Meeting Title: Roadmap for Interns Date: 2025-06-19 Meeting participants: Awaish Kumar, Uttam Kumaran
WEBVTT
1 00:01:04.760 ⇒ 00:01:05.990 Uttam Kumaran: Hey! I wish.
2 00:01:07.120 ⇒ 00:01:07.810 Awaish Kumar: Hello!
3 00:01:15.190 ⇒ 00:01:19.019 Uttam Kumaran: I think it’s maybe gonna be just, I think it’s maybe just gonna be us today. Cause.
4 00:01:19.020 ⇒ 00:01:19.450 Awaish Kumar: Yes.
5 00:01:19.450 ⇒ 00:01:21.490 Uttam Kumaran: It’s a holiday today. Yeah.
6 00:01:23.260 ⇒ 00:01:27.910 Awaish Kumar: Yeah, like it. It is between us 3. And amber is out of office.
7 00:01:29.300 ⇒ 00:01:33.260 Uttam Kumaran: Yeah, I think me and you can talk. It’s fine, and she can probably watch the recording.
8 00:01:36.670 ⇒ 00:01:43.589 Awaish Kumar: Yeah, it’s for the interns. So like.
9 00:01:43.590 ⇒ 00:01:44.260 Uttam Kumaran: Perfect.
10 00:01:44.970 ⇒ 00:01:48.330 Awaish Kumar: For for 8 weeks, like, I would like
11 00:01:48.650 ⇒ 00:01:51.119 Awaish Kumar: that on a high level issue
12 00:01:52.050 ⇒ 00:01:55.920 Awaish Kumar: if we can like. Come up with some projects which can.
13 00:01:58.080 ⇒ 00:02:01.689 Awaish Kumar: which can fill up like 8 weeks for for
14 00:02:01.920 ⇒ 00:02:04.169 Awaish Kumar: use, too. And I don’t know like
15 00:02:04.300 ⇒ 00:02:10.180 Awaish Kumar: yesterday I you like, even you didn’t have the Johnson.
16 00:02:10.180 ⇒ 00:02:14.940 Uttam Kumaran: Yeah, I try. I tried to join, but then the meeting link wasn’t working.
17 00:02:15.320 ⇒ 00:02:22.650 Uttam Kumaran: and then I just sort of I had, like somebody had a couple other things I had to go do so if you give me even his like
18 00:02:23.100 ⇒ 00:02:24.760 Uttam Kumaran: Whatsapp, I’m happy to call it.
19 00:02:26.130 ⇒ 00:02:26.659 Uttam Kumaran: That’s a.
20 00:02:28.010 ⇒ 00:02:29.770 Awaish Kumar: Now you have a vision.
21 00:02:30.310 ⇒ 00:02:39.299 Uttam Kumaran: Yeah, yeah, yeah, I saw his resume and everything I can email him and set it back up. I just, I was in the meeting, and then I was doing other stuff, and then it never really loaded. So
22 00:02:43.430 ⇒ 00:02:51.550 Uttam Kumaran: okay, but like, maybe walk me through your document and tell me what you’re thinking and kind of your confidence level. And then, yeah, happy to walk through projects.
23 00:02:57.410 ⇒ 00:03:00.979 Awaish Kumar: in the document is just so like
24 00:03:02.077 ⇒ 00:03:06.550 Awaish Kumar: formal announcement when to start, and how we are.
25 00:03:07.200 ⇒ 00:03:10.930 Awaish Kumar: how you are going to start with the with the team.
26 00:03:11.643 ⇒ 00:03:16.120 Awaish Kumar: It’s mostly like in the initial phases, like, 1st week is just
27 00:03:16.280 ⇒ 00:03:27.099 Awaish Kumar: can be introduction, or it’s just working with some exploratory task like the the way we did with the metamor, like they can just
28 00:03:27.320 ⇒ 00:03:34.120 Awaish Kumar: open up a notebook and do some exploration like, maybe I can give them a snowflakes
29 00:03:34.730 ⇒ 00:03:39.289 Awaish Kumar: data like the Linkedin data and snowflake which you were talking about.
30 00:03:39.870 ⇒ 00:03:47.590 Awaish Kumar: Yeah, like, if if the initially like, we can design some tasks wrong
31 00:03:48.620 ⇒ 00:03:53.519 Awaish Kumar: so they can work on that. Or or the second one was the clockify thing?
32 00:03:54.531 ⇒ 00:03:59.899 Awaish Kumar: Maybe they can. They build like in the initial phases, just build a dashboard for that.
33 00:04:00.380 ⇒ 00:04:07.780 Awaish Kumar: But yeah, but on a like that that would like 1st week giving some credentials.
34 00:04:08.550 ⇒ 00:04:13.310 Awaish Kumar: asking them to explore some data and meeting with the team
35 00:04:13.430 ⇒ 00:04:15.900 Awaish Kumar: like that. That’s like how it’s been.
36 00:04:17.209 ⇒ 00:04:23.109 Uttam Kumaran: So so do you. So for it overall like, do you want everybody to go through
37 00:04:23.229 ⇒ 00:04:25.769 Uttam Kumaran: like all of the core
38 00:04:26.109 ⇒ 00:04:32.349 Uttam Kumaran: like, do you want everybody to go through all of sort of data. Or do you want it to be more focused on data analysis?
39 00:04:36.150 ⇒ 00:04:41.220 Awaish Kumar: Like each one has, like different skill set.
40 00:04:41.720 ⇒ 00:04:42.030 Uttam Kumaran: Okay.
41 00:04:42.780 ⇒ 00:04:48.069 Awaish Kumar: The Vishnu. Like he. He has worked full time for for some.
42 00:04:48.320 ⇒ 00:04:52.939 Awaish Kumar: for one of the company, and have worked on on multiple different projects.
43 00:04:53.970 ⇒ 00:05:07.093 Awaish Kumar: is familiar with Python. Is this girl, and one of the things he was saying was like the the thing I I mostly hear from you and and the marketing team like you are picking up on engagement
44 00:05:08.080 ⇒ 00:05:12.759 Awaish Kumar: looking for like hosting some something. So he has
45 00:05:13.270 ⇒ 00:05:18.139 Awaish Kumar: work something similar, he was saying, like as a data analyst in a marketing team.
46 00:05:18.340 ⇒ 00:05:23.400 Awaish Kumar: So like scrapping, like like one of the project he mentioned was that
47 00:05:23.510 ⇒ 00:05:28.710 Awaish Kumar: kind of he scrap. For example, Youtube videos of of of
48 00:05:28.970 ⇒ 00:05:35.679 Awaish Kumar: data guys or the similar companies like us and try to figure out what type of content
49 00:05:35.930 ⇒ 00:05:38.490 Awaish Kumar: is is getting more traction.
50 00:05:40.260 ⇒ 00:05:41.649 Uttam Kumaran: Oh, I see. Okay.
51 00:05:42.420 ⇒ 00:05:44.240 Awaish Kumar: Yeah, and then.
52 00:05:44.240 ⇒ 00:05:46.717 Uttam Kumaran: My my question. There would be
53 00:05:48.190 ⇒ 00:05:50.550 Uttam Kumaran: I guess my question there would be, can we?
54 00:05:51.050 ⇒ 00:05:57.629 Uttam Kumaran: So I think with clearly, I have a need for like finance operations like a dashboard.
55 00:05:57.760 ⇒ 00:06:00.559 Uttam Kumaran: I also have a need for marketing.
56 00:06:00.900 ⇒ 00:06:05.629 Uttam Kumaran: So I think there is a sort of a marketing analysis path. There is also this, like
57 00:06:05.740 ⇒ 00:06:12.252 Uttam Kumaran: financial operations path that includes sort of like linear measurement.
58 00:06:13.110 ⇒ 00:06:18.889 Uttam Kumaran: like other task measurement, like a linear measurement clockify measurement. But I guess my question would be.
59 00:06:19.030 ⇒ 00:06:21.870 Uttam Kumaran: What is the 3? rd What do you think the 3rd option is?
60 00:06:22.110 ⇒ 00:06:24.519 Uttam Kumaran: Or do you think we should just have 2 people do?
61 00:06:25.060 ⇒ 00:06:27.150 Uttam Kumaran: We should just have. Those are the 2 pads.
62 00:06:27.380 ⇒ 00:06:33.884 Awaish Kumar: Yeah, like I, I would say, like the the other 2 people which I’m I think, like
63 00:06:34.930 ⇒ 00:06:35.700 Uttam Kumaran: Yeah, for the event.
64 00:06:36.193 ⇒ 00:06:40.629 Awaish Kumar: And Abigail. They they are not. Not that experienced.
65 00:06:40.820 ⇒ 00:06:43.400 Awaish Kumar: Yes, so like
66 00:06:44.100 ⇒ 00:06:56.849 Awaish Kumar: like they can work on these tasks. Maybe like like we can do like when we when we design a project, we can see like which one is bigger one, and we can keep the 2 in on one thing on one project.
67 00:06:57.650 ⇒ 00:07:04.939 Uttam Kumaran: Okay, yeah. The. So the the fine, the finance one is gonna have the most.
68 00:07:07.490 ⇒ 00:07:12.009 Uttam Kumaran: I mean, I actually, I don’t know, like, yeah, maybe we should. We should sort of decide
69 00:07:14.010 ⇒ 00:07:30.020 Uttam Kumaran: maybe we should decide which one is bigger. But I think there’s 2 paths. So one is like, yes, there’s actually a lot of like Linkedin and content related measurement that I want to do. There’s also, I think there’s also a lot of operational dashboards that I want built.
70 00:07:32.010 ⇒ 00:07:44.130 Uttam Kumaran: So I think we can just talk about what those are. And then, that’s like where we put people. But yeah, there’s plenty of sort of internal data analysis work that I I would like done. Whether that happens in
71 00:07:44.360 ⇒ 00:07:49.870 Uttam Kumaran: in real or whether that happens somewhere else, I think we can sort of decide.
72 00:07:50.800 ⇒ 00:07:51.164 Awaish Kumar: Okay.
73 00:07:54.470 ⇒ 00:07:55.120 Awaish Kumar: Okay.
74 00:07:55.795 ⇒ 00:07:56.470 Uttam Kumaran: Yeah.
75 00:07:57.050 ⇒ 00:08:04.640 Awaish Kumar: I don’t know. Like like am I going to like? Is our zoom working, taking notes?
76 00:08:05.695 ⇒ 00:08:11.830 Uttam Kumaran: Yeah, it’s taking notes. Yeah. So you could use, you can use the well, actually, let’s just let me just check if it’s recording.
77 00:08:12.440 ⇒ 00:08:16.769 Uttam Kumaran: Yeah, it’s recording. So you can. You can then use this to take the notes. Yeah.
78 00:08:19.410 ⇒ 00:08:21.599 Awaish Kumar: Okay, then we can talk about those.
79 00:08:24.740 ⇒ 00:08:25.400 Awaish Kumar: Screens.
80 00:08:25.400 ⇒ 00:08:29.320 Uttam Kumaran: Yeah. So a couple of things that I
81 00:08:29.640 ⇒ 00:08:35.260 Uttam Kumaran: sort of need short term is one. I want to get all the clockified data
82 00:08:35.730 ⇒ 00:08:40.240 Uttam Kumaran: into snowflake. So part, I think the data engineering work
83 00:08:40.350 ⇒ 00:08:45.869 Uttam Kumaran: like me or you or or you can ask them if they wanna take that and learn as well. But
84 00:08:46.430 ⇒ 00:08:49.569 Uttam Kumaran: first, st I need to get all the quackified data into
85 00:08:50.230 ⇒ 00:08:53.869 Uttam Kumaran: Snowflake. So I want to get all the clockified data, all the linear data
86 00:08:54.330 ⇒ 00:09:00.110 Uttam Kumaran: and all of our like people information into Snowflake.
87 00:09:00.440 ⇒ 00:09:03.179 Uttam Kumaran: and then start to build some like force.
88 00:09:03.600 ⇒ 00:09:08.570 Uttam Kumaran: Productivity. Related views like how many.
89 00:09:08.810 ⇒ 00:09:16.869 Uttam Kumaran: how many, how many tickets are being taken? What are the most? What are the tasks that are taking the longest to do?
90 00:09:18.650 ⇒ 00:09:20.277 Uttam Kumaran: Things like that?
91 00:09:20.940 ⇒ 00:09:24.549 Uttam Kumaran: So there’s gonna be a bunch of things on top. I have a bunch of questions I have
92 00:09:24.710 ⇒ 00:09:26.768 Uttam Kumaran: on top of the the
93 00:09:28.090 ⇒ 00:09:30.069 Uttam Kumaran: on top of the linear data. So
94 00:09:31.344 ⇒ 00:09:37.729 Uttam Kumaran: those will be the things that that I share there. So you have that linear data. And then
95 00:09:37.900 ⇒ 00:09:39.140 Uttam Kumaran: you have
96 00:09:41.780 ⇒ 00:09:44.589 Uttam Kumaran: So you have the linear data. And then, yeah, go ahead.
97 00:09:45.580 ⇒ 00:09:46.879 Awaish Kumar: So like the
98 00:09:47.990 ⇒ 00:09:55.589 Awaish Kumar: for the like for the like that you mentioned. If they want to take some tasks like, obviously they right now they are
99 00:09:56.170 ⇒ 00:10:03.958 Awaish Kumar: in the phase where they are asking, like, Okay, I can. I want to learn full end to end data engineering data analysis thing. But
100 00:10:05.030 ⇒ 00:10:05.990 Awaish Kumar: yeah, the rest.
101 00:10:05.990 ⇒ 00:10:11.509 Uttam Kumaran: The the risk, the risk there is that I don’t want them spending 4 weeks trying to learn python.
102 00:10:12.180 ⇒ 00:10:13.060 Awaish Kumar: You know.
103 00:10:13.310 ⇒ 00:10:17.359 Uttam Kumaran: I think they’re gonna get more value out of like actually doing some data analysis
104 00:10:17.730 ⇒ 00:10:20.969 Uttam Kumaran: and working with a stakeholder to answer questions.
105 00:10:21.410 ⇒ 00:10:22.580 Awaish Kumar: So.
106 00:10:24.330 ⇒ 00:10:28.350 Uttam Kumaran: Versus like Hitler, Daxer, and Python, and all this stuff.
107 00:10:28.550 ⇒ 00:10:29.770 Uttam Kumaran: It’s gonna be tougher.
108 00:10:32.980 ⇒ 00:10:34.079 Awaish Kumar: Yeah. Okay?
109 00:10:35.110 ⇒ 00:10:35.890 Awaish Kumar: For sure.
110 00:10:36.090 ⇒ 00:10:36.620 Awaish Kumar: 2 min.
111 00:10:36.620 ⇒ 00:10:41.439 Uttam Kumaran: So my, that’s my my feedback is like, okay on the operational side I have. There’s
112 00:10:41.710 ⇒ 00:10:48.330 Uttam Kumaran: a bunch of questions I have on, like our our operational performance like engineering productivity. Basically.
113 00:10:50.220 ⇒ 00:10:54.510 Uttam Kumaran: So there, that’s like one whole project. I’m I’m the direct stakeholder for that.
114 00:10:54.630 ⇒ 00:10:58.089 Uttam Kumaran: Me, me and Amber are probably the direct stakeholders for that work.
115 00:10:58.590 ⇒ 00:11:02.009 Uttam Kumaran: Second is, yes, there’s a lot of marketing analysis work.
116 00:11:02.290 ⇒ 00:11:03.979 Uttam Kumaran: So I want to look at
117 00:11:04.250 ⇒ 00:11:06.609 Uttam Kumaran: all of the performance of our
118 00:11:06.910 ⇒ 00:11:09.379 Uttam Kumaran: all of our website performance data.
119 00:11:11.270 ⇒ 00:11:15.250 Uttam Kumaran: I wanna look at all of our social media performance information.
120 00:11:16.170 ⇒ 00:11:18.330 Uttam Kumaran: I also want to look at
121 00:11:19.060 ⇒ 00:11:26.630 Uttam Kumaran: like the marketing teams, specific linear tickets and productivity, related metrics.
122 00:11:29.480 ⇒ 00:11:34.549 Uttam Kumaran: And then, so there’s there’s that data.
123 00:11:35.070 ⇒ 00:11:38.920 Uttam Kumaran: So yeah, that’s overall. Just like, who’s coming to the site. Where are they coming from?
124 00:11:39.070 ⇒ 00:11:45.020 Uttam Kumaran: What are they doing? And then, who’s looking on our Linkedin post? What Linkedin posts are performing better than others?
125 00:11:45.824 ⇒ 00:11:51.529 Uttam Kumaran: And probably their primary stakeholder is gonna be Ryan and Hannah.
126 00:11:53.960 ⇒ 00:11:54.620 Awaish Kumar: Great.
127 00:11:58.170 ⇒ 00:12:02.989 Uttam Kumaran: So I mean that I think between both those there’s more than enough work to do.
128 00:12:04.930 ⇒ 00:12:06.020 Awaish Kumar: Okay. Sure.
129 00:12:06.020 ⇒ 00:12:09.449 Uttam Kumaran: So. So I think you can use this linear ticket to sort of probably break
130 00:12:09.660 ⇒ 00:12:14.679 Uttam Kumaran: at least like high level tickets out. And and then, ideally like.
131 00:12:15.020 ⇒ 00:12:21.820 Uttam Kumaran: yeah, they should. I think you should. Also. One thing that could be helpful for them to learn is like, How do you work with a stakeholder right like.
132 00:12:22.100 ⇒ 00:12:26.620 Uttam Kumaran: how should you plan presentations? How should you take feedback?
133 00:12:26.780 ⇒ 00:12:36.810 Uttam Kumaran: How should you like be able to present on on your data and and get. Get your stakeholder to give you more information. I think that would also be helpful.
134 00:12:38.460 ⇒ 00:12:42.790 Uttam Kumaran: So yeah, I think the marketing piece and the operations piece are like kind of the biggest
135 00:12:43.310 ⇒ 00:12:45.060 Uttam Kumaran: things in my mind. Really.
136 00:12:47.950 ⇒ 00:12:56.029 Awaish Kumar: Okay, and in terms of like my thoughts, like how we, when I assign those.
137 00:12:58.960 ⇒ 00:13:05.480 Uttam Kumaran: Yeah, I think it. I think it’s tough, because we only have like one or data analyst. So I think
138 00:13:05.730 ⇒ 00:13:11.509 Uttam Kumaran: we should pair someone with Annie. I think Annie said she was open.
139 00:13:12.268 ⇒ 00:13:14.309 Uttam Kumaran: I also think that, like
140 00:13:16.120 ⇒ 00:13:23.459 Uttam Kumaran: I don’t think anyone who’s a stakeholder can be a direct mentor, so like I’m I, it’s gonna be tough for me. I do think, like
141 00:13:23.610 ⇒ 00:13:26.189 Uttam Kumaran: Kyle could probably be a good candidate as well.
142 00:13:28.560 ⇒ 00:13:30.650 Uttam Kumaran: Because he has a background in teaching.
143 00:13:32.990 ⇒ 00:13:41.100 Uttam Kumaran: And yeah, maybe we need to think of like one more person.
144 00:13:46.010 ⇒ 00:13:48.200 Uttam Kumaran: yeah. Kyle, Annie,
145 00:13:53.790 ⇒ 00:14:00.430 Uttam Kumaran: maybe if someone is doing a lot of modeling. They can work with them a lot of but
146 00:14:01.270 ⇒ 00:14:03.679 Uttam Kumaran: that may be it for now
147 00:14:04.000 ⇒ 00:14:07.010 Uttam Kumaran: or yeah, that’s that. May be it for now.
148 00:14:07.530 ⇒ 00:14:11.859 Uttam Kumaran: And but you should also label like what their mentors responsibilities are like.
149 00:14:12.090 ⇒ 00:14:16.270 Uttam Kumaran: are they to meet with a mentor once a week and like.
150 00:14:17.200 ⇒ 00:14:22.469 Uttam Kumaran: are they? And then also, like, I actually want those interns to also present on Friday meetings.
151 00:14:22.980 ⇒ 00:14:26.340 Uttam Kumaran: I think it’ll give them a lot of experience on how to present in front of a group.
152 00:14:26.770 ⇒ 00:14:27.090 Awaish Kumar: Yep.
153 00:14:27.090 ⇒ 00:14:32.059 Uttam Kumaran: Like, you know, present one slide on your progress this week, or whatever you know.
154 00:14:32.520 ⇒ 00:14:36.520 Uttam Kumaran: And then also, I think a final press final final presentation could be good as well.
155 00:14:38.810 ⇒ 00:14:39.640 Awaish Kumar: Okay.
156 00:14:45.530 ⇒ 00:14:48.470 Awaish Kumar: okay? And like, do you want me to
157 00:14:48.910 ⇒ 00:14:50.850 Awaish Kumar: run daily? Stand ups with them.
158 00:14:52.150 ⇒ 00:14:55.359 Uttam Kumaran: Yeah, I guess that’s a good question, like, What what is your like?
159 00:14:55.580 ⇒ 00:14:57.980 Uttam Kumaran: How do you feel? What do you think you’re like?
160 00:14:58.410 ⇒ 00:15:02.490 Uttam Kumaran: Availability is like. So I guess there’s a couple of things. One I’m
161 00:15:02.810 ⇒ 00:15:07.829 Uttam Kumaran: I don’t know. I’m not like a hundred percent sure. But I feel like I’m gonna be able to get you a little bit more time on Eden
162 00:15:08.090 ⇒ 00:15:10.260 Uttam Kumaran: like, give you a little bit time back.
163 00:15:11.155 ⇒ 00:15:17.740 Uttam Kumaran: So I think I found like a good source for like one or 2 junior aes
164 00:15:19.560 ⇒ 00:15:23.399 Uttam Kumaran: So the other thing is like.
165 00:15:23.620 ⇒ 00:15:30.779 Uttam Kumaran: Yeah, I mean, what do you? What is your app appetite for running stand ups with them like I would say.
166 00:15:31.180 ⇒ 00:15:34.840 Uttam Kumaran: it’s it would almost be a replacement, for, like your data platform work,
167 00:15:37.050 ⇒ 00:15:47.312 Uttam Kumaran: you know, and you can also assign them that work. And I will try to join as many of those as I can like. You’re I don’t think you’ll be alone like near me, or amber
168 00:15:48.270 ⇒ 00:15:52.060 Uttam Kumaran: So if you’re okay with that, I feel like that could be good.
169 00:15:53.490 ⇒ 00:15:59.850 Awaish Kumar: Yeah, like, that’s that’s I. That’s what I was thinking, because otherwise they might not.
170 00:15:59.850 ⇒ 00:16:00.460 Uttam Kumaran: Lose momentum.
171 00:16:00.460 ⇒ 00:16:01.349 Awaish Kumar: Able to work.
172 00:16:01.840 ⇒ 00:16:02.670 Awaish Kumar: Sorry.
173 00:16:03.480 ⇒ 00:16:09.899 Uttam Kumaran: Yeah, last year it was really hard we did an intern. But like I couldn’t do daily stand ups. And yeah, like.
174 00:16:10.160 ⇒ 00:16:11.930 Uttam Kumaran: it was difficult for a lot of them.
175 00:16:11.930 ⇒ 00:16:18.110 Awaish Kumar: Yeah, yeah, without any standoffs like they will lose the traction, and where
176 00:16:20.770 ⇒ 00:16:25.673 Awaish Kumar: I think I will run, maybe try to keep it short and simple and
177 00:16:26.120 ⇒ 00:16:28.679 Awaish Kumar: and like they can reach out to
178 00:16:29.710 ⇒ 00:16:31.318 Awaish Kumar: like we we can like
179 00:16:32.030 ⇒ 00:16:39.260 Awaish Kumar: motivate them to like reach out to us if there are any blockers to reach out to the Mentor, reach out to me.
180 00:16:39.910 ⇒ 00:16:40.750 Uttam Kumaran: Yeah.
181 00:16:45.010 ⇒ 00:16:48.500 Uttam Kumaran: And then, yeah, I guess we should find a way to get
182 00:16:49.110 ⇒ 00:16:55.799 Uttam Kumaran: like had. Like, I think I mean, our goal, of course, is to see if anyone after this program wants to join us. Right?
183 00:16:58.010 ⇒ 00:16:58.970 Uttam Kumaran: So I think
184 00:16:59.370 ⇒ 00:17:06.909 Uttam Kumaran: that’s probably my question for you and the mentors is at the end of the program. Would they recommend anyone
185 00:17:07.170 ⇒ 00:17:08.619 Uttam Kumaran: you know, full time?
186 00:17:09.718 ⇒ 00:17:15.520 Uttam Kumaran: I also think that a question I have is
187 00:17:18.980 ⇒ 00:17:26.020 Uttam Kumaran: like, do we want? Do you want to arrange like, are you gonna kind of do? Are they just gonna work on the projects? Or are you gonna sort of
188 00:17:26.230 ⇒ 00:17:29.639 Uttam Kumaran: assign any other kind of curriculum or like, how do you feel.
189 00:17:34.000 ⇒ 00:17:41.040 Awaish Kumar: Like, I think they, they are mostly going to work on data like these on these projects.
190 00:17:41.910 ⇒ 00:17:49.730 Awaish Kumar: It’s like, and whatever it includes like, if it if it has data, analytics, work or data analysis work
191 00:17:50.480 ⇒ 00:17:52.840 Awaish Kumar: and the like, the presentations.
192 00:17:58.380 ⇒ 00:17:59.000 Uttam Kumaran: Okay.
193 00:17:59.630 ⇒ 00:18:05.220 Awaish Kumar: And for the extra like, they will join Friday meetings with them, as their extracurricular activity.
194 00:18:07.740 ⇒ 00:18:14.890 Uttam Kumaran: Okay, my other. My other thing is like, how do we get everybody to use AI Bam?
195 00:18:15.410 ⇒ 00:18:20.360 Uttam Kumaran: How do we get everybody to use AI like from day one
196 00:18:20.770 ⇒ 00:18:30.570 Uttam Kumaran: like should we do? An AI training day like that’s, I think, something unique about Brainforge is like, how do we get everybody using cursor.
197 00:18:30.690 ⇒ 00:18:36.889 Uttam Kumaran: How do we get everybody to use the Zoom Meeting automations, the slack automations.
198 00:18:37.260 ⇒ 00:18:39.480 Awaish Kumar: Like, I think that could be the
199 00:18:39.670 ⇒ 00:18:50.489 Awaish Kumar: that’s part of onboarding like. For the 1st week we introduce them to the tools we use and how we use it and like like for this training part like we can.
200 00:18:51.090 ⇒ 00:18:51.690 Awaish Kumar: we can.
201 00:18:51.690 ⇒ 00:18:55.579 Uttam Kumaran: Do you want someone from the AI team to to run a session? Okay.
202 00:18:56.040 ⇒ 00:19:04.700 Awaish Kumar: Yeah. So like, when someone from realization comes in and says, Okay, that’s how you can create a project in cursor from scratch.
203 00:19:05.760 ⇒ 00:19:10.159 Uttam Kumaran: Yes, so like how to set up your cursor.
204 00:19:11.102 ⇒ 00:19:16.689 Uttam Kumaran: What is cursor? How to use it like, I want someone on the AI team to sort of walk through the whole thing.
205 00:19:18.980 ⇒ 00:19:22.630 Uttam Kumaran: So let’s definitely put that and that person will need to prep. So
206 00:19:22.820 ⇒ 00:19:25.059 Uttam Kumaran: I think you can. Just I don’t know if.
207 00:19:25.600 ⇒ 00:19:28.429 Uttam Kumaran: if you think like, Miguel may be the best person there.
208 00:19:31.445 ⇒ 00:19:31.960 Uttam Kumaran: Like.
209 00:19:31.960 ⇒ 00:19:35.660 Awaish Kumar: Yeah, I, I, actually.
210 00:19:36.980 ⇒ 00:19:39.609 Uttam Kumaran: Or you can do it, too, are good. Like.
211 00:19:39.890 ⇒ 00:19:44.012 Uttam Kumaran: yeah, yeah, yeah. I think if I think if I think maybe ask
212 00:19:45.520 ⇒ 00:19:49.020 Uttam Kumaran: I mean? You could ask. Yeah, you could ask both of them to be there and then.
213 00:19:49.140 ⇒ 00:19:53.240 Uttam Kumaran: both of them and you, and then all the interns. I think that’s a great session to do
214 00:19:53.350 ⇒ 00:19:55.679 Uttam Kumaran: how to use AI at Brainforge.
215 00:19:59.310 ⇒ 00:20:01.400 Uttam Kumaran: So I think that’s 1 thing.
216 00:20:06.850 ⇒ 00:20:15.250 Awaish Kumar: And the other things are like introduction to like the using our demo. This zoom Zoom Meeting side.
217 00:20:15.250 ⇒ 00:20:20.029 Uttam Kumaran: So. No, that’s that’s the whole. That’s that whole session, all of the Zoom, all the AI tools.
218 00:20:21.930 ⇒ 00:20:22.430 Awaish Kumar: Okay.
219 00:20:25.180 ⇒ 00:20:29.220 Uttam Kumaran: And then if you if I’m I mean for for both of those projects.
220 00:20:29.380 ⇒ 00:20:31.790 Uttam Kumaran: I think the stakeholders should do
221 00:20:31.990 ⇒ 00:20:36.490 Uttam Kumaran: a little bit of like a presentation on why this matters.
222 00:20:37.960 ⇒ 00:20:38.255 Awaish Kumar: Yeah.
223 00:20:38.550 ⇒ 00:20:47.250 Uttam Kumaran: Like, I’m happy to do a couple of slides on why operational data matters.
224 00:20:47.830 ⇒ 00:20:55.080 Uttam Kumaran: you know, and like what what our, what our goals are, and like how they’re gonna impact the business. I think. Similarly, on the marketing side, you should have
225 00:20:56.680 ⇒ 00:21:03.250 Uttam Kumaran: Anna and Ryan do a brief presentation on like.
226 00:21:03.470 ⇒ 00:21:08.889 Uttam Kumaran: why, the metrics, we’re measuring matter. And like how we what is brand at Brain Forge
227 00:21:09.030 ⇒ 00:21:12.089 Uttam Kumaran: that way, it’ll make you motivated to actually solve the problem.
228 00:21:15.410 ⇒ 00:21:21.800 Awaish Kumar: Okay? So that that’s that’s also part of the 1st week onboarding sessions.
229 00:21:22.240 ⇒ 00:21:22.720 Uttam Kumaran: Okay.
230 00:21:23.710 ⇒ 00:21:24.560 Awaish Kumar: Which one.
231 00:21:24.560 ⇒ 00:21:25.060 Uttam Kumaran: And then.
232 00:21:25.060 ⇒ 00:21:29.442 Awaish Kumar: Like that’s that way, like they meet. They meet with the team. And
233 00:21:30.030 ⇒ 00:21:32.689 Awaish Kumar: no like, maybe the tools we are using.
234 00:21:33.100 ⇒ 00:21:35.940 Awaish Kumar: and we’ll get ready to start it for me. Then.
235 00:21:39.020 ⇒ 00:21:47.969 Uttam Kumaran: And then my other question would be, I think we should definitely try to do, maybe do some like you, are you? When are you planning on starting, though the whole thing.
236 00:21:48.760 ⇒ 00:21:49.670 Awaish Kumar: Next week
237 00:21:50.070 ⇒ 00:22:03.869 Awaish Kumar: I was start. I was planning to to send them some some kind of plan from tomorrow, like, and get the answer from them, and then in the next week to start it.
238 00:22:05.200 ⇒ 00:22:09.429 Uttam Kumaran: Okay, cool. So then, one action item. Also, I’ll get you offer letters
239 00:22:09.940 ⇒ 00:22:12.419 Uttam Kumaran: so you can send a formal offer letter
240 00:22:13.160 ⇒ 00:22:21.380 Uttam Kumaran: for an internship. I also think you should ask them if they’re open to doing like a Linkedin announcement. We can prepare that.
241 00:22:22.390 ⇒ 00:22:22.900 Awaish Kumar: Okay.
242 00:22:23.990 ⇒ 00:22:30.709 Uttam Kumaran: I think I would just need like a we. We have some marketing would have some requirements, but I can have them work
243 00:22:31.200 ⇒ 00:22:32.903 Uttam Kumaran: with you on that
244 00:22:34.380 ⇒ 00:22:36.020 Uttam Kumaran: And
245 00:22:39.280 ⇒ 00:22:40.983 Uttam Kumaran: yeah, I feel like,
246 00:22:43.450 ⇒ 00:22:51.470 Uttam Kumaran: that’s pretty good. I mean, you know, I’m not going anywhere. I just wanna make sure that you can own a good chunk of that. And then, yeah, I mean, I’m hopeful that at least
247 00:22:52.160 ⇒ 00:22:54.590 Uttam Kumaran: hopefully, at least one person from that group.
248 00:22:55.590 ⇒ 00:22:59.489 Uttam Kumaran: you know, seems like someone that we can bring on for full time.
249 00:23:00.970 ⇒ 00:23:07.289 Awaish Kumar: Okay? And okay? And like.
250 00:23:07.710 ⇒ 00:23:11.629 Awaish Kumar: So where? When would you like to meet with that prade?
251 00:23:12.619 ⇒ 00:23:19.079 Uttam Kumaran: Yeah. Can you ask him if he has time, or can you ask him just for his Whatsapp like? Because I’ll just call him.
252 00:23:19.531 ⇒ 00:23:22.789 Uttam Kumaran: I’m sort of in and out of stuff like. Do you think he’d be open for that?
253 00:23:25.970 ⇒ 00:23:26.650 Awaish Kumar: French?
254 00:23:27.150 ⇒ 00:23:29.489 Awaish Kumar: Should I give you his Whatsapp number.
255 00:23:30.010 ⇒ 00:23:36.779 Uttam Kumaran: Yeah, if you if you think he’d be okay with me, just messaging him there, that’s gonna be easiest. It’s gonna be easiest thing for me.
256 00:23:37.720 ⇒ 00:23:42.548 Awaish Kumar: Okay, then, yeah, he he can. He should be okay with that. And
257 00:23:43.750 ⇒ 00:23:48.619 Awaish Kumar: I’ll let him know and give you your number. Give give you his number.
258 00:23:54.410 ⇒ 00:23:55.220 Awaish Kumar: I have to.
259 00:23:57.200 ⇒ 00:24:01.980 Awaish Kumar: But yeah, I am.
260 00:24:19.600 ⇒ 00:24:20.860 Uttam Kumaran: Okay, perfect.
261 00:24:29.500 ⇒ 00:24:36.510 Awaish Kumar: So yeah, like, amber is not today here, like tomorrow, I will meet with her to actually
262 00:24:37.290 ⇒ 00:24:42.659 Awaish Kumar: expand these projects like, I will work on that, and then meet with him to clear all those tickets.
263 00:24:43.370 ⇒ 00:24:44.880 Uttam Kumaran: Okay.
264 00:24:46.640 ⇒ 00:24:52.469 Awaish Kumar: Yeah, I end up and the plan, and then we tomorrow. We can also send the offer letters.
265 00:24:53.220 ⇒ 00:24:54.330 Uttam Kumaran: Okay. Okay.
266 00:24:55.150 ⇒ 00:24:56.299 Uttam Kumaran: Okay. Perfect.
267 00:24:58.170 ⇒ 00:24:58.830 Awaish Kumar: Okay, and.
268 00:24:58.830 ⇒ 00:24:59.440 Uttam Kumaran: Oh, my goodness!
269 00:24:59.440 ⇒ 00:25:06.660 Awaish Kumar: We want to send the like within the plan, like, what do we want to send them like.
270 00:25:07.180 ⇒ 00:25:08.109 Uttam Kumaran: I think you should send.
271 00:25:08.110 ⇒ 00:25:08.580 Awaish Kumar: I know.
272 00:25:08.580 ⇒ 00:25:10.699 Uttam Kumaran: I think you should send them the notion, Doc.
273 00:25:11.030 ⇒ 00:25:11.360 Awaish Kumar: Okay.
274 00:25:11.360 ⇒ 00:25:16.579 Uttam Kumaran: Like, I think after this meeting, take the transcript edit. The notion, Doc. I think that’s the plan.
275 00:25:17.520 ⇒ 00:25:18.160 Uttam Kumaran: And.
276 00:25:18.160 ⇒ 00:25:28.749 Awaish Kumar: Like my question was like, Do we want to add the details of the project, or just high level, like what kind of things they are going to learn like tools and stuff like that.
277 00:25:28.750 ⇒ 00:25:46.259 Uttam Kumaran: No, I I think. Well, I I don’t think the project details like we’re gonna have everything laid out. But in the I want them to also learn what it’s like to work with a stakeholder, get requirements, break them into tickets, work with you to plan those tickets, execute, get the review like.
278 00:25:46.500 ⇒ 00:25:48.959 Uttam Kumaran: you know what I mean. That’s more important.
279 00:25:49.240 ⇒ 00:25:54.129 Uttam Kumaran: Then, like getting a marketing like that’s what we need them to learn
280 00:25:55.590 ⇒ 00:26:02.129 Uttam Kumaran: like they’re gonna learn. Hey? My stakeholder isn’t getting back to me. What do I do? Right? That’s like a
281 00:26:02.640 ⇒ 00:26:06.519 Uttam Kumaran: that’s a great question. And that’s what we’re consult. We’re consultants.
282 00:26:06.710 ⇒ 00:26:18.499 Uttam Kumaran: right? So that’s what we I want them to end up with is like, how do I move a project forward reliably? And so I think it’s helpful for them to see in it, like we can get requirements for all those. And I think
283 00:26:18.690 ⇒ 00:26:20.080 Uttam Kumaran: that’ll be okay.
284 00:26:20.870 ⇒ 00:26:22.010 Awaish Kumar: Okay, fine.
285 00:26:47.700 ⇒ 00:26:49.960 Awaish Kumar: Then I don’t have any.
286 00:26:49.960 ⇒ 00:26:59.829 Uttam Kumaran: I guess. My, I guess I wanted to talk a little bit about this. While I have you for a sec. This kite site thing. Did you see the the thing I sent in Managers Channel.
287 00:27:01.820 ⇒ 00:27:06.700 Awaish Kumar: Yeah, I saw my email that you have sent something from kites.
288 00:27:07.470 ⇒ 00:27:11.100 Uttam Kumaran: Yeah, maybe just check that link. I check that link. I put there
289 00:27:12.090 ⇒ 00:27:15.239 Uttam Kumaran: really quick. I just wanna get your feedback.
290 00:27:41.850 ⇒ 00:27:44.010 Awaish Kumar: So outside is a client or.
291 00:27:44.690 ⇒ 00:27:49.890 Uttam Kumaran: No, no, Skyside is, I’m gonna basically start sourcing Junior
292 00:27:50.100 ⇒ 00:27:52.400 Uttam Kumaran: and mid and junior people from.
293 00:27:53.210 ⇒ 00:28:02.150 Uttam Kumaran: because right now we’re recruiting, for I want us to focus more on recruiting, more senior people took hindsight.
294 00:28:02.950 ⇒ 00:28:10.019 Uttam Kumaran: Kite is like the is A is a firm that they have a lot of like
295 00:28:10.270 ⇒ 00:28:14.140 Uttam Kumaran: junior mid level data and AI engineers.
296 00:28:14.910 ⇒ 00:28:15.600 Awaish Kumar: Okay.
297 00:28:16.260 ⇒ 00:28:23.550 Uttam Kumaran: I’m just just data engineer. Sorry. So I basically my, I want to pull you slowly off of like.
298 00:28:24.000 ⇒ 00:28:30.349 Uttam Kumaran: like, lower level Eden work like you mentioned last time, like, Hey, they’re still modeling things.
299 00:28:30.630 ⇒ 00:28:32.059 Uttam Kumaran: I want to hand that off.
300 00:28:35.440 ⇒ 00:28:36.180 Awaish Kumar: Okay.
301 00:28:38.114 ⇒ 00:28:42.229 Uttam Kumaran: So that’s 1 thing, and then second is, we have 3 or 4 more clients
302 00:28:42.540 ⇒ 00:28:45.430 Uttam Kumaran: I want. We don’t have like. I don’t think
303 00:28:45.690 ⇒ 00:28:49.500 Uttam Kumaran: like, I just wanna make sure we have available talent for those
304 00:28:49.740 ⇒ 00:28:57.580 Uttam Kumaran: like, I think Kyle is gonna be able to help on probably one of them. But I think that
305 00:28:57.970 ⇒ 00:28:59.569 Uttam Kumaran: they’re gonna need more help.
306 00:29:00.650 ⇒ 00:29:05.640 Uttam Kumaran: So I want to understand, like for all those clients, if if we need aes like.
307 00:29:07.110 ⇒ 00:29:08.509 Uttam Kumaran: can they supply it
308 00:29:14.460 ⇒ 00:29:14.880 Uttam Kumaran: right.
309 00:29:14.880 ⇒ 00:29:16.850 Awaish Kumar: Oh, no! Let me see here!
310 00:29:21.280 ⇒ 00:29:23.660 Uttam Kumaran: For example, we don’t have a power bi person.
311 00:29:25.100 ⇒ 00:29:25.940 Awaish Kumar: Yeah.
312 00:29:27.980 ⇒ 00:29:32.559 Uttam Kumaran: But this is also where, like, I want us to be able to have people short term, and then
313 00:29:32.920 ⇒ 00:29:38.570 Uttam Kumaran: we should be able to interview for people we want to bring on full time like, you know what I mean. So
314 00:29:39.270 ⇒ 00:29:42.460 Uttam Kumaran: I I want, I don’t want us to scramble anymore for people.
315 00:29:45.750 ⇒ 00:29:49.189 Awaish Kumar: Okay. And like, like, I said.
316 00:29:50.760 ⇒ 00:29:55.540 Awaish Kumar: kind of like, talent provider, or they just post the.
317 00:29:55.540 ⇒ 00:30:07.859 Uttam Kumaran: Yeah. So no. So kitesight. So kitesight is someone I met here I met I met a I met this guy deep Vagani. He’s be. He runs Kitesight. He’s like us. He’s like a brain forge, except like much smaller.
318 00:30:08.333 ⇒ 00:30:19.610 Uttam Kumaran: So he has. Like 10 or 11 people in India. They’re all well trained he himself is, is like kind of similar background to me. And you like led data teams and is a data engineer.
319 00:30:21.220 ⇒ 00:30:29.385 Uttam Kumaran: And so his model is like he’s growing his company. So he wants to work with companies like ours and help with staffing
320 00:30:30.080 ⇒ 00:30:32.879 Uttam Kumaran: and also wants to grow like his business
321 00:30:33.040 ⇒ 00:30:49.419 Uttam Kumaran: for us. Like I, I need I. When I go hire data people I go to like 5 or 6 different sources. I I just want to go to one person if I can. And so I found that like, it looks like pretty possible if I just go through him. So I wanted to basically see like.
322 00:30:49.690 ⇒ 00:30:58.990 Uttam Kumaran: do you think if the 1st target is like Eden, then that’s where we’ll start like trying to get someone to take off like, take on the most, the really basic things on Eden for you.
323 00:31:03.420 ⇒ 00:31:08.739 Awaish Kumar: Alright, I see we. We already have someone. Kvc.
324 00:31:10.760 ⇒ 00:31:12.779 Uttam Kumaran: Yeah, ABC, is Luke right now.
325 00:31:13.300 ⇒ 00:31:15.420 Uttam Kumaran: but no one on like data engineering.
326 00:31:19.940 ⇒ 00:31:20.600 Awaish Kumar: Okay.
327 00:31:21.300 ⇒ 00:31:24.920 Uttam Kumaran: Like me, and you are the only data engineers. Let’s put it that way.
328 00:31:27.210 ⇒ 00:31:27.770 Awaish Kumar: Yeah.
329 00:31:28.890 ⇒ 00:31:34.540 Uttam Kumaran: So that’s hard, because both of us are going to be the busiest.
330 00:31:38.270 ⇒ 00:31:46.100 Uttam Kumaran: So I want to get one more data engineer and probably one more analytics engineer have them work, probably
331 00:31:47.350 ⇒ 00:31:49.400 Uttam Kumaran: 1020 HA week. We’ll see.
332 00:31:50.910 ⇒ 00:31:51.820 Awaish Kumar: Yeah.
333 00:31:52.370 ⇒ 00:31:58.169 Awaish Kumar: And these people from kites side. How like, how are the rates.
334 00:31:59.370 ⇒ 00:32:01.589 Uttam Kumaran: The rates are pretty good for
335 00:32:01.910 ⇒ 00:32:09.609 Uttam Kumaran: out. So for like bringing them in. But I think what what? I’m what I’m what I want us to focus on is getting people full time
336 00:32:09.780 ⇒ 00:32:12.610 Uttam Kumaran: like longer term.
337 00:32:13.030 ⇒ 00:32:22.149 Uttam Kumaran: So I see them as like a Band-aid. And then we look for like, okay, one of the interns gonna convert or let’s go interview. For, like someone at our level
338 00:32:22.330 ⇒ 00:32:27.609 Uttam Kumaran: versus like just getting like tons of people in
339 00:32:28.280 ⇒ 00:32:32.900 Uttam Kumaran: which is fine like we? It’s it’s just difficult to do that like I did that last time. And
340 00:32:33.410 ⇒ 00:32:40.300 Uttam Kumaran: I’m telling you, for when I talk to you I talked to like a hundred other people. So it’s it’s really time intensive.
341 00:32:40.770 ⇒ 00:32:46.660 Uttam Kumaran: So I want to be able to like go source candidates very thoughtfully versus like.
342 00:32:47.250 ⇒ 00:32:52.680 Uttam Kumaran: Hey, we have position. Let’s interview anybody, you know, like I want all of us to be involved in that.
343 00:32:53.840 ⇒ 00:32:55.040 Awaish Kumar: Okay, okay.
344 00:33:04.470 ⇒ 00:33:06.849 Awaish Kumar: yeah, we are like, if
345 00:33:06.970 ⇒ 00:33:12.010 Awaish Kumar: I see the more d work on fault if they want us to implement things.
346 00:33:12.920 ⇒ 00:33:13.710 Uttam Kumaran: Oh, really. Okay.
347 00:33:15.080 ⇒ 00:33:15.900 Awaish Kumar: Yeah.
348 00:33:16.930 ⇒ 00:33:19.629 Uttam Kumaran: So let’s let me let me note that in there
349 00:33:23.400 ⇒ 00:33:27.760 Awaish Kumar: Because, like what I heard that like, they are more
350 00:33:27.880 ⇒ 00:33:32.749 Awaish Kumar: obviously, we need some modeling. But it’s more like how to bring in data. First, st right?
351 00:33:35.660 ⇒ 00:33:37.970 Uttam Kumaran: Okay, so let me let me note that. So
352 00:33:39.100 ⇒ 00:33:43.270 Uttam Kumaran: for Eden, we’re gonna need a de for Emr.
353 00:33:44.280 ⇒ 00:33:47.200 Awaish Kumar: And then do you need an ae for your work as well?
354 00:33:47.991 ⇒ 00:33:53.110 Awaish Kumar: I don’t think we need de for for Emr like
355 00:33:53.520 ⇒ 00:33:57.300 Awaish Kumar: so like they are building the mostly the
356 00:33:58.580 ⇒ 00:34:07.980 Awaish Kumar: like on the de side. What we need is only one person who can add those connectors in in segment right.
357 00:34:10.630 ⇒ 00:34:13.939 Uttam Kumaran: Yeah, but I guess, like, tell me, what like are there custom.
358 00:34:13.949 ⇒ 00:34:20.689 Awaish Kumar: It’s more like work. It’s more like for the emr the
359 00:34:21.329 ⇒ 00:34:25.799 Awaish Kumar: what is what data exists today and what do we need right things like that, like
360 00:34:26.109 ⇒ 00:34:31.119 Awaish Kumar: able, able, able to like like the map out the data we need.
361 00:34:31.349 ⇒ 00:34:37.159 Awaish Kumar: And then in what format, like kind of data, warehouse design, right?
362 00:34:38.246 ⇒ 00:34:39.719 Awaish Kumar: And then
363 00:34:39.879 ⇒ 00:34:53.899 Awaish Kumar: when that data comes in through events, we just need to build some segment events. And then again, a work. So it’s mostly a work some designing work like the data warehouse design or
364 00:34:54.169 ⇒ 00:35:01.259 Awaish Kumar: and plus through some D work like adding segment connectors.
365 00:35:06.600 ⇒ 00:35:07.300 Uttam Kumaran: Okay.
366 00:35:09.690 ⇒ 00:35:10.900 Uttam Kumaran: So
367 00:35:11.180 ⇒ 00:35:15.650 Uttam Kumaran: you think it’s like, how much, how, how much ae work you think there is like in terms of hours.
368 00:35:17.110 ⇒ 00:35:28.419 Awaish Kumar: So like, just not just for Emr, like the what I’m taking out is is is easily 15 to 20 h. Right? It’s it’s not. It’s just for.
369 00:35:29.350 ⇒ 00:35:29.789 Uttam Kumaran: For the 8.
370 00:35:30.930 ⇒ 00:35:33.983 Awaish Kumar: Yeah, like it’s for. It’s marketing. It’s
371 00:35:35.688 ⇒ 00:35:41.399 Awaish Kumar: like, like the customer I like. It’s it’s, I think, customer service marketing.
372 00:35:41.600 ⇒ 00:35:45.980 Awaish Kumar: And how all of that work
373 00:35:53.770 ⇒ 00:35:56.700 Awaish Kumar: and that like on the Mr. Side, like.
374 00:35:56.920 ⇒ 00:35:59.390 Awaish Kumar: So what I wanted help with is like
375 00:36:00.060 ⇒ 00:36:02.650 Awaish Kumar: when we are building this data
376 00:36:03.800 ⇒ 00:36:08.539 Awaish Kumar: warehouse design or something. I want to go in deeper in, in all the details, like.
377 00:36:08.930 ⇒ 00:36:12.049 Awaish Kumar: so build some documentation like, offer all the.
378 00:36:12.547 ⇒ 00:36:13.560 Uttam Kumaran: Okay. So let’s.
379 00:36:13.560 ⇒ 00:36:13.890 Awaish Kumar: Thank God!
380 00:36:13.890 ⇒ 00:36:14.779 Uttam Kumaran: Way you were.
381 00:36:15.630 ⇒ 00:36:16.420 Awaish Kumar: Yeah.
382 00:36:16.860 ⇒ 00:36:20.750 Uttam Kumaran: Okay, okay? So like, I think, overall 15 to 20 h ae.
383 00:36:20.910 ⇒ 00:36:30.499 Uttam Kumaran: we have ae work for Emr planning and ae work for general work for primary work, stream, right.
384 00:36:31.030 ⇒ 00:36:31.690 Awaish Kumar: Yes.
385 00:36:33.470 ⇒ 00:36:34.100 Uttam Kumaran: Okay?
386 00:36:34.250 ⇒ 00:36:38.110 Uttam Kumaran: And then how about matter? More stuff?
387 00:36:42.980 ⇒ 00:36:45.660 Awaish Kumar: So on the macamo side. I
388 00:36:46.130 ⇒ 00:36:52.899 Awaish Kumar: I think, like you can help like the any like. We don’t need more resources.
389 00:36:53.350 ⇒ 00:36:56.139 Awaish Kumar: whatever capacity they have right now, like
390 00:36:57.650 ⇒ 00:36:59.938 Awaish Kumar: it’s fine, it’s moving forward. And
391 00:37:01.450 ⇒ 00:37:03.830 Awaish Kumar: right now, like we don’t have any.
392 00:37:04.820 ⇒ 00:37:05.760 Awaish Kumar: Come on
393 00:37:06.020 ⇒ 00:37:13.130 Awaish Kumar: like the scope is the same right. We have not increased our scope. So it’s the same thing we are building right now.
394 00:37:13.920 ⇒ 00:37:16.769 Awaish Kumar: We are just moving the existing project. Only
395 00:37:17.190 ⇒ 00:37:22.819 Awaish Kumar: Dbt, we have access to power bi so, and he will build in some dashboards, and that’s all
396 00:37:25.030 ⇒ 00:37:27.481 Awaish Kumar: loop is, I think now it’s
397 00:37:28.340 ⇒ 00:37:33.969 Awaish Kumar: This has more availability, because from the metamor side kind of modeling work is kind of
398 00:37:35.320 ⇒ 00:37:38.350 Awaish Kumar: done right? It’s just final finishing work.
399 00:37:39.860 ⇒ 00:37:42.729 Awaish Kumar: I’m doing right. So line data. And
400 00:37:42.920 ⇒ 00:37:49.979 Awaish Kumar: and it requires some more changes, but so far it’s that’s a good one.
401 00:37:55.220 ⇒ 00:38:00.239 Awaish Kumar: So I think he’s spending like, 2, 3, 5, 5, 10 h. Yeah.
402 00:38:02.859 ⇒ 00:38:03.309 Uttam Kumaran: Okay.
403 00:38:03.310 ⇒ 00:38:04.120 Awaish Kumar: Analyt, file.
404 00:38:04.120 ⇒ 00:38:06.170 Uttam Kumaran: Loop with me on fan steak.
405 00:38:06.340 ⇒ 00:38:11.300 Uttam Kumaran: I think we’re gonna consider Kyle for one of these.
406 00:38:11.720 ⇒ 00:38:15.410 Uttam Kumaran: and then for this one, we’ll need a support. Probably.
407 00:38:30.670 ⇒ 00:38:33.680 Uttam Kumaran: Okay, so okay, makes sense.
408 00:38:51.860 ⇒ 00:38:53.180 Uttam Kumaran: Okay, okay, great.
409 00:38:53.380 ⇒ 00:39:04.159 Uttam Kumaran: Alright. Thank you. That’s all I wanted. Okay, so yeah, take the Zoom Meeting and maybe prepare that, and then just ping me for for stuff. Today as you need it, I’m I have a big working session with AI team later. So.
410 00:39:06.010 ⇒ 00:39:07.200 Awaish Kumar: Okay. Sure.
411 00:39:09.020 ⇒ 00:39:11.750 Uttam Kumaran: Okay, thank you, Dude. I’ll talk to you soon.
412 00:39:11.750 ⇒ 00:39:12.370 Awaish Kumar: Houston.
413 00:39:12.750 ⇒ 00:39:13.780 Uttam Kumaran: Okay? Bye.