Meeting Title: Uttam_Awaish Date: 2025-01-27 Meeting participants: Awaish Kumar, Uttam Kumaran
WEBVTT
1 00:09:45.740 ⇒ 00:09:46.780 Uttam Kumaran: Hey!
2 00:09:51.620 ⇒ 00:09:52.370 Awaish Kumar: Hello!
3 00:09:52.580 ⇒ 00:09:53.830 Uttam Kumaran: Hi! How are you?
4 00:09:54.040 ⇒ 00:09:55.609 Awaish Kumar: I’m good. How about you?
5 00:09:55.610 ⇒ 00:10:02.859 Uttam Kumaran: Good. Nice to meet you. Thanks for taking the time. I know we’ve been trying to connect for a while now. So appreciate the time today.
6 00:10:06.472 ⇒ 00:10:07.290 Uttam Kumaran: How’s it going.
7 00:10:07.290 ⇒ 00:10:10.319 Awaish Kumar: Thank you so much also for clicking. Yeah, it is.
8 00:10:10.620 ⇒ 00:10:12.229 Awaish Kumar: It’s going good. How about you.
9 00:10:12.610 ⇒ 00:10:20.580 Uttam Kumaran: Yeah, it’s going. Well, yeah. And I’m I’m glad we have some more business come in this month. And so we’re just
10 00:10:20.880 ⇒ 00:10:25.409 Uttam Kumaran: it’s getting really busy, which is good. But it’s been very stressful. So
11 00:10:25.740 ⇒ 00:10:28.149 Uttam Kumaran: yeah, how about you? How’s the New Year? So far.
12 00:10:30.642 ⇒ 00:10:38.067 Awaish Kumar: Yeah, it’s my 1st winter in Canada. So it’s been a little different experience. But yeah, it’s good.
13 00:10:38.480 ⇒ 00:10:40.620 Uttam Kumaran: Where where were you before? Canada?
14 00:10:41.930 ⇒ 00:10:46.919 Awaish Kumar: Before Canada. I was in Copenhagen, Denmark.
15 00:10:48.190 ⇒ 00:10:51.969 Uttam Kumaran: It’s cold there, right, isn’t it? It’s still pretty cold there, right in Denmark.
16 00:10:52.660 ⇒ 00:10:57.710 Awaish Kumar: Yeah, like, Copenhagen is really really, like
17 00:10:57.990 ⇒ 00:11:04.716 Awaish Kumar: a very like amazing city, like also in terms of quality of life, and but also, like
18 00:11:06.750 ⇒ 00:11:09.060 Awaish Kumar: the city is vibrant.
19 00:11:09.060 ⇒ 00:11:09.750 Uttam Kumaran: Oh, really.
20 00:11:10.160 ⇒ 00:11:10.770 Awaish Kumar: Yeah.
21 00:11:10.770 ⇒ 00:11:17.550 Uttam Kumaran: Oh, okay, I’ve never been there, I heard. I mean, I know a lot of people students are there. There’s a lot of universities and things like that. So.
22 00:11:18.075 ⇒ 00:11:21.029 Uttam Kumaran: yeah, how did you? How did you end up there?
23 00:11:22.180 ⇒ 00:11:23.670 Awaish Kumar: In Copenhagen.
24 00:11:23.670 ⇒ 00:11:24.110 Uttam Kumaran: Yes.
25 00:11:24.110 ⇒ 00:11:40.810 Awaish Kumar: Actually, I am from Pakistan. And I started working with a startup it was a Danish startup and we were. I was their 1st data engineer building the data system for them. And then and we that startup was making an an analytical platform
26 00:11:41.456 ⇒ 00:11:44.089 Awaish Kumar: for the vacation rental industry.
27 00:11:44.310 ⇒ 00:11:44.680 Uttam Kumaran: Okay.
28 00:11:44.800 ⇒ 00:12:04.685 Awaish Kumar: And finally, after one year, working with them, it got acquired by a a company called Oyo Vacation Homes, and they they came into Europe acquired a lot of like vacation rental agencies. And then they also acquired our data science startup and then they yeah
29 00:12:05.370 ⇒ 00:12:08.110 Awaish Kumar: asked me to relocate to Copenhagen.
30 00:12:08.400 ⇒ 00:12:12.620 Uttam Kumaran: Oh, okay, okay. What was the stack there like, what did you end up implementing.
31 00:12:14.610 ⇒ 00:12:16.020 Awaish Kumar: You have a tool stack.
32 00:12:16.020 ⇒ 00:12:16.600 Uttam Kumaran: Yeah.
33 00:12:17.610 ⇒ 00:12:23.229 Awaish Kumar: It there like in the beginning, as a startup we were using like postgres as well
34 00:12:24.476 ⇒ 00:12:34.230 Awaish Kumar: big like. Item, mostly for transformations. SQL, and airflow for orchestration.
35 00:12:34.450 ⇒ 00:12:35.860 Awaish Kumar: And
36 00:12:36.829 ⇒ 00:12:44.609 Awaish Kumar: like, I’ll after that when it got acquired we had, like 400% surge in volume.
37 00:12:44.920 ⇒ 00:12:45.330 Awaish Kumar: You don’t.
38 00:12:46.065 ⇒ 00:12:52.389 Awaish Kumar: Because now we are in the company, and also that company is not a like
39 00:12:52.560 ⇒ 00:12:55.030 Awaish Kumar: a single brand. Right? They they acquired
40 00:12:55.270 ⇒ 00:13:18.750 Awaish Kumar: the major 3, 4 vacation rental brands called Belle Villa, then Center. So it gave us like, exposure to a lot of data and our data pipelines started to like exhaust with existing system. And then we had to move. So I was the lead developer to migrate all those pipelines. So then we started using.
41 00:13:19.324 ⇒ 00:13:41.160 Awaish Kumar: For example, you can say bigquery as our data warehouse. And we moved all of our data all all the data to the bigquery. But also we migrated our data pipelines there, because, there was like multiple solutions which came in mind. Number one was to like, add more
42 00:13:41.880 ⇒ 00:13:57.340 Awaish Kumar: databases, instances, or compute inside in the existing system to to process the data the the large data volume. Second option was to like use the aws with the redshift and awcmr
43 00:13:57.500 ⇒ 00:14:06.169 Awaish Kumar: for the distributed processing. And the 3rd option was to switch from Etl to Elt, basically
44 00:14:06.330 ⇒ 00:14:21.946 Awaish Kumar: and load to the big like the data warehouse first, st and and use its capabilities. And at that moment, bigquery was very mature platform, serverless platform. Then redshift, and it provided a lot of
45 00:14:22.827 ⇒ 00:14:37.580 Awaish Kumar: like SQL. There, like the transformative abilities like the, there were a lot of analytical functions, and we could perform all our transformations which we could. We were doing in python before
46 00:14:38.008 ⇒ 00:14:43.539 Awaish Kumar: then. So we started migrating all of our transformations on top of the big carry.
47 00:14:43.830 ⇒ 00:14:44.600 Awaish Kumar: Great. Okay?
48 00:14:44.600 ⇒ 00:15:13.980 Awaish Kumar: So I’ve been using. You can say, Google bigquery and obviously we had postgres. We had a web scrapping project as well, which I was leading. So we have been using postgres for that to store over all the other scrapping data there and then it got every day it gets migrated to the bigquery. So we were using postgres for that. We have still had the airflow. We were using the Google Bigquery as our data warehouse.
49 00:15:14.300 ⇒ 00:15:22.350 Awaish Kumar: And we are using Apache superset as our for the dashboard.
50 00:15:22.750 ⇒ 00:15:28.445 Awaish Kumar: And yeah, that was the main part of it. And
51 00:15:29.410 ⇒ 00:15:37.469 Awaish Kumar: we had different systems like for the source systems, you can tell like, so the the data which was coming from was a lot of different
52 00:15:38.041 ⇒ 00:15:49.100 Awaish Kumar: systems. Now, because, as I mentioned, we had a lot of brands. So we are. So some brands are still using S. 400 to handle their bookings
53 00:15:49.538 ⇒ 00:15:59.009 Awaish Kumar: and one of the brand which was using a different platform but the only way to get data from them was to call the Apis.
54 00:15:59.020 ⇒ 00:16:22.539 Awaish Kumar: and the 3rd one was to get. We were getting some files from a 3rd system. So it was a mix of that. So we have been using airflow operators mostly the tools like air by the 5 trend, and the Dbt became like they came in the hype in the later, in the
55 00:16:23.120 ⇒ 00:16:36.640 Awaish Kumar: changes. But at that moment. We were mostly using airflow operators to get those data from there and writing python scripts to call the, for example, Apis and get the data from there and and store it.
56 00:16:37.930 ⇒ 00:16:41.880 Uttam Kumaran: So you did a lot of data. And your, so then, how did you get it to Dbt stuff.
57 00:16:43.064 ⇒ 00:17:10.029 Awaish Kumar: So I have been like. As I mentioned, it wasn’t a startup. So I have been doing a lot of different. So I this like I was involved in setting up the airflow and the Apache superset, because we as a startup, we wanted to reduce our cost, and that’s why, we were using open source tools a lot trying to host it ourselves. So there was a lot of.
58 00:17:10.030 ⇒ 00:17:12.080 Uttam Kumaran: Like a lot of devops. Yeah.
59 00:17:12.089 ⇒ 00:17:34.129 Awaish Kumar: Yeah. And then you can say, I have been part of the data, building the data pipelines and data warehouse. But then also, I have been involved in doing the analytical work because I was really into the business. Like the dynamics like, what what’s the effect of
60 00:17:34.259 ⇒ 00:17:38.299 Awaish Kumar: like, if we are doing in the back end something, how it is going to affect the.
61 00:17:38.300 ⇒ 00:17:38.800 Uttam Kumaran: Yeah.
62 00:17:39.201 ⇒ 00:18:01.700 Awaish Kumar: Business. So there were a lot of reports like I did. One of the the analytical analysis which I did was to we were wanted to acquire one more company it had, like 40 40 to 50 homes in their portfolio. And now the company wants to acquire those homes. Right. So so then, like.
63 00:18:02.010 ⇒ 00:18:15.923 Awaish Kumar: how can I support that decision making like this? CEO want to make a decision? And Cfo wants to have a report from us. So I’m making a report based on the data we have, we have the internal data. We have the
64 00:18:16.592 ⇒ 00:18:32.797 Awaish Kumar: competitors data which is coming from our scrappers. And now, based on that, I want to find the all for the individual home, I find. Want to find some homes which are nearby. What is their occupancy? How do they look like in terms of
65 00:18:33.350 ⇒ 00:18:42.589 Awaish Kumar: like? How good they are also the the area, or if it is a villa, or what whatever it is these kind of things, and how the pri pricing
66 00:18:43.137 ⇒ 00:19:12.439 Awaish Kumar: is, is there? If we have internal homes there, how? What is the revenue being generated from them all these kind of things to put forward to to help them make informed decision. Yeah. So these are the kind of thing which I’ve been doing. I’ve been involved in machine learning as well billing model through like forecast, the occupancy for our homes in, for example, Denmark, or for our homes in Switzerland, and these kind of things.
67 00:19:13.620 ⇒ 00:19:43.319 Awaish Kumar: So after that, when I switched the jobs, and then there was a lot of I went to a logistics company. There was also they were using, utilizing a very old systems like Ssis is still for Atl and and they have been using like the still the Ss Scheduler, all the Microsoft SQL. Server as their data browse. So all the that technology. And then I was there to to migrate that
68 00:19:43.570 ⇒ 00:20:01.449 Awaish Kumar: to to the, to the new stack which is coming in like using python SQL. And there. I like, when I was exploring different tools and technologies we could use. We, I started using, for example, Dbt, to to standardize the the SQL and the transformations.
69 00:20:01.450 ⇒ 00:20:03.930 Uttam Kumaran: How long ago was that when you start.
70 00:20:03.930 ⇒ 00:20:06.790 Awaish Kumar: That was in 2022.
71 00:20:07.220 ⇒ 00:20:08.190 Uttam Kumaran: Okay. Okay.
72 00:20:08.500 ⇒ 00:20:09.429 Awaish Kumar: It’s good. Yeah.
73 00:20:09.960 ⇒ 00:20:15.309 Awaish Kumar: So after that, in my jobs in Canada, I’ve also been using Dvt bigquery.
74 00:20:15.987 ⇒ 00:20:18.660 Awaish Kumar: I have used a lot of different tools, like
75 00:20:19.711 ⇒ 00:20:25.769 Awaish Kumar: redshift like Athena Snowflake. So all the different tools and tools.
76 00:20:25.770 ⇒ 00:20:29.030 Uttam Kumaran: But you’ve also done work on like Cicd, and like sort of.
77 00:20:29.723 ⇒ 00:20:30.330 Awaish Kumar: Yes! Yes!
78 00:20:30.330 ⇒ 00:20:31.080 Uttam Kumaran: Okay, okay.
79 00:20:31.180 ⇒ 00:20:49.730 Uttam Kumaran: out of like, I mean, it’s very similar to my background. And I know, I guess I could tell you a little bit about brainforce. So we started the company. I start. So my background is in data engineering. I was a data engineer and a bi engineer for a few years in New York I worked at. We work. So a lot of similar like real estate related data. I can relate occupancy, things like that.
80 00:20:50.277 ⇒ 00:20:55.900 Uttam Kumaran: And then I went to another company, was the 1st data engineer, and then same thing built the entire data stack
81 00:20:56.020 ⇒ 00:21:03.810 Uttam Kumaran: we were using airflow. Then we were using like Databrick spark jobs. And we were. I brought on Snowflake 5 tran looker
82 00:21:04.010 ⇒ 00:21:05.880 Uttam Kumaran: and brought on Dbt, and then
83 00:21:06.761 ⇒ 00:21:24.790 Uttam Kumaran: I I worked one more job after that and decided to start Brainforge. And really, we, we help companies stand up data infrastructure and and basically help them report on their business. So kind of like full stack on the on the analytics side. So we do a lot of data warehouse work primarily on Snowflake. But we have some bigquery clients as well.
84 00:21:24.910 ⇒ 00:21:27.399 Uttam Kumaran: very similar. So you won’t have a challenge. There
85 00:21:27.610 ⇒ 00:21:29.539 Uttam Kumaran: we do a lot of Dvt work.
86 00:21:30.001 ⇒ 00:21:35.549 Uttam Kumaran: And so that’s where really, I think short term. We need a lot of help on like the data inside and the Dbt side.
87 00:21:36.750 ⇒ 00:22:03.300 Uttam Kumaran: it’s just creating models, right? Creating new models, optimizing existing models, migrating like scheduled queries into bigquery. Things like that. And then we also help companies do analysis. So we stand up business intelligence. So we help them with analysis. And then we do some broader, like, almost like head of data sort of strategy work for clients. So we’ve been in business since 2023, but probably like, had it been growing more steadily over the last, like 6 months.
88 00:22:03.853 ⇒ 00:22:07.929 Uttam Kumaran: Their team is right now, maybe 11 people kind of like all across
89 00:22:08.090 ⇒ 00:22:18.529 Uttam Kumaran: the Us and around the world. So mix of data people, and then also we also sell AI services. So we use a lot of internal AI agents for work.
90 00:22:18.700 ⇒ 00:22:22.920 Uttam Kumaran: We everybody on the data team is using cursor and everything to kind of speed up work.
91 00:22:23.320 ⇒ 00:22:30.279 Uttam Kumaran: And then we’re also have some clients on the AI side. And kind of finding ways to bring those 2 businesses closer together.
92 00:22:31.020 ⇒ 00:22:39.970 Uttam Kumaran: So yeah, really, like short term, you know, a lot of our need is around personally like I. So my, I’ve done a lot of I’ve been working with Dbt since like 2,018
93 00:22:40.370 ⇒ 00:22:42.020 Uttam Kumaran: done a lot of Dvt work.
94 00:22:42.550 ⇒ 00:23:02.750 Uttam Kumaran: Primarily, it’s just writing models and bringing in new sources and modeling whether it’s financial data, customer data product data. I’m sure probably this stuff you’re familiar with. Problem with consulting is a lot of communication. So it’s different than working internal community internal in a company. It takes a lot of communication with clients working on requirements, things like that. So.
95 00:23:02.750 ⇒ 00:23:07.370 Awaish Kumar: I think, when talking with Nicholas, I think cool.
96 00:23:08.004 ⇒ 00:23:30.640 Awaish Kumar: Yeah, we. I mentioned the my experience at data Gpt, where it’s kind of similar, but in little bit different, like in the brand forge, we are providing the customized analytical, the solutions. But in the brand the data Gpt, we had the platform the dashboard is a standardized way of showing it.
97 00:23:31.090 ⇒ 00:23:35.509 Awaish Kumar: Yeah, obviously, it is for different customers. But it’s in a standardized format.
98 00:23:36.340 ⇒ 00:23:53.710 Awaish Kumar: AI powered. It is using chat Gpt in the in the back end. So so at that. There I was. The data engineer and I have been have experience of talking to different clients. So we are like, I am the the client facing engineer.
99 00:23:54.040 ⇒ 00:23:55.029 Uttam Kumaran: Really. Okay. Great.
100 00:23:55.030 ⇒ 00:23:56.730 Awaish Kumar: So I’m talking to the client. So.
101 00:23:56.730 ⇒ 00:23:58.699 Uttam Kumaran: You know exactly what I’m talking about. Yeah, yeah.
102 00:23:58.700 ⇒ 00:24:01.519 Awaish Kumar: Yeah, whenever we want to onboard a new client.
103 00:24:01.590 ⇒ 00:24:09.980 Awaish Kumar: So like the pressure is on me to like. 1st of all, I have to understand the customers data, and then
104 00:24:10.440 ⇒ 00:24:32.469 Awaish Kumar: based on that and and understand their requirements. What do they need? And based on that create some transformations in Dbt, so data is in the right format, and I have to write a configuration for our model as well. And in the back end that model runs, and it generates the the required segments or matrix, or whatever.
105 00:24:32.780 ⇒ 00:24:37.139 Awaish Kumar: So basically. And I have to undo it, do they do it in in like 24 h
106 00:24:37.890 ⇒ 00:25:00.680 Awaish Kumar: to like to on fully on board the client, at least on the like. We said, we define the minimal expectation. Like, we okay, we are going to look for one or 2 metrics with different segments. So it’s like doable in 24 h, then showcase them so that they are confident in our product. And then I can like keep incrementally increasing the
107 00:25:01.260 ⇒ 00:25:05.260 Awaish Kumar: keep increasing the metrics. And if I want to, we would things we want to analyze.
108 00:25:05.510 ⇒ 00:25:05.990 Uttam Kumaran: Yeah, so.
109 00:25:05.990 ⇒ 00:25:19.929 Awaish Kumar: So yeah, I have experience working with different clients. Different tool estate, like one is using Athena as their data warehouse. Someone is using redshift. Someone is using victory. Someone is using snowflake. So and then how to get data from there
110 00:25:20.190 ⇒ 00:25:22.719 Awaish Kumar: and maybe use airbite or something.
111 00:25:22.720 ⇒ 00:25:23.330 Uttam Kumaran: Exactly.
112 00:25:23.330 ⇒ 00:25:27.420 Awaish Kumar: So, and then run DVD. On top of it and find it.
113 00:25:27.420 ⇒ 00:25:27.870 Uttam Kumaran: Variance.
114 00:25:27.870 ⇒ 00:25:28.859 Awaish Kumar: Some dashboards.
115 00:25:28.860 ⇒ 00:25:56.880 Uttam Kumaran: Yeah, very similar. And at the moment, you know, it’s we have a, we have 2 analysts. And then we have me. I’m the only data engineer. And then we have one other person who’s on the analytics engineering side. So we’re looking to try to bring someone on the team. That kind of can not only write models, but also help to review Prs like we have a Pr process to review Dbt. And then also help me a little bit on the data engineering side. So spinning up new warehouses, helping me with some Cicd process.
116 00:25:56.890 ⇒ 00:26:05.799 Uttam Kumaran: we want to create staging developer environments stuff for quality of life for our engineers. So definitely was looking kind of looking for somebody who’s like
117 00:26:06.300 ⇒ 00:26:16.090 Uttam Kumaran: seen that portion, but also knows that like sometimes it’s just like whatever the client needs, we need to get done. But also, like we’re trying to build, you know, an actual engineering organization.
118 00:26:16.090 ⇒ 00:26:16.770 Awaish Kumar: Oh, yeah.
119 00:26:16.830 ⇒ 00:26:32.122 Awaish Kumar: I truly understand. And I I can share with one more example. If you want. So, like, as you mentioned, we have to satisfy the clients as well. Like one of the experience I can share, share that we have been using. The
120 00:26:32.470 ⇒ 00:26:48.160 Awaish Kumar: One of our client was using the Athena as their data warehouse, and that our current existing system, we were using Dbt. To run our transformations, and at that point in time the Dbt. Connector, for Athena was not like there.
121 00:26:48.599 ⇒ 00:26:53.400 Awaish Kumar: And it was a challenge, because we have also the very tight deadline.
122 00:26:53.670 ⇒ 00:27:04.559 Awaish Kumar: So even if I want to build a new connector. I I need some time to implement that. And I I look for look for that solutions to implement it. But I told them.
123 00:27:04.740 ⇒ 00:27:26.059 Awaish Kumar: If I using, if I use that solution, we are not going to deliver the solution 24 h. So what could be the alternative? So maybe you’ll use air by to move the data required from Athena to our internal bigquery. Run the Dbt transformation, build the dashboard like at the 1st instance, like show something to to client
124 00:27:26.190 ⇒ 00:27:42.829 Awaish Kumar: have their confidence in our product, and then we can discuss with them like, if we, if we want to build a Tina Connector, or we want to migrate data, internal sources, or like, what could be the best situation solution after that. So I’ve been in those scenarios where we want.
125 00:27:42.830 ⇒ 00:27:43.670 Uttam Kumaran: You have to make a trade off.
126 00:27:43.670 ⇒ 00:27:48.020 Awaish Kumar: Want to satisfy the clients, and we also want to improve our internal systems.
127 00:27:48.020 ⇒ 00:27:56.689 Uttam Kumaran: Yeah, makes a lot of sense. So tell me about like, where you are right now in your in your you know job search. And then your availability. Like, yeah, curious.
128 00:27:56.690 ⇒ 00:28:00.569 Awaish Kumar: So basically, I am right. Now I’m in Canada.
129 00:28:00.670 ⇒ 00:28:26.110 Awaish Kumar: I’ve been looking for Job, as you know, for some time, and I do got some lot of job offers. Only the problem was that I am on a closed work permit and in Canada. So the companies had to sponsor me the visa if they wanted me to work from Canada, so I’m flexible to like if they I have a friend’s company in
130 00:28:26.570 ⇒ 00:28:50.919 Awaish Kumar: Toronto. I I put forward some suggestions like, Okay, if you want to have some b 2 b contract with him. So I don’t have to then need a visa sponsorship and even if if you don’t want to do it, I’m I can move outside of Canada to work like, because like a lot of countries now offer, like digital nomad visas and.
131 00:28:50.920 ⇒ 00:28:51.750 Uttam Kumaran: Yeah, yeah.
132 00:28:51.750 ⇒ 00:28:58.329 Awaish Kumar: But Canada doesn’t offer that right now, so I can work from, for example, Mexico. But I cannot work from Canada.
133 00:28:58.330 ⇒ 00:28:59.110 Uttam Kumaran: Yeah, that’s true.
134 00:28:59.110 ⇒ 00:29:06.341 Awaish Kumar: Oh, I I did suggest some solutions, but the I I think, because of different, like the election time and different
135 00:29:07.375 ⇒ 00:29:13.219 Awaish Kumar: situations right now with immigration companies didn’t wanted to sponsor the visa at that moment.
136 00:29:13.430 ⇒ 00:29:17.389 Awaish Kumar: and because of that, I think. And they also like
137 00:29:17.540 ⇒ 00:29:25.690 Awaish Kumar: and didn’t wanted someone on a contract, right with a company or something. They wanted someone as a full time employee.
138 00:29:26.080 ⇒ 00:29:27.330 Uttam Kumaran: Yeah for me, like.
139 00:29:27.940 ⇒ 00:29:37.283 Uttam Kumaran: yeah. So we don’t. So we’re we’re. I’m based here in the in the Us. But where we don’t, everybody sort of is on contract right now.
140 00:29:37.880 ⇒ 00:29:45.580 Uttam Kumaran: and so if like, that’s something that I would definitely be more open to, we haven’t. We have a couple of people that are full time. But we’re sort of growing.
141 00:29:45.580 ⇒ 00:29:59.019 Awaish Kumar: So like I. I have 2 things like I have a company. If you want me to stay in Canada like work from Canada, I have a friend’s company there, and we can have a contact with them, and then they can.
142 00:29:59.500 ⇒ 00:30:13.040 Awaish Kumar: They will run my payroll like you don’t have to deal with that. And the second thing is, I can move out from Canada, where I have the working rights, or maybe get digital moment visa somewhere and then work work from there.
143 00:30:13.260 ⇒ 00:30:17.570 Uttam Kumaran: Yeah, I mean, I don’t wanna get you. I don’t want to force you to move anywhere. Also, like I wanna.
144 00:30:17.570 ⇒ 00:30:21.660 Awaish Kumar: Because I am moving out because of the
145 00:30:22.312 ⇒ 00:30:28.099 Awaish Kumar: here companies are not not sponsoring visa. So anyhow, I’m moving out.
146 00:30:28.100 ⇒ 00:30:30.970 Uttam Kumaran: Okay, okay, when what time? What’s the timeline for that?
147 00:30:32.240 ⇒ 00:30:32.880 Awaish Kumar: Rough
148 00:30:34.520 ⇒ 00:30:53.459 Awaish Kumar: like in February. I will be moving out if I don’t have have a like with a with a contract, or with any job with something like that. But I’m willing to work from outside. because I can work in the Eastern time zone, or whatever like it is.
149 00:30:53.460 ⇒ 00:30:57.359 Uttam Kumaran: Yeah, for so for for us like to be really frank does not
150 00:30:57.520 ⇒ 00:31:01.869 Uttam Kumaran: matter to me where you are, I think for us. The number. One thing is
151 00:31:02.040 ⇒ 00:31:04.700 Uttam Kumaran: that clients are happy, and that we build like a really.
152 00:31:04.700 ⇒ 00:31:16.029 Awaish Kumar: I have worked with. For example, companies like the both companies. I’ve worked with the data Gpt and Maino games. They were open to me like you. I can choose my location and work from there.
153 00:31:16.496 ⇒ 00:31:37.749 Awaish Kumar: The the only requirement was that I will be there in the meetings, or if in the client meetings, or if it is something so, only the the required things are there like we satisfy the client, and we are at the meetings required, and the stand ups, and all these things and like. Then I can work like anywhere, anytime, like after that.
154 00:31:38.160 ⇒ 00:31:41.059 Uttam Kumaran: So then I mean, kind of our our typical process.
155 00:31:41.435 ⇒ 00:31:43.189 Uttam Kumaran: You know the way we do. It is a
156 00:31:43.400 ⇒ 00:31:53.010 Uttam Kumaran: for me like I again, like, I’m building our whole engineering team from scratch, like, typically, the way I like to work is like you. Come on, you take a couple of tickets and sort of see?
157 00:31:53.030 ⇒ 00:32:13.520 Uttam Kumaran: Get a sense of like what it’s like to work with our team. We get a sense of like what it’s like to work with you. I think what would be helpful is maybe to bring you on to just one of our clients, where maybe we have some tickets that need to be reviewed, some Dbtprs that need to be made, and then we just go from there. You know, we I mean, I’m happy to do like
158 00:32:13.640 ⇒ 00:32:22.849 Uttam Kumaran: got contract, either through. You know your friend’s company, or whatever like typically everybody here, we just pay through gusto on like a contract, hourly basis.
159 00:32:22.850 ⇒ 00:32:23.370 Awaish Kumar: Yeah.
160 00:32:23.556 ⇒ 00:32:23.910 Uttam Kumaran: If that’s.
161 00:32:25.190 ⇒ 00:32:32.931 Awaish Kumar: So yeah, that’s what my thing, I it depends on the timing as well, because I’m I’m short on time
162 00:32:33.300 ⇒ 00:32:39.260 Uttam Kumaran: Yeah, this would be like, as soon as as I got. This would be as soon as possible like this we could get. We could start this week if
163 00:32:39.400 ⇒ 00:32:40.700 Uttam Kumaran: if you wanted to.
164 00:32:41.710 ⇒ 00:32:47.020 Awaish Kumar: Yeah. But as you mentioned there, that like, we will go from step to step
165 00:32:47.443 ⇒ 00:32:52.270 Awaish Kumar: then it it won’t be a it. Will it be a full time contract, or what? What?
166 00:32:52.270 ⇒ 00:32:55.269 Uttam Kumaran: Yeah, at the moment we would start with like half time.
167 00:32:55.680 ⇒ 00:33:04.613 Uttam Kumaran: And we’re basically we’re freeing. We have, like, probably like 20 h of work easily, and then that’ll sort of scale up.
168 00:33:05.310 ⇒ 00:33:05.710 Awaish Kumar: Yeah.
169 00:33:05.710 ⇒ 00:33:06.359 Uttam Kumaran: And so.
170 00:33:06.360 ⇒ 00:33:16.640 Awaish Kumar: Then I think the the direct contract will work, not the with the company, because, based on part time contract, they cannot sponsor any rep right?
171 00:33:17.055 ⇒ 00:33:21.189 Awaish Kumar: We need to have a full time job to get a sponsor in the Canada.
172 00:33:21.190 ⇒ 00:33:21.880 Uttam Kumaran: Yeah, so.
173 00:33:21.920 ⇒ 00:33:22.790 Awaish Kumar: My turn.
174 00:33:23.340 ⇒ 00:33:27.380 Uttam Kumaran: So would you be open to doing like part time to start with, and then.
175 00:33:27.750 ⇒ 00:33:35.090 Awaish Kumar: Yeah, I’m open. I’m open to it by a like a direct contract as an individual, right?
176 00:33:35.330 ⇒ 00:33:36.449 Uttam Kumaran: Yeah, exactly.
177 00:33:36.450 ⇒ 00:33:37.140 Awaish Kumar: Yeah.
178 00:33:37.550 ⇒ 00:33:39.370 Uttam Kumaran: Yeah. And then that way, you know,
179 00:33:39.820 ⇒ 00:33:46.659 Uttam Kumaran: again for us, it’s like important just to see that we work together. And then again, we’re growing. We’re gonna have another big client starting next month.
180 00:33:47.281 ⇒ 00:33:53.919 Uttam Kumaran: and then probably a few more. So I’m I will. I won’t be surprised if if you get closer to 40 h
181 00:33:54.050 ⇒ 00:33:57.379 Uttam Kumaran: pretty soon. But the biggest thing for me is like
182 00:33:57.520 ⇒ 00:34:06.449 Uttam Kumaran: like, we’re not trying to hire people that are gonna quickly like, come here, work and then jump like, because that’s what I know happens in engineering like we’re trying to really build
183 00:34:06.610 ⇒ 00:34:13.899 Uttam Kumaran: a great team of like client facing, you know, data engineers and analytics engineers. So that’s for me the biggest thing you know.
184 00:34:13.900 ⇒ 00:34:34.560 Awaish Kumar: I can say that I have been with the companies for for like for a long time. Like, if you even see my recent jobs like I have not switched minor games, and a Gbt. Are the same company. Sister companies they both are with the same CEO. So they brought me to other company, because.
185 00:34:34.909 ⇒ 00:34:35.549 Uttam Kumaran: You didn’t do that.
186 00:34:35.550 ⇒ 00:34:47.830 Awaish Kumar: There, so I have not jumped from one company to other. I’ve been with them for 2 years, and also recently there was like because of the shift in their
187 00:34:47.940 ⇒ 00:34:51.580 Awaish Kumar: business, right? Because of the they had to do a layoff.
188 00:34:51.929 ⇒ 00:34:54.369 Awaish Kumar: They went from a very
189 00:34:54.580 ⇒ 00:35:06.990 Awaish Kumar: we had a very web. 3 based video game. Minor games had that. And after some new investment the the business shifted from
190 00:35:07.170 ⇒ 00:35:20.149 Awaish Kumar: creating a sophisticated like a very complex game. They went to a very simplistic game and try to get more into the Asian markets like China, Japan.
191 00:35:20.450 ⇒ 00:35:28.329 Awaish Kumar: And they invested a lot there. And that’s why the game was very simple to do any analytics on on it. So so they had to go there.
192 00:35:29.410 ⇒ 00:35:31.107 Uttam Kumaran: Makes sense. Well, how about like
193 00:35:31.961 ⇒ 00:35:36.990 Uttam Kumaran: Maybe I’ll send you an email after this. I know we’re running low on time with just some like logistics
194 00:35:37.310 ⇒ 00:35:43.230 Uttam Kumaran: for me. It would be great if you’re like, Hey, I’m I’m down to just start. And maybe we could take some stuff on this week.
195 00:35:45.030 ⇒ 00:35:45.420 Uttam Kumaran: Okay.
196 00:35:45.420 ⇒ 00:35:47.730 Awaish Kumar: So right now. So if we’re
197 00:35:47.880 ⇒ 00:35:50.560 Awaish Kumar: I can start like from tomorrow, right.
198 00:35:50.560 ⇒ 00:35:58.290 Uttam Kumaran: Okay, cool. So then maybe I’ll send you an email with some details. Yeah, tomorrow would be great. I need. I’ll need some time for like paperwork and stuff.
199 00:35:58.440 ⇒ 00:36:02.679 Uttam Kumaran: and then, like, what do you like? Do you have a sense of like the rate that you’d be looking for.
200 00:36:03.660 ⇒ 00:36:07.520 Awaish Kumar: I’m open to it. If if you have any budget you can share.
201 00:36:07.520 ⇒ 00:36:15.070 Uttam Kumaran: Okay, yeah, I mean, we, we have some leveling. I think for me, it’s important to know, like again, for for us.
202 00:36:15.190 ⇒ 00:36:28.959 Uttam Kumaran: we basically have, like we have some people that are more junior side with some people that are more senior. So for me, I’m like, I I think, after probably a week or 2 I would be able to kind of give you a more sense of like where you would land in terms of our like level.
203 00:36:28.960 ⇒ 00:36:29.980 Awaish Kumar: I think initially, there.
204 00:36:29.980 ⇒ 00:36:34.579 Uttam Kumaran: It seems more. You seem more like on the senior side. But yeah, I don’t know.
205 00:36:34.580 ⇒ 00:36:38.709 Awaish Kumar: So initially, I can, we can start with maybe 35.
206 00:36:38.710 ⇒ 00:36:39.270 Uttam Kumaran: Okay.
207 00:36:39.800 ⇒ 00:36:41.300 Awaish Kumar: Per hour.
208 00:36:41.300 ⇒ 00:36:41.700 Uttam Kumaran: Okay.
209 00:36:42.299 ⇒ 00:36:48.909 Awaish Kumar: And then the we can discuss like, if, like, everything goes well, we can then further discuss.
210 00:36:48.910 ⇒ 00:36:51.437 Uttam Kumaran: Okay, yeah. And and again, for me, it’s like, I
211 00:36:51.870 ⇒ 00:37:02.569 Uttam Kumaran: I don’t know. I’ve worked at a lot of engineering companies. Salary is important. And I know it’s like, I want to pay people the best for me. Also, it’s like, I want to make investments on people that are gonna be with us
212 00:37:02.640 ⇒ 00:37:22.450 Uttam Kumaran: long term, and also people that are going to be part of our our team. You know, we’re not just a consultancy where everybody like doesn’t talk. It’s like we’re actually have a core engineering team. And so definitely, I think that’s the price range we can work with. And then maybe, as time goes on. If if we have success, I think we can talk a bit about like sort of what long term is
213 00:37:22.936 ⇒ 00:37:24.220 Uttam Kumaran: and that would work great.
214 00:37:24.860 ⇒ 00:37:26.010 Awaish Kumar: Okay. Sure.
215 00:37:26.220 ⇒ 00:37:43.239 Uttam Kumaran: Okay, cool. So let me send you an email with some details, and I’ll I’ll move as fast as I can. I have like I have like 4 or 5 h of Dbt. Work to do right now. So after that I’ll send you some details but we do everything on slack. And so you’ll get an email, a brain forge email, and
216 00:37:43.320 ⇒ 00:38:02.179 Uttam Kumaran: you’ll be part of slack and then we’ll think about a couple of things to maybe hand off to you. Of course I know it’s like coming in a new clients and stuff. But hearing your background, this stuff is, gonna be pretty easy. So I’ll let you know, maybe as as soon as I can, you know what that’s gonna look like, and then maybe we can hopefully start to kick some stuff off tomorrow.
217 00:38:03.290 ⇒ 00:38:06.040 Awaish Kumar: Okay. And also about what about the
218 00:38:06.280 ⇒ 00:38:09.289 Awaish Kumar: A contract, or that you also send an email or.
219 00:38:09.290 ⇒ 00:38:16.090 Uttam Kumaran: Yeah, I’ll send it all an email for you to review. And then we do payroll through gusto. So you’ll get an email to hook up your bank account.
220 00:38:16.090 ⇒ 00:38:18.209 Awaish Kumar: I’ve been using deal.
221 00:38:18.210 ⇒ 00:38:19.889 Uttam Kumaran: Deal. Yeah, it’s similar to deal.
222 00:38:20.040 ⇒ 00:38:20.830 Awaish Kumar: Okay.
223 00:38:21.100 ⇒ 00:38:27.049 Uttam Kumaran: Yeah, it’s very similar. And then, yeah, contract will do. I’ll also send for signing basic. I’ll just confirm some details.
224 00:38:29.090 ⇒ 00:38:36.320 Uttam Kumaran: And yeah, I’ll aim for tomorrow. Let’s see, I have a lot of work to do today. So maybe it may be later tonight that I can get you an email over. So.
225 00:38:37.284 ⇒ 00:38:39.889 Awaish Kumar: Yeah. Sure, no worries, thank you.
226 00:38:39.890 ⇒ 00:38:44.760 Uttam Kumaran: Okay, cool. Yeah. And again, I really appreciate it. And I’m I’m really glad. Finally, I know I
227 00:38:45.020 ⇒ 00:38:53.410 Uttam Kumaran: we had some business, and it fell through. And then I’ve been so busy, so really happy. We got to connect, and looking forward to the, you know, chance to work together. So.
228 00:38:53.920 ⇒ 00:38:58.079 Awaish Kumar: Yeah, it was very nice talking to you as well. Thank you so much.
229 00:38:58.390 ⇒ 00:39:00.510 Uttam Kumaran: Thank you. Talk soon, Patrick.