Meeting Title: Uttam—Kumaran <> Agustin-Bergoglio Date: 2024-02-19 Meeting participants: Agustin, Uttam Kumaran
WEBVTT
1 00:01:41.000 ⇒ 00:01:41.910 Uttam Kumaran: Hello!
2 00:02:14.670 ⇒ 00:02:15.350 Uttam Kumaran: Well.
3 00:02:19.260 ⇒ 00:02:20.010 yes.
4 00:03:31.740 ⇒ 00:03:33.309 Uttam Kumaran: hey! Can you hear me? Hello!
5 00:03:33.920 ⇒ 00:03:37.480 Uttam Kumaran: Yes, I can hear you. Nice to meet you. Nice to meet you, too.
6 00:03:37.960 ⇒ 00:03:40.550 Agustin: I’m doing good. Your name is
7 00:03:40.810 ⇒ 00:03:42.150 Agustin: Wutamu Bhutan.
8 00:03:42.220 ⇒ 00:03:43.610 Uttam Kumaran: the top. Yes.
9 00:03:44.620 ⇒ 00:03:47.279 Agustin: thank you. You pronounce your name, Augustine.
10 00:03:47.920 ⇒ 00:03:50.090 Agustin: That’s right. Yeah.
11 00:03:50.590 ⇒ 00:04:06.410 Uttam Kumaran: Nice name. Thank you. I know it’s not a common name in the Us. It is common here in my country. No, I just think II met some Augustine, but your spelling is unique. A. GUI feel like, sometimes, it’s a UG,
12 00:04:07.410 ⇒ 00:04:08.430 Agustin: yeah, yeah.
13 00:04:08.620 ⇒ 00:04:13.430 Uttam Kumaran: what is the what’s like? The origination of Augustine is like a Christian name.
14 00:04:14.670 ⇒ 00:04:19.800 Agustin: Yeah, that’s a very good question. And I think it’s it’s Latin.
15 00:04:19.850 ⇒ 00:04:21.950 Agustin: Okay? Yeah.
16 00:04:22.630 ⇒ 00:04:24.519 Uttam Kumaran: okay, great. Where are you?
17 00:04:24.590 ⇒ 00:04:26.319 Agustin: And basis?
18 00:04:26.850 ⇒ 00:04:31.999 Uttam Kumaran: I’m based in Argentina, Latin America, South America, where in Argentina.
19 00:04:32.340 ⇒ 00:04:34.689 Agustin: in Buenos Aires, the capital city.
20 00:04:34.700 ⇒ 00:04:37.210 Uttam Kumaran: Okay. Nice. How do you like it?
21 00:04:37.580 ⇒ 00:04:40.959 Agustin: I really like it. Yeah, were you? Were you? You grew up there?
22 00:04:41.370 ⇒ 00:04:50.619 Agustin: Yeah, I did all my life. That’s right. Yeah. And I studied here my whole life. I graduated here.
23 00:04:50.630 ⇒ 00:04:57.160 Agustin: Everything. Yeah, nice. I’ve I’ve seen a lot of videos of when it’s eyes have never been. But I want to go to Argentina
24 00:04:57.220 ⇒ 00:05:04.639 Uttam Kumaran: sometime in the next 2 years I wanted to go visit me, and I wanted to go visit. Sorry. So
25 00:05:04.820 ⇒ 00:05:07.580 Agustin: that’s nice. It’s very
26 00:05:07.840 ⇒ 00:05:10.239 Uttam Kumaran: yeah. I’m located in Austin, Texas.
27 00:05:11.060 ⇒ 00:05:11.960 Agustin: Okay.
28 00:05:12.410 ⇒ 00:05:16.879 Agustin: Never been there. I’ve been in the Us. Not Floria.
29 00:05:16.930 ⇒ 00:05:18.629 Agustin: where in Florida? Miami, yeah.
30 00:05:18.810 ⇒ 00:05:22.639 Uttam Kumaran: Orlando Miami. Okay. Okay, how? Recently?
31 00:05:23.580 ⇒ 00:05:30.339 Agustin: 5 years ago, yeah, it was a long time ago. I yeah. I went there to visit some relatives
32 00:05:30.660 ⇒ 00:05:32.060 Agustin: that leave the hair
33 00:05:32.130 ⇒ 00:05:38.019 Agustin: until get to visit Disney Parks or London.
34 00:05:38.380 ⇒ 00:05:51.510 Uttam Kumaran: Nice? Yeah, I’m in. II grew up in California, and then I lived in New York City. for 5 years. And then I moved here to Austin, Texas. So I lived everywhere in the Us. Somehow.
35 00:05:51.600 ⇒ 00:05:58.959 Uttam Kumaran: but you know I, my, my career has been in data, engineering and computer engineering. And then, I recently started
36 00:05:59.240 ⇒ 00:06:27.579 Uttam Kumaran: a company that I’ll kind of tell you about today, Brainforge, which is kind of helping other companies do data engineering modeling. And then, internally, we have some products as well. So I started the company last April and thankfully still in business. So have a couple of clients that we’re working directly for happy to kind of share a little bit more about what we do. But we’d love to hear about your background. In data you mentioned kind of like, you have experience, and like
37 00:06:27.700 ⇒ 00:06:29.520 Uttam Kumaran: python like
38 00:06:29.740 ⇒ 00:06:38.590 Uttam Kumaran: spark things like that just interested in, like kind of some of the stuff that you’ve done in the past, and like what you like to do, and data and just kind of overall. Hear about your background.
39 00:06:39.670 ⇒ 00:06:49.989 Agustin: Okay, that’s right. First, yeah, I did a bit of research with your company, not not like in depth, but just to know what it’s all about for.
40 00:06:50.220 ⇒ 00:06:51.549 Agustin: Yeah, I did some.
41 00:06:51.640 ⇒ 00:07:03.110 Agustin: I read about some E case studies that are published there to understand more about how the solutions are made. I think it has more like a holistic approach, right? The problems.
42 00:07:03.430 ⇒ 00:07:04.120 Agustin: And
43 00:07:05.010 ⇒ 00:07:06.710 I really like that.
44 00:07:06.870 ⇒ 00:07:12.510 Agustin: I well, from my side, I’ve been working in the engineering engineering for 3 and a half years.
45 00:07:12.920 ⇒ 00:07:20.690 Agustin: I started more like in the data science role, combined with data engineering working with Disney as a main client.
46 00:07:20.930 ⇒ 00:07:25.879 Agustin: I was part of doing the dashboards processing the data, pulling data from Apis.
47 00:07:26.180 ⇒ 00:07:27.610 Uttam Kumaran: Okay, yeah.
48 00:07:27.870 ⇒ 00:07:34.159 Agustin: Cleaning transforming data. Then I switch to Mexican Startup or Houston.
49 00:07:34.170 ⇒ 00:07:42.889 Agustin: where it was part of creating and maintaining pipelines for internal, like internal clients, such as data, science finance products.
50 00:07:43.220 ⇒ 00:07:44.010 Uttam Kumaran: Okay?
51 00:07:44.220 ⇒ 00:07:48.439 Agustin: And and then II switched to
52 00:07:49.090 ⇒ 00:07:50.709 which is neighbor country.
53 00:07:50.780 ⇒ 00:07:58.840 Agustin: They’re working with 2 clients in the Us. One in the hospitality industry, processing big data
54 00:07:58.890 ⇒ 00:08:07.260 Agustin: from international hotel chains and the other one was a healthcare client that needed to like
55 00:08:07.540 ⇒ 00:08:11.899 Agustin: great and I not great, but code and already
56 00:08:12.770 ⇒ 00:08:19.799 Agustin: deterministic algorithm. Right? That was my my doctor and I had to create an Api
57 00:08:20.080 ⇒ 00:08:23.200 Agustin: to present the data. Python library.
58 00:08:23.250 ⇒ 00:08:28.709 Agustin: Yeah. 5 lines dashboards. That client specifically, yeah.
59 00:08:28.750 ⇒ 00:08:39.020 Agustin: As well as a lot of unit tests to test the accuracy of the algorithm which was for that you built. You built like the rest Api, and, like you hosted it, or like, what was the architecture for that
60 00:08:40.020 ⇒ 00:08:48.690 Agustin: it was hosted on a Amazon of services. It was hosted on Ecs Us. Api. So
61 00:08:49.020 ⇒ 00:08:49.710 yeah.
62 00:08:49.830 ⇒ 00:08:56.380 Agustin: And I also made a python library for data scientist to work with. Yeah.
63 00:08:56.530 ⇒ 00:08:58.220 sorry in my throat.
64 00:09:00.190 ⇒ 00:09:04.759 Agustin: Yeah, that’s basically my experience right now.
65 00:09:04.880 ⇒ 00:09:11.360 Agustin: And I’m looking for a change because this company doesn’t have any more data
66 00:09:11.620 ⇒ 00:09:15.220 Agustin: related projects. and they had to switch me to
67 00:09:15.290 ⇒ 00:09:19.099 Agustin: a full stack development pressure which I do not like.
68 00:09:19.210 ⇒ 00:09:29.619 Agustin: and because well, my career has been in data, right? And I want to keep Pursay. I want to keep pursuing this path right. This current path.
69 00:09:29.820 ⇒ 00:09:34.520 Agustin: I enjoy working with the cloud services like Selena infrastructure
70 00:09:34.830 ⇒ 00:09:37.949 and setting things from scratch and creating solutions
71 00:09:38.070 ⇒ 00:09:39.140 Agustin: from there.
72 00:09:41.130 ⇒ 00:09:46.400 Agustin: Of of of course I’m open to freelancing or contracting, depending.
73 00:09:46.430 ⇒ 00:09:48.370 Agustin: want the crochets or
74 00:09:48.450 ⇒ 00:09:50.479 Agustin: the company? Right? Yeah.
75 00:09:50.710 ⇒ 00:10:01.190 Agustin: Kind of like the entire stack of data from
76 00:10:01.210 ⇒ 00:10:02.889 Uttam Kumaran: like Etl
77 00:10:03.010 ⇒ 00:10:14.559 Uttam Kumaran: airflow pipelines all the way to like dashboarding. So it seems like most of your background. And the stuff you like to do is more on, like the data engineering side, writing pipelines interacting with Apis, and like kind of moving
78 00:10:14.570 ⇒ 00:10:16.950 Uttam Kumaran: flat files or data around.
79 00:10:17.350 ⇒ 00:10:34.780 Uttam Kumaran: I would say, within within our firm, we work with companies that are all over the place. So there’s a couple of companies where we’ve written our own pipelines. Or we’re using 5 train to bring data in. We do a lot of data modeling. So a lot of Dbt,
80 00:10:34.910 ⇒ 00:10:47.229 Uttam Kumaran: dbt, writing, sequel writing, they’re writing tests. Within Dbt, and then we do have a component of like doing some dashboarding. Most of our work is in analytics, engineering, and doing a lot of modeling
81 00:10:47.240 ⇒ 00:10:48.720 Uttam Kumaran: as well as
82 00:10:48.820 ⇒ 00:10:53.950 Uttam Kumaran: like either replicating etl workflows or writing some new ones.
83 00:10:54.060 ⇒ 00:11:01.219 Uttam Kumaran: A couple of the projects that I think are would be really interesting to kind of get your perspective on, and kind of. I think there is.
84 00:11:01.300 ⇒ 00:11:12.740 Uttam Kumaran: you know, opportunity internally now is 2 things, one, we have a current client that is using a legacy etl provider for 2 different pipelines
85 00:11:12.770 ⇒ 00:11:17.949 Uttam Kumaran: they connect to Walmart’s Api, and they also connect to an inventory system called unleashed.
86 00:11:18.120 ⇒ 00:11:35.689 Uttam Kumaran: All this was run. They have like a rest. Api kind of like work flow builder. I wanna take it out of there because they’re currently paying like a couple of 100 bucks a month to use that. And I wanna re replicate it, using python snowpark within Snowflake you you ever use like Snowflake before?
87 00:11:37.110 ⇒ 00:11:41.399 Yeah, I have used Snowflake not I more like a more
88 00:11:41.430 ⇒ 00:11:49.199 Agustin: sorry. Let me try to rephrase it more like a storage solution for warehousing, not so much for
89 00:11:49.280 ⇒ 00:11:59.699 Agustin: processing. If that makes sense. No, I created external tables for stages and created modeling the warehouse for clients.
90 00:12:00.190 ⇒ 00:12:03.770 And that’s more. Yeah, that’s my experience.
91 00:12:04.220 ⇒ 00:12:13.489 Uttam Kumaran: So recently, in the last like few months Snowflake released this functionality called snowpark. Basically, you can run python workflows
92 00:12:13.530 ⇒ 00:12:15.680 Uttam Kumaran: directly in snowflake.
93 00:12:15.810 ⇒ 00:12:20.789 Uttam Kumaran: What does that allow you to do? Previously you have to run python workflows and like airflow, and then
94 00:12:20.940 ⇒ 00:12:34.989 Uttam Kumaran: Jbc. Snowflake and then move stuff. Now you can run everything within snowflake, so not only does the compute run there. You can actually import a lot of libraries and then interact with the snowflake table. So
95 00:12:35.190 ⇒ 00:12:50.840 Uttam Kumaran: and you and again, you can orchestrate everything with snowflake tasks, which is awesome because you don’t need airflow. You don’t need, like all these external stuff. You could do everything there. So my idea was to try and replicate that Etl work flow directly in Snow Park.
96 00:12:51.170 ⇒ 00:12:55.250 Uttam Kumaran: it’s pretty basic like calling some rest Api paginating
97 00:12:55.350 ⇒ 00:13:04.780 Uttam Kumaran: and then incrementing and incrementally Updating a table. That’s one project. The second thing is, are you familiar with the snowflake like data marketplace
98 00:13:05.750 ⇒ 00:13:07.100 Agustin: to Vienna? Snow?
99 00:13:07.310 ⇒ 00:13:16.770 Uttam Kumaran: So Amazon has this you, if you might, if you Google real quick, you’ll kind of see it. So Snowflake, now, is a marketplace where data providers can list data.
100 00:13:16.810 ⇒ 00:13:19.960 Uttam Kumaran: And I’ll give you an example of
101 00:13:20.600 ⇒ 00:13:22.580 one that we did
102 00:13:25.540 ⇒ 00:13:29.999 Uttam Kumaran: give me 1 s.
103 00:13:39.650 ⇒ 00:13:48.309 Agustin: In my experience I have worked more more with Etl pipelines, like processing them in memory, mainly in a cluster. Yeah, cluster. For example.
104 00:13:48.370 ⇒ 00:13:50.250 Agustin: I’m doing. I,
105 00:13:50.480 ⇒ 00:14:00.430 Agustin: yeah, the approach is more. And let’s say updated or novel approach with that may work very well with some clients or solutions.
106 00:14:00.550 ⇒ 00:14:01.789 Agustin: I really like it.
107 00:14:02.370 ⇒ 00:14:07.700 Uttam Kumaran: Yeah. And II think again, it’s for me. It’s just like, How do I use less tools and accomplish the same thing. And then
108 00:14:07.960 ⇒ 00:14:17.410 Uttam Kumaran: again, if we don’t have to spin up our own airflow instance, we don’t have to do orchestration elsewhere. It’s really easy. So I just sent you a link in zoom. This is an example of a
109 00:14:17.520 ⇒ 00:14:22.459 Uttam Kumaran: of a of a data listing that we created for a client.
110 00:14:22.970 ⇒ 00:14:32.550 Uttam Kumaran: Basically, we have a client that needs this data from Fema Fema is a government data set in the Us for like disasters.
111 00:14:32.630 ⇒ 00:14:56.080 Uttam Kumaran: like, you can think if there’s a flood, or if there’s a hurricane, Fema is the government agency that kind of handles that disaster and like the response. So they needed this data set. So Snowflake actually has a process by which you could write the Etl list the data and then purchase it from a provider right? And what does this help you do? If you’re a company that needs this data, you don’t have to go right. Your own pipeline
112 00:14:56.080 ⇒ 00:15:09.069 Uttam Kumaran: figure out the endpoint definitions, host that you could just pay me and I host it. And there’s like a provider. So the second project that we’re working on is actually increasing the amount of listings that we’re putting up.
113 00:15:09.110 ⇒ 00:15:16.139 Uttam Kumaran: And this is like kind of like a very brand new part of snowflake. But there’s a lot of, I think
114 00:15:16.440 ⇒ 00:15:23.219 Agustin: And if if I understood correctly, your idea is to make data available for other clients and sell it.
115 00:15:23.840 ⇒ 00:15:36.719 Uttam Kumaran: Yeah, so basically, we host the pipeline. And you can just get access to that data via the marketplace. The the actual, like real unlock here is that the entire pipeline also runs on Snowflake.
116 00:15:36.900 ⇒ 00:15:49.069 Uttam Kumaran: So we have a python. We have a python script that calls the open Api open the open Api Fema data set brings the data in and it gets run by tasks.
117 00:15:49.240 ⇒ 00:16:01.089 Uttam Kumaran: and then that gets shared externally to the marketplace. So the second project is adding a couple of more listings and then improving the general workflow of our brain forage.
118 00:16:01.140 ⇒ 00:16:06.030 Uttam Kumaran: Updates, listings. So those are the 2 different projects.
119 00:16:06.210 ⇒ 00:16:14.790 Uttam Kumaran: this I don’t. I would say this was one seem like more interesting than the other. I also kind of like I’ll give you kind of a little bit of like how
120 00:16:15.200 ⇒ 00:16:20.310 worked and brought on engineers. So again, my background brain forge. Again, we
121 00:16:20.420 ⇒ 00:16:35.009 Uttam Kumaran: we kind of work for a multitude of different clients. But we also have some internal projects that we’re working on. Typically, I like to just bring people in and try out working on a project for like a week, and just kind of seeing how we like working with each other. I can add you to slack
122 00:16:35.080 ⇒ 00:16:45.679 Uttam Kumaran: Google and give you access to everything. And they kind of just gauge like, whether you like click work, whether we can kinda communicate together and then just kind of getting a sense
123 00:16:45.810 ⇒ 00:17:01.489 Uttam Kumaran: like I I’ve I’ve interviewed as an engineer. I also interview a lot of engineers like, I just like people working with people. I think that’s way more fun and actually way more effective at like gauging, whether someone is good, and also for you to gauge like
124 00:17:01.490 ⇒ 00:17:20.910 Uttam Kumaran: whether you wanna work with us or whether we’re like weirdos. So I think that’s like, I think that’s also like a healthy way of like everyone understanding so I’d love to see whether maybe we could spend like a week. If you have availability like taking on one of those projects, and just kind of like walking through and kind of seeing your skill set.
125 00:17:21.819 ⇒ 00:17:31.870 Agustin: And were you thinking about paying hourly, for for example. Yeah. So so for for all of our engagements, this would be like
126 00:17:32.180 ⇒ 00:17:33.940 Uttam Kumaran: on a contract basis.
127 00:17:33.980 ⇒ 00:17:40.109 Uttam Kumaran: I don’t know if we have the bandwidth right now, for like 8 h of work per day.
128 00:17:40.120 ⇒ 00:17:43.520 Uttam Kumaran: but it at least be a few hours of work per day worth of
129 00:17:43.680 ⇒ 00:17:53.419 Uttam Kumaran: compensation. Depending on how we look at the projects. We can price things out on a project basis. But yeah, it would just be like a couple of hours a day.
130 00:17:53.430 ⇒ 00:17:56.020 Uttam Kumaran: I. What what I would say is for the one project
131 00:17:56.290 ⇒ 00:18:03.379 Uttam Kumaran: I would provide you with all the specs and all the current implementation, and we could say, hey, yeah, roughly, should take about
132 00:18:03.520 ⇒ 00:18:22.019 Uttam Kumaran: 5 days, couple of hours per day, and then we can kind of just understand whether that’s effective. And then that way, at the end of the time we can both review the work that’s been done and kind of understand like, Hey, is this interesting work? Can we both do business together.
133 00:18:22.060 ⇒ 00:18:34.690 Agustin: these 2 projects, if everything goes well. Ho! How long do you think they can take hours? I’m not an exact number, but maybe 100 h for both, or something like that.
134 00:18:34.840 ⇒ 00:18:41.499 Uttam Kumaran: Yeah, so ideally at the I would start with the first project which is just rewriting the Etl workflows
135 00:18:41.560 ⇒ 00:18:46.679 Uttam Kumaran: for an existing client in Snowflake. I think that’s honestly probably like
136 00:18:47.380 ⇒ 00:18:59.599 Uttam Kumaran: a 20 h project like again, just hearing your background. It’s very, very. It’s like, not that complicated. It’s a pretty simple rest, Api call. We already have it running somewhere. So I don’t think that’s gonna take.
137 00:18:59.990 ⇒ 00:19:02.119 Uttam Kumaran: you know, more than 20 h of work
138 00:19:02.180 ⇒ 00:19:06.180 Uttam Kumaran: which hopefully, is like, you know, roughly, a couple of hours a day for a week.
139 00:19:06.600 ⇒ 00:19:12.259 Uttam Kumaran: that would be. That’d be my suggestion as we start with just one project
140 00:19:12.330 ⇒ 00:19:31.220 Uttam Kumaran: kind of get you in everything, and then we can kind of decide after there. I guess my other question would be like you mentioned that you are currently working somewhere. So like, do you have availability for like contract type work like, what does that typically look like? And then, yeah, I mean, I have like 4 or 5
141 00:19:31.480 ⇒ 00:19:38.439 Agustin: more like 4 HA day availability. Now, if everything goes well, I’m open to
142 00:19:38.610 ⇒ 00:19:40.700 Agustin: quitting and changing
143 00:19:40.800 ⇒ 00:19:43.440 Agustin: Russia. Yeah.
144 00:19:43.530 ⇒ 00:19:44.740 Uttam Kumaran: if I have more.
145 00:19:44.940 ⇒ 00:20:09.369 Agustin: one bandwidth, right? If I get more of a project from you or other clients, yeah, I’m open to changing, from contracting to freelancing or whatever. Yeah, there can be the same. Yeah. Yeah. And to give you context about the business, like, we’re, we’re a new business. So a lot of the thing that I my job here to do is to find amazing people, everybody in the company, our engineers, by the way, there’s no business people. I
146 00:20:09.440 ⇒ 00:20:15.090 Uttam Kumaran: II was an engineer. I led engineering teams. I also worked as a product manager and led product.
147 00:20:15.110 ⇒ 00:20:28.480 Uttam Kumaran: For me. I love just working with engineers and keeping things very streamlined. So there’s like, not a lot of meetings. There’s like, not really like a ton of like Bs. You gotta sit through. Everything is like on slack, and then we call on zoom
148 00:20:28.480 ⇒ 00:20:52.249 Uttam Kumaran: so for the most part, I think it should be pretty hardcore, just like engineering work. The benefit is like as you kind of get on board, and I get understanding your skill set that allows me to go get more projects and kind of understand cool now that we have the internal skill sets to tackle these things. So, although the work may start just a few hours a day, knowing that you have availability and kind of understanding how you work and the things you’re good at.
149 00:20:52.250 ⇒ 00:21:19.529 Uttam Kumaran: I’m gonna go get some more work for us. And then also we have a we have a couple of other members of the team who you’ll get to meet and then we can. There’s there’s 2 other people that are working. And then I have a couple of other people, friends of mine in the Us. That have like come in and worked on projects. Roughly, I would say, 6 or 7 people total but like 3, right now, including me, that are actually actively doing engineering work.
150 00:21:19.740 ⇒ 00:21:22.220 Uttam Kumaran: and then.
151 00:21:22.990 ⇒ 00:21:29.530 Uttam Kumaran: yeah, that’s that’s basically, I guess my other ask was like, what what is like, what’s your like expectation for like salary? And
152 00:21:29.560 ⇒ 00:21:36.500 Uttam Kumaran: yeah, I was gonna ask that. Do you have like an hourly rate mine in us dollars?
153 00:21:37.030 ⇒ 00:21:44.529 Uttam Kumaran: yeah, I would say, it’s gonna be probably close to like 30 to 40 bucks an hour.
154 00:21:44.550 ⇒ 00:21:47.820 Uttam Kumaran: I don’t know what. What do you think about that rate?
155 00:21:48.070 ⇒ 00:22:11.669 Uttam Kumaran: II can’t. I think again, I kinda wanna understand your current like availability and then depending on that really the challenge of the projects. So again, we, I wanna like definitely look at all the products that we have coming in the pipeline. And we could kind of say, like, roughly, it’s gonna take 20 h here, 40 h here. And then that way. It helps me understand? Okay, how much revenue are those products gonna build, and then how much I can
156 00:22:11.790 ⇒ 00:22:22.649 Uttam Kumaran: kind of give out. So I was thinking about in my mind before coming here. I was thinking about 30, $35 an hour. Okay, so let’s
157 00:22:22.900 ⇒ 00:22:25.050 Uttam Kumaran: let’s start some. Let’s start there.
158 00:22:25.070 ⇒ 00:22:29.150 Uttam Kumaran: But for this initial week, are you okay with just like trying out one of these?
159 00:22:29.380 ⇒ 00:22:38.370 Uttam Kumaran: And then if if it ends up working out, I’m happy to pay you for that time. Well, maybe we can. What we can do is just take on one of these projects.
160 00:22:38.470 ⇒ 00:22:44.780 Uttam Kumaran: For this week. And then I’m I’m gonna kind of put together a project scope and all the desk necessary details
161 00:22:44.880 ⇒ 00:22:49.390 Uttam Kumaran: and send that over to you. Does that sound? Okay?
162 00:22:50.290 ⇒ 00:22:56.209 Agustin: Yeah. I was thinking about not. I mean, we don’t know each other right.
163 00:22:56.430 ⇒ 00:23:11.679 Agustin: like like. Sorry, I think about the right word to use like some money in advance, but not too much like 20, if you agree, or 10% or whatever, just to
164 00:23:11.870 ⇒ 00:23:16.549 Uttam Kumaran: yeah, I’d be happy to send. I’d be happy to send something in advance.
165 00:23:16.610 ⇒ 00:23:38.679 Uttam Kumaran: maybe I’m just kind of like busy today and tomorrow. So maybe we can just like I can just get the project over to you, and then I’ll just send some stuff I’ll I can send you whatever is easiest like Paypal or whatever I have to take some time today to get the project, Doc. Set up and over to you. But I’m happy to, you know, if we’re if we’re able to say, like, Yeah, roughly, this is, gonna take
166 00:23:38.800 ⇒ 00:23:41.079 Uttam Kumaran: 15 or 20 h. Happy to send
167 00:23:41.100 ⇒ 00:23:43.179 Uttam Kumaran: 10% upfront.
168 00:23:43.490 ⇒ 00:23:52.119 Agustin: Okay, I really like it. I really like it. Thank you for that. I can sing. Yeah, I use wise, which is like a bill for account.
169 00:23:52.140 ⇒ 00:23:57.850 Agustin: But yeah, I mean, if you have some time later today, or whenever
170 00:23:58.060 ⇒ 00:24:06.889 Agustin: great, to meet again to show me more about the project. Is that what you were saying? Yeah. So basically, I’m gonna give you? I’m gonna give you
171 00:24:06.940 ⇒ 00:24:15.040 Uttam Kumaran: you are, are you? You? I assume you’re on slack because you’re on the DVD slack. So maybe I’ll I’m gonna invite you to our slack channel
172 00:24:15.070 ⇒ 00:24:42.930 Uttam Kumaran: and I’m just gonna use your same I’m just gonna use your same email. Would you? Actually, would you mind if I created you a brain for email account. And you could use that just cause you have login everything just for security. So I’ll I’ll create. I’ll create you that email. I’ll send it to you on the deep. I’ll send you a note on the Dbt slack. Once I get that set up and then everything will go through your personal email. And then, yeah, let me let me check on trying to meet up with you later today.
173 00:24:43.200 ⇒ 00:24:50.370 Uttam Kumaran: I don’t know how late you’re gonna be online right now. So let’s about 1050 am. My time. But I should have time.
174 00:24:50.600 ⇒ 00:24:58.920 Uttam Kumaran: maybe after 3 or 4 Pm. That may be too late for you, though. Let me know what what time is it where you live? It’s it’s almost 11 Am.
175 00:24:59.140 ⇒ 00:25:00.129 Uttam Kumaran: In the morning.
176 00:25:00.700 ⇒ 00:25:03.780 Agustin: 11. So it’s 3 h difference
177 00:25:04.130 ⇒ 00:25:11.409 Agustin: if you’re okay. Would it meet at 6 pm. Your time? If that’s okay. So that would be like.
178 00:25:12.010 ⇒ 00:25:15.019 Uttam Kumaran: I mean, I’m happy to do 6 pm, yeah, if that’s okay with you.
179 00:25:15.840 ⇒ 00:25:18.759 Yeah, it is. I’m free in the night. So yeah.
180 00:25:18.800 ⇒ 00:25:22.630 Uttam Kumaran: okay, cool. So let me just let me. I’m gonna put some time on right now.
181 00:25:23.170 ⇒ 00:25:30.160 Agustin: then, I will.
182 00:25:30.360 ⇒ 00:25:36.720 Agustin: So sorry. II just remember I had to do something at the time, and I
183 00:25:37.650 ⇒ 00:25:44.300 Uttam Kumaran: and 7, we have your time. Yeah. 7 pm. I could do 7 pm. Too.
184 00:25:44.480 ⇒ 00:25:49.499 Uttam Kumaran: what I’m gonna do is I’m gonna send it to you as soon as I get it done, and then we can even slack back and forth.
185 00:25:49.970 ⇒ 00:26:00.049 Uttam Kumaran: And then again I have. I may be free sometime around 4 or 5 Pm. My time and I could just let you know. But let me put something on for 7 pm.
186 00:26:00.190 ⇒ 00:26:03.329 Uttam Kumaran: And then if we can meet earlier, we can meet earlier.
187 00:26:03.950 ⇒ 00:26:05.859 Uttam Kumaran: Okay, that’s great.
188 00:26:06.160 ⇒ 00:26:06.910 Uttam Kumaran: Great.
189 00:26:09.720 ⇒ 00:26:17.300 Uttam Kumaran: Okay, cool. Again. Thank you so much for the time. I’m excited. and I’m always looking for like, really, really talented
190 00:26:17.560 ⇒ 00:26:25.410 Uttam Kumaran: engineers who want to work on some of these like more modern Etl work close. And I think we’re gonna try and build some really cool stuff. So
191 00:26:25.560 ⇒ 00:26:32.929 Uttam Kumaran: excited to kind of get started, and so I’ll send some stuff your way in the next you know, few hours or so, and then
192 00:26:33.230 ⇒ 00:26:35.690 Uttam Kumaran: yeah, I’m excited to kind of see out where this goes.
193 00:26:36.060 ⇒ 00:26:44.679 Agustin: Yeah, I’m really excited to thank you for your time with Tom, and well hope to see you later.