.440 ⇒ 00:08:17.449 Patrick Trainer: Weird times we live in.
52 00:08:17.550 ⇒ 00:08:24.919 Patrick Trainer: and then I’ve worked with other larger data teams. Did some campaigns and then worked with
53 00:08:25.240 ⇒ 00:08:33.280 Patrick Trainer: multiple startups as well. Always in kind of like a data engineering scope. But I’ve worked through
54 00:08:33.640 ⇒ 00:08:39.939 Patrick Trainer: kind of across the spectrum in terms of like data. Vi, kind of this whole ecosystem that we’re doing.
55 00:08:40.200 ⇒ 00:08:54.920 Patrick Trainer: with brain forwards. What I’m doing here right now, kinda like specializing in the like visualization slash information architecture and kinda like playing that
56 00:08:55.030 ⇒ 00:08:56.490 Patrick Trainer: role between.
57 00:08:56.540 ⇒ 00:09:01.830 Patrick Trainer: I guess like like database a a, and the space in between modeling as well
58 00:09:01.900 ⇒ 00:09:04.250 Patrick Trainer: of yeah.
59 00:09:05.410 ⇒ 00:09:10.460 Uttam Kumaran: And then me and Pat are connected. Oh, yeah, right.
60 00:09:10.540 ⇒ 00:09:12.040 Uttam Kumaran: Did I lose everybody?
61 00:09:12.580 ⇒ 00:09:13.859 Patrick Trainer: Nope, you’re here
62 00:09:14.250 ⇒ 00:09:31.260 Patrick Trainer: I was on the Presidential campaign, and one of the guys that I worked with a guy named Mike
63 00:09:31.270 ⇒ 00:09:35.490 Patrick Trainer: and he worked with Utome at wework.
64 00:09:35.630 ⇒ 00:09:37.100 Patrick Trainer: and
65 00:09:37.160 ⇒ 00:09:41.069 Patrick Trainer: just over the time, like Mike and I have been close and always
66 00:09:41.090 ⇒ 00:09:49.669 Patrick Trainer: went back and forth. And then it’s we’ve started adding on to this group chat and adding more and more people, a bunch of people that I’d never met before
67 00:09:49.770 ⇒ 00:09:56.199 Patrick Trainer: actually, still haven’t met in person. But it turned into this actually pretty big group chat, and that’s why I met
68 00:09:56.220 ⇒ 00:10:02.190 Patrick Trainer: and then what? So I got laid off, and it was literally like what 2 days
69 00:10:02.230 ⇒ 00:10:08.879 Patrick Trainer: yes, so it was. It was like really clutched turnaround in in terms of like
70 00:10:09.170 ⇒ 00:10:12.569 Patrick Trainer: not being unemployed for too long. So
71 00:10:12.800 ⇒ 00:10:32.490 Uttam Kumaran: which is how
72 00:10:32.680 ⇒ 00:10:47.869 Uttam Kumaran: commonly like I’m not that good at doing dashboards. II suck, I can do them. But like II think, for a lot of our clients. That’s the main way to interact a lot of our work, you know, below the stack. So kind of like wanted to make sure we have someone on board that’s like that’s their bread and butter.
73 00:10:47.970 ⇒ 00:10:50.579 Uttam Kumaran: Jack, do you wanna go next?
74 00:10:51.700 ⇒ 00:10:55.230 Jack Tomei: Sure, what’s up, guys? I’m in Seattle?
75 00:10:56.040 ⇒ 00:11:05.579 Jack Tomei: I just joined Brain Forge like on a Tuesday a week ago. I’m working on the pool parts to go account
76 00:11:05.630 ⇒ 00:11:06.810 Jack Tomei: doing like
77 00:11:07.250 ⇒ 00:11:17.050 Jack Tomei: analytics stuff, I think, similar to what? Patrick was just kind of talking about visualization stuff and like, kind of giving the data to the client sort of stuff.
78 00:11:17.710 ⇒ 00:11:26.900 Jack Tomei: My background is in like more strictly data analytics, not so much data engineering stuff. I worked at a startup like coding
79 00:11:27.320 ⇒ 00:11:34.100 Jack Tomei: Academy kind of coding schools or online coding school thing in San Francisco out of college.
80 00:11:34.230 ⇒ 00:11:46.129 Jack Tomei: That’s where I met Udam’s like best friend, or like old childhood friend Sri. We were close. He was like a financial analyst kinda at the company. And I was doing most of the data
81 00:11:46.280 ⇒ 00:11:51.350 Jack Tomei: sort of analysis work for the finance team and for the sales team. So we we work together a lot.
82 00:11:52.900 ⇒ 00:11:58.230 Jack Tomei: Most recently I worked for Deloitte for the last, like 2 years, I was on the Meta platforms account.
83 00:11:58.930 ⇒ 00:12:10.989 Jack Tomei: not really doing so much data work, doing more like, pm, kind of stuff. For hardware engineers, my backgrounds and like computer engineering. So I like, kinda knew what I was doing. But it was honestly like
84 00:12:11.250 ⇒ 00:12:16.619 Jack Tomei: the way Deloitt pairs people with projects is very random. And
85 00:12:16.690 ⇒ 00:12:26.790 Jack Tomei: you kind of just get a call, and you have to say yes to it, and I wasn’t really on the best team. To be honest. I was like by far the most technical person on a very technical account. So I didn’t really have like
86 00:12:27.050 ⇒ 00:12:31.029 Jack Tomei: anyone to ask questions to, you know, and I didn’t know what was going on, and you were really
87 00:12:31.100 ⇒ 00:12:37.739 Jack Tomei: encourage not to ask a lot of questions, cause we were supposed to be like the experts in our field, you know. But I’m like.
88 00:12:37.920 ⇒ 00:12:49.270 Jack Tomei: I’m not super experienced. It was just a weird. It was a bit of a weird gig that I had when Meta laid off 11,000 people last year. They cut everyone at Deloitte. There’s 250 of us. They cut all of us.
89 00:12:49.340 ⇒ 00:12:54.169 And I ended up getting laid off, so I didn’t survive. There’s no rounds of layoffs
90 00:12:54.230 ⇒ 00:13:07.910 Jack Tomei: at Deloi. So I’ve been looking for a new job for like a few months now. I got laid off in like November, so it was kind of a weird time, like Thanksgiving Christmas, like New Year’s. Now things kind of got picking back up. I reached out to a bunch of old friends from
91 00:13:08.090 ⇒ 00:13:11.680 Jack Tomei: San Francisco, and that’s how I got put in connection with Udem.
92 00:13:11.930 ⇒ 00:13:19.800 Jack Tomei: So I’m happy to get back into the data stuff. It’s been a little while. But II I’ve been doing a bunch of work last week, and it’s like kind of pick it right back up. I feel like so
93 00:13:20.240 ⇒ 00:13:21.290 Jack Tomei: happy to be here.
94 00:13:21.470 ⇒ 00:13:29.419 Patrick Trainer: not not asking questions is like the one of the most big 4 things I’ve ever heard.
95 00:13:29.670 ⇒ 00:13:47.490 Jack Tomei: The first thing like here, here’s a suit and like, Go on. Yeah, she’s like, I’m gonna introduce you to the team. Don’t mention how you just it was like my fourth day at Deloitte, or something when I got put on the account she’s like, don’t mention that you just got here. Don’t mention you’ve never done this. Don’t mentioned like okay, Fuck, you can’t really say who you are. It’s very
96 00:13:47.570 ⇒ 00:13:57.559 Jack Tomei: 20 year veteran, 25 years old and managing like 5 year old people that have worked there for 20 literally, those types of people. And it’s like
97 00:13:58.030 ⇒ 00:14:06.520 Jack Tomei: I was really just letting them do their thing, you know, cause it’s like, what can I really bring but I saw, yeah, I’m happy to get back at something that like, I actually know how to do and
98 00:14:07.090 ⇒ 00:14:09.150 Jack Tomei: and yeah, more, the data analytics stuff.
99 00:14:09.950 ⇒ 00:14:24.610 Uttam Kumaran: Yeah? And so just to comment on that, I think very similar to Pat’s work. You know, I’ve spent a lot of my career in like data, engineering and modeling. And then I’ve done a lot of analysis stuff myself. But again, I think it requires a different brain in that requires someone who can just like
100 00:14:24.660 ⇒ 00:14:45.580 Uttam Kumaran: jump through a bunch of different rabbit holes, like again and again and again, and and Jack particularly the discounts and warranty stuff that I demoed to them today was like a super hit. So I gotta send you a little bit of a summary. And the recording. But that stuff went really, really well. And I’m really excited to go offer that to like our clients. And I think that’s that’s a specific area as well like
101 00:14:45.580 ⇒ 00:15:01.070 Uttam Kumaran: hardcore data analysis. Really putting yourself in the shoes of the company. But then, by any means necessary, whether it’s Google, Doc, excel or light dash, or whatever being like. Here’s the impact. Here’s what we should do. A lot of people are not only asking us to surface data, but then
102 00:15:01.250 ⇒ 00:15:26.099 Uttam Kumaran: they they do the thing where they ask us to also act on it and be like, what should we do? And so that’s where, you know, we’ve been in a lot of companies in a lot of different roles. And so that work at like Deloitte and stuff. I think there’s a huge application here, cause you know how certain companies run, and you’ll be surprised how many companies just want us to just make a decision on stuff. And so for us to be confidently able to go do that and be like this, how you should handle warranties, or this is what you should do.
103 00:15:26.580 ⇒ 00:15:30.969 Uttam Kumaran: You know what happened out of that call today. They pretty much are gonna cut that Cfd 32 skew.
104 00:15:31.200 ⇒ 00:15:41.719 Uttam Kumaran: basically. And and it was really great to see them make up the zoom. We’re like, okay, perfect. We’re already gonna do this, the data show this. We’re gonna make a move on this. And so that’s the sort of stuff I think, like
105 00:15:41.740 ⇒ 00:15:50.700 Uttam Kumaran: at any other company, it would have taken us 6 months to kind of come to a decision. In one week. We do the whole thing. We put in front of the operator and then make a decision.
106 00:15:50.730 ⇒ 00:16:00.450 Uttam Kumaran: And they’re gonna end up saving a ton of money, and that pretty much pays for us, right? So great example of like that. So Augustine, you wanna go?
107 00:16:02.730 ⇒ 00:16:09.560 Agustin: Okay? Thanks. Well, I’m Austin. I’m based in whenis, Argentina, Latin America
108 00:16:09.950 ⇒ 00:16:13.469 Agustin: to the south. Yeah, I
109 00:16:14.180 ⇒ 00:16:18.230 Uttam Kumaran: what time is it where you are? 4 or 5?
110 00:16:18.600 ⇒ 00:16:19.900 It’s like.
111 00:16:19.930 ⇒ 00:16:22.830 Agustin: now, it’s 4 15 pm.
112 00:16:24.000 ⇒ 00:16:24.680 Agustin: yeah.
113 00:16:24.810 ⇒ 00:16:31.280 Agustin: I have a degree in it’s called like management systems. Engineering. Yeah.
114 00:16:31.600 ⇒ 00:16:32.890 Agustin: and
115 00:16:33.210 ⇒ 00:16:37.180 Agustin: I’m not sure if it exists right over there. By yeah.
116 00:16:37.570 ⇒ 00:16:43.180 Agustin: I’ve been working as a data engineer for almost 4 years now
117 00:16:43.750 ⇒ 00:16:47.459 Agustin: they work for a local marketing consultancy.
118 00:16:47.560 ⇒ 00:16:56.789 Agustin: Then I switch to Mexican startup and then to another company in Ruai, which is neighbor country.
119 00:16:56.840 ⇒ 00:17:07.060 Agustin: But basically, I quit like a week ago. Yeah, I quit
120 00:17:07.160 ⇒ 00:17:11.589 Agustin: because they were like the all the data related projects.
121 00:17:11.690 ⇒ 00:17:17.510 Agustin: And I forgot the word like. they vanished because the client
122 00:17:17.520 ⇒ 00:17:24.530 Agustin: laid off the contractors, and I was being given like a full stack development work which I do not like.
123 00:17:24.569 ⇒ 00:17:28.810 Agustin: And basically I met utam like randomly
124 00:17:28.910 ⇒ 00:17:32.770 Agustin: in and in a slack channel called Devi.
125 00:17:32.840 ⇒ 00:17:35.209 Agustin: which you probably know. Yeah.
126 00:17:35.230 ⇒ 00:17:37.430 Agustin: that’s how I join here.
127 00:17:37.650 ⇒ 00:17:40.080 Patrick Trainer: That’s that’s how I met Mike.
128 00:17:40.320 ⇒ 00:17:43.570 Uttam Kumaran: Oh, really, actually, yeah, yeah, yeah, slack.
129 00:17:44.270 ⇒ 00:18:03.179 Uttam Kumaran: Nice? Yeah, I I’m in like 4 different data slacks. And I occasionally post stuff. Dbt, slack is massive. So I get little relax. I used to post a lot like in 2019. Now, it’s like too big. I don’t. I don’t really like feel comfortable. But yeah, no, I mean again, it’s like I. So I seen, it’s helping us.
130 00:18:03.230 ⇒ 00:18:27.519 Uttam Kumaran: we’re doing some work on the data engineering side for both pool parts. And externally, basically, some of the initiatives we have which, hopefully, I’ll get to explain next week on data providers and some of the work we’re doing around the Snowflake data marketplace. There’s some interesting things that we’re working on, that obviously, is gonna kind of like lead and try out some of the tech for basically trying to run Etl directly in Snowflake.
131 00:18:27.540 ⇒ 00:18:40.839 Uttam Kumaran: So we can kind of walk some of that through that next week. And then basically a lot of hopefully, a lot of the data engineering related processes hopefully, some stuff related to Github actions and things like that. We’ll kind of like be able to work with him on. So
132 00:18:41.210 ⇒ 00:18:46.840 Uttam Kumaran: really excited to thank you. Yeah. And then, Ryan, do you wanna give an intro?
133 00:18:47.360 ⇒ 00:19:00.389 Ryan Luke Daque: Nice Hi, guys my name’s Ryan. I guess I’m aside from utam. I’m the oldest here I’ve been working with Utam for about like 2 months now, I believe. A little bit more.
134 00:19:00.450 ⇒ 00:19:07.979 Ryan Luke Daque: Yeah, probably. Yeah, the better. 2 months working, or maybe maybe maybe 3. I don’t know.
135 00:19:08.120 ⇒ 00:19:22.909 Ryan Luke Daque: But yeah, I’m at the other side of the world. I’m in in the Philippines. So it’s like 3 in the morning here. So yeah, so south east Asia, basically, where, like
136 00:19:23.000 ⇒ 00:19:36.730 Ryan Luke Daque: III don’t know if you’ve seen like Youtube videos of, like the the the Philippines and stuff like that. But yeah. so yeah, I’ve been working with Utam, mostly doing data modeling. But I’ve also worked a couple of
137 00:19:37.060 ⇒ 00:19:44.900 Ryan Luke Daque: well, basically, I’ve been working with an any kind of data stuff doing also data visualization in light dash. And
138 00:19:45.010 ⇒ 00:20:08.379 Ryan Luke Daque: also trying to do data analysis, which, is probably not my best suit. But yeah, I I’m trying to do my best as well. There. Us 2 experiences. I’ve actually been work. I I’ve worked with Lexmark. The printer company for like 11 years. And I basically stopped working there.
139 00:20:08.620 ⇒ 00:20:11.360 Ryan Luke Daque: I resigned. And that was like
140 00:20:11.560 ⇒ 00:20:38.950 Ryan Luke Daque: pandemic times well, where we always stuck in our houses. So I just did like freelance stuff doing data related jobs. I got into data engineering work to it. Bigquery, Google Cloud, as well as like Dbt, basically. And I fell in love with it. Data modeling. Dbt, bigquery like.
141 00:20:39.110 ⇒ 00:20:43.640 Ryan Luke Daque: cloud. basically, software and stuff tools.
142 00:20:43.700 ⇒ 00:20:45.649 Ryan Luke Daque: Yeah, that’s basically it
143 00:20:46.590 ⇒ 00:20:56.060 Uttam Kumaran: awesome. So yeah, I would say, Ryan is kind of like in my kind of my speed spot area, which is like on everything data model you really came on helped me scale up
144 00:20:56.060 ⇒ 00:21:14.000 Uttam Kumaran: kind of our impact on writing Dbt, and then light dash, of course, which has been a little bit of a new software. So I think you know, through all of us, we pretty much have basically like full stack data, which is, I usually describe as like even fuller stack, so pretty much, we can go from any source all the way to the Vis, I think with this team, and
145 00:21:14.000 ⇒ 00:21:28.040 Uttam Kumaran: to kind of give you expectation of me. Now, I’m spending a lot because of, you know. All of y’all are now on the team. I’m spending a majority of my time. Make sure that we can now go advertise for services and go really, knock on doors and and
146 00:21:28.110 ⇒ 00:21:39.699 Uttam Kumaran: push a lot of data insights into a lot of companies that really need our type of work. Basically the pitch I give, you know, to a lot of companies is when you go hire a full time employee.
147 00:21:39.700 ⇒ 00:21:59.379 Uttam Kumaran: Not only are they really expensive, they typically have only done implementations, maybe once or twice typically have not used Dbt in a lot of the modern tooling and typically are not specialized in every area. But why do you hire brainforward? Is that pretty much? Do you hire us? Right? You hire specialists in every single area who move incredibly fast?
148 00:21:59.380 ⇒ 00:22:10.500 Uttam Kumaran: We basically can just start tomorrow on any sort of work. And across the 5 of us, we’ve seen so many different types of data issues. And you know, kind of ways of managing, and so
149 00:22:10.500 ⇒ 00:22:30.399 Uttam Kumaran: that alone justifies its price. And you know that pitch has been refined a little bit over time. But that’s kind of how I started a company. Basically. So that’s the pitch on continue to run with ideally, we go after bigger clients, more hairier data problems. And we’re all able to kind of work as a crew, but for the foreseeable future this is basically
150 00:22:30.400 ⇒ 00:22:38.420 Uttam Kumaran: the crew. So you know, on our plate right now we have 2 clients. We have we have 3 clients. Actually, we have Ampla, which is
151 00:22:38.680 ⇒ 00:23:04.439 Uttam Kumaran: they are a Cpg focused bank. They offer financing services to Cpg companies. We have asset link. They are startup for wealth managers and financial advisors to kind of match, basically like Linkedin. And then we have full parts to go, which is like an e-commerce. Company that sells pool parts online. Both all 3 of them are in different phases, startups established companies. All 3 of them are different levels of data maturity.
152 00:23:04.440 ⇒ 00:23:15.920 Uttam Kumaran: But all have data challenges that we’ve all seen. So it’s been really awesome to kind of like ramp that up. There’s a bunch more people in the pipeline that hopefully, I’ll get to like expose you guys to
153 00:23:16.160 ⇒ 00:23:18.130 Uttam Kumaran: not only early on in like
154 00:23:18.150 ⇒ 00:23:29.750 Uttam Kumaran: how I’m like pitching those folks, but also as we begin to involve them and and share what we can do. So that you know something I’m I’m super super excited for next week.
155 00:23:29.950 ⇒ 00:23:42.239 Uttam Kumaran: I wanted to do take a time, maybe on a Monday again, just to go through our entire like tech stack. Basically. So looking at how we run things on Dvt, how we run things on light dash, how we
156 00:23:42.270 ⇒ 00:24:04.989 Uttam Kumaran: look! Run things on github action snowflake. And just like, understand where people are interested in learning more like a lot of this stuff is just like in my brain. So I’m hoping to kind of like disseminate. We have a lot of docs for everything. But also I wanna kind of share everybody else’s skill set hopefully as the weeks go by. Each of us can share interesting parts of like kind of where we sit in the stack.
157 00:24:04.990 ⇒ 00:24:20.480 Uttam Kumaran: And the last thing is like, although, yeah, this is like a data company, I would say, I am very interested in not running this like a normal company. But for all like the best reasons, meaning my focus is not to keep engineers and meetings. My focus is not to hire
158 00:24:20.540 ⇒ 00:24:47.299 Uttam Kumaran: basically nonengineering folks. I’m an engineer. I still do engineering work. I just only wanna work with engineers. And so pretty much, I try to run as need of an operation as possible, which basically means we spend more time working on clients. And we all make more money. So as much as we can leverage the tools we have at our disposal, whether writing loom, slack, huddle, zoom, we should do. We’re all in different time zones. So as much as we can rely on Async communication.
159 00:24:47.490 ⇒ 00:25:00.080 Uttam Kumaran: It’s best, and then also the benefit is, we can kind of work kind of whenever we feel like there’s not really any structured hours or anything. Everything is really based on deliverables. And I’m kind of gonna start doing a better job of communicating like
160 00:25:00.150 ⇒ 00:25:03.949 Uttam Kumaran: project plans and things like that. But again, Mike.
161 00:25:04.020 ⇒ 00:25:14.639 Uttam Kumaran: I I’m a fan of process. But I’m not a fan of process. Just for process sake, I’ve brought on a lot of you guys cause you guys all have really high agency. And you guys take on tasks and
162 00:25:14.680 ⇒ 00:25:20.649 Uttam Kumaran: run a million miles an hour towards that. And that’s exactly what we’re selling to folks.
163 00:25:20.750 ⇒ 00:25:46.729 Uttam Kumaran: And so that being said also, you know, if there’s any interesting people or smart people in your network that you’re also thinking are like would be really good fit for something like this. We’re probably not gonna bring on more people right now. But I’m always meeting with amazing people. So feel free to share about the company. And this time that work you’re doing. And often over the next 2 weeks I’m getting some more material prepared, like one pagers and things about us that hopefully we can share around our networks to drama, more business.
164 00:25:47.050 ⇒ 00:25:58.560 Uttam Kumaran: But that was a lot of me talking hopefully. I don’t have to talk too much more today. But, I don’t know if there’s anything else we wanted to talk about or anything else anyone else wants to share. Feel free.
165 00:26:00.090 ⇒ 00:26:01.880 Jack Tomei: What city are you in, Ryan?
166 00:26:03.050 ⇒ 00:26:07.070 Ryan Luke Daque: I’m in Sebusi. Like, middle of the Philippines.
167 00:26:07.530 ⇒ 00:26:14.290 Uttam Kumaran: Yeah, we gotta do a Philippine strip as soon as I can get the budget I can tour you guys.
168 00:26:14.990 ⇒ 00:26:16.139 Ryan Luke Daque: how do you get a beat?
169 00:26:16.650 ⇒ 00:26:20.100 Uttam Kumaran: So I I’ve been hiring some people
170 00:26:20.190 ⇒ 00:26:34.290 Uttam Kumaran: in the Philippines for, like a bunch of other like related like Va and other admin related work early on. And I was like, Okay, I wonder if there’s good data people? And then I saw one profile on there with Dvt. And that was like Ryan’s. And then we talked. And I’m like
171 00:26:34.630 ⇒ 00:26:53.859 Uttam Kumaran: Dude. You’re the you’re the man like I really would love someone to come in and just like Rip, Dvt up with me. And we talked. And I was like, I don’t know. It’s basically the same process with everyone, whereas, like, Hey, try some workout. Let me know what you think. And so then we kinda went from there. So I feel really lucky. I yeah,
172 00:26:53.910 ⇒ 00:26:57.170 Uttam Kumaran: I feel glad to have people that are that are everywhere. But also.
173 00:26:57.240 ⇒ 00:27:02.089 Uttam Kumaran: I think some of this work isn’t as accessible around the globe. But again, ultimately, it’s data work.
174 00:27:02.140 ⇒ 00:27:13.020 Uttam Kumaran: And so I feel happy that I’m able to kind of like, bring everyone together, and then just smart people wherever you are. So I don’t. You know it doesn’t matter if you’re on. If you’re even on the planet, you know, as long as you can do the data work.
175 00:27:13.160 ⇒ 00:27:14.490 Jack Tomei: Yeah, it’s kind of cool.
176 00:27:14.720 ⇒ 00:27:15.700 Uttam Kumaran: Yeah.
177 00:27:15.890 ⇒ 00:27:17.260 Jack Tomei: I want to go to the Philippines.
178 00:27:17.590 ⇒ 00:27:27.540 Uttam Kumaran: Yeah, I think we gotta arrange a company trip. I’ll have to talk. I wanna go to Buenos Aires. I know, Jack, do you watch a lot of soccer? So maybe there’s some connection there.
179 00:27:27.640 ⇒ 00:27:29.220 Jack Tomei: I’ve been reading a lot about
180 00:27:29.310 ⇒ 00:27:47.249 Jack Tomei: you. Been there when they were won the World Cup and everything. Was that crazy? Yeah, yeah, I was there. It was very crazy. What there was like 4 million people. I just watched like the messy on Hbo they just released like the messy documentary. And it’s just seeing the parades and stuff. It’s wild.
181 00:27:47.680 ⇒ 00:27:56.880 Agustin: Yeah. A a guy through himself from a bridge to fall into the bus where Messi was, but he fell, and I think he died. Yeah.
182 00:27:56.910 ⇒ 00:27:58.340 Jack Tomei: holy trace.
183 00:27:59.710 ⇒ 00:28:06.980 Uttam Kumaran: It was wild, just we just can’t do an off site in New Orleans like.
184 00:28:07.470 ⇒ 00:28:17.280 Uttam Kumaran: yeah, I gotta start with the Us. I gotta drive to New Orleans. I’m gonna fly to Seattle sometime soon when I’m back in the bay, maybe. And then.
185 00:28:17.380 ⇒ 00:28:23.519 Uttam Kumaran: yeah, maybe we can do a little hop to Argentina, and then we’ll do. We’ll do hopefully a larger off site facility.
186 00:28:23.850 ⇒ 00:28:25.840 Jack Tomei: I was in New York cool enough.
187 00:28:25.870 ⇒ 00:28:44.200 Jack Tomei: I was in New Orleans for the first time, like a few years ago, but it was right after the like hurricane, or whatever happened, and I was just there to see Eric Clapton with my dad. I bought him like tickets to see Eric Clapton.
188 00:28:44.410 ⇒ 00:28:55.310 Jack Tomei: They’re just like a weekend we show up, and our hotel is fucking. There’s a dude sitting outside in the fold foldable chair, and he’s like, are you guys here to check in. And we’re like, Yeah, he’s like, we’re close dude, like, we don’t have any power.
189 00:28:55.400 ⇒ 00:29:01.559 Jack Tomei: So we had to like scramble the night out to figure it out. The house didn’t have power for 27 days.
190 00:29:01.720 ⇒ 00:29:13.489 Jack Tomei: Yeah, the whole city seemed like it was just, and it was pouring rain. It was just. It was wild. Yeah, it was. It was. It was fucked, not the best time to visit. So maybe we gotta redo that
191 00:29:13.640 ⇒ 00:29:16.569 Uttam Kumaran: I’ve just been once for like a bachelor party and
192 00:29:16.830 ⇒ 00:29:20.240 Uttam Kumaran: like it was insane, like, I don’t know.
193 00:29:20.280 ⇒ 00:29:25.040 Uttam Kumaran: Yeah, I wanna go. I feel like Patrick lives a pretty calm life. I wanna go wherever he is.
194 00:29:25.240 ⇒ 00:29:32.890 Uttam Kumaran: and like, stay in that part of New Orleans, I might. It’s so I’m like across from the French border. But they’re like across the river.
195 00:29:33.130 ⇒ 00:29:35.810 Patrick Trainer: So you have to be pretty intentional to get here.
196 00:29:35.930 ⇒ 00:29:49.710 Patrick Trainer: So it’s like we don’t get, or my neighborhood specifically doesn’t get like the. And I like riffraff stumbling through. But it’s like, when you want to get into the mix, it’s literally just a ferry ride across. Takes 5 min.
197 00:29:50.860 ⇒ 00:29:54.630 Jack Tomei: Yeah, that’s nice. You can do it if you want to see it all the time.
198 00:29:54.870 ⇒ 00:29:55.760 Uttam Kumaran: Yeah.
199 00:29:57.080 ⇒ 00:29:59.319 Patrick Trainer: Where? Where in Seattle are you, Jack?
200 00:30:00.000 ⇒ 00:30:11.789 Jack Tomei: I’m by Green Lake it’s called. It’s like not in the city. I have 2 big dogs, so like I’m not really into the whole city thing. I wanted to be like from a suburb.
201 00:30:11.990 ⇒ 00:30:14.030 Jack Tomei: Okay, sweet.
202 00:30:14.160 ⇒ 00:30:25.689 Jack Tomei: You. What I’ve been up there? I’ve taken this there over to would be. Yeah. I did like, yeah, just like a little road trip, kind of like seeing the State. It’s such a beautiful area. Dude, this whole place
203 00:30:25.820 ⇒ 00:30:30.990 Patrick Trainer: that up in like what’s it? Deception pass
204 00:30:31.210 ⇒ 00:30:48.669 Jack Tomei: sounds super cool. Deception passes like a great movie. It’s like it’s like the skinniest part where that’s like the sound exits into the ocean. So it’s like the water gets super rough and fast. Apparently there’s a there that you can serve, too. It it’s like
205 00:30:49.660 ⇒ 00:30:54.359 Patrick Trainer: crazy, beautiful, too, but it’s yeah. It’s really awesome. All the big trees, and
206 00:30:54.770 ⇒ 00:31:03.040 Jack Tomei: the only thing is it rains pretty much every day. Today is the first day that I’ve seen the sky, and in a few weeks. So I’m excited.
207 00:31:03.740 ⇒ 00:31:07.489 Jack Tomei: It’s very like, just cloudy. And you know, you know how it is.
208 00:31:07.630 ⇒ 00:31:09.010 Uttam Kumaran: Yeah.
209 00:31:09.870 ⇒ 00:31:16.760 Uttam Kumaran: okay, great. Well, I think that’s basically all I had. I guess we’re all talking slack. I’m trying to get everyone together again
210 00:31:16.850 ⇒ 00:31:44.669 Uttam Kumaran: next week. But again, everybody here is everybody’s resource. If there’s any questions about any part of the stack or anything. People wanna learn hopefully over the next few weeks we can kind of start working on some more like cross project infrast stuff like, I, wanna implement SQL, Fluff, I wanna implement some of those formaters we wanna try self hosting real. And some of our own bi’s code solution. So basically, there’s a couple of pieces of software where you can code the actual dashboards.
211 00:31:44.720 ⇒ 00:31:59.720 Uttam Kumaran: And so basically, we wanna try some of those tools and hopefully, Augustine, we can nail some of the provider stuff so we can do a demo of that to everybody. So there’s a lot of like internal infrast stuff that is like pretty on the edge that I hopefully like we can.
212 00:31:59.730 ⇒ 00:32:01.480 Uttam Kumaran: I’ll kinda share of. And then.
213 00:32:01.490 ⇒ 00:32:12.580 Uttam Kumaran: yeah, I’m basically going like full sales warrior mode and like, that’s what a lot of my time is gonna be spent is like on the phone and like, pitch in and sell in. So
214 00:32:12.710 ⇒ 00:32:19.070 Jack Tomei: once you get one figure, once you get like something that you that you feel comfortable sharing. I have a lot of people
215 00:32:19.910 ⇒ 00:32:34.140 Uttam Kumaran: people that their own sort of similar things where they’re very in touch with, I think a lot of small startup companies. That’d be good people. Yeah. So we’re so we’re doing couple of things we’re doing. We have one pager. We have some case studies. So contents written, I’m getting it all redesigned.
216 00:32:34.140 ⇒ 00:32:59.060 Uttam Kumaran: Hopefully, I’ll get some. I’ll get some feedback on the designs like I’m trying to. There’s, I’m trying to make them look like absolutely stunning, like basically works of art. One pagers. And then, yeah, hopefully, everybody can kind of share that with the network design goes a long way, for that stuff goes a long way. Because again, we’re competing with like Mckinsey. And it looks ugly. Yeah. So I wanna people to look at that
217 00:32:59.060 ⇒ 00:33:16.629 Uttam Kumaran: be like this looks great. I just want to print this out and put on the wall. So that’s one thing, and then the other thing is we find a lot of assistance working with other agencies. So if there’s another design agency or another like software agency. Or, for example, I’m working with an sap agency.
218 00:33:16.630 ⇒ 00:33:33.819 Uttam Kumaran: We come in. We’re like, Hey, where your snowflake experts or we’re your data people. If you guys have any client that S smells or sounds like data, you send them our way. And same thing. We. I’ve sent people, clients that come past me that we can’t work on. So I think once I get those materials done, basically, that’s like what my week is focused on
219 00:33:33.900 ⇒ 00:33:56.140 Uttam Kumaran: we’ll just like Rip, that. And again, the the last thing I’ll say is like, I told everybody that’s probably individually, is that our product is like you guys and us. So that’s the people, not really the data. So we have an amazing group of people here. And I’m so confident like, we can just walk in anywhere and pretty much do any job necessarily on the data side. So
220 00:33:56.470 ⇒ 00:34:08.849 Uttam Kumaran: our goal is just to translate that via documents and our pitch. And then, of course, when they meet us, our smiles and things like that. So I’m excited to kind of make that happen. So it’s gonna be a. It’s gonna be really exciting here.
221 00:34:09.920 ⇒ 00:34:18.450 Jack Tomei: Oh, yeah, Brian needs to get some sleep. It’s too late. Yeah. Ask him how many times I’ve told him this, but
222 00:34:18.750 ⇒ 00:34:21.520 it is the end of the day, I think, for him.
223 00:34:22.310 ⇒ 00:34:24.270 Jack Tomei: Awesome nice to meet you guys.
224 00:34:24.510 ⇒ 00:34:29.100 Ryan Luke Daque: Nice to meet you.
225 00:34:29.210 ⇒ 00:34:30.639 Ryan Luke Daque: Bye, bye, bye.