Meeting Title: Uttam Date: 2025-03-17 Meeting participants: Annie Yu, Uttam Kumaran
WEBVTT
1 00:02:06.720 ⇒ 00:02:07.680 Uttam Kumaran: Hey! Annie!
2 00:02:08.440 ⇒ 00:02:09.949 Annie Yu: Hello, Tim!
3 00:02:10.190 ⇒ 00:02:11.219 Uttam Kumaran: Hey! How are you?
4 00:02:12.080 ⇒ 00:02:14.250 Uttam Kumaran: Good! Nice to see you
5 00:02:14.520 ⇒ 00:02:15.750 Annie Yu: Nice to see you. Hi!
6 00:02:15.750 ⇒ 00:02:16.819 Uttam Kumaran: How was the weekend
7 00:02:17.510 ⇒ 00:02:24.730 Annie Yu: Pretty good. I recently we moved from like an apartment to the house, so there’s a lot of space to fill, and there’s
8 00:02:24.730 ⇒ 00:02:25.460 Uttam Kumaran: Nice
9 00:02:25.660 ⇒ 00:02:29.479 Annie Yu: A small garden ish kind of thing. It’s like a pergola.
10 00:02:30.340 ⇒ 00:02:32.709 Uttam Kumaran: Oh, no way. Okay. Lucky.
11 00:02:32.710 ⇒ 00:02:46.230 Annie Yu: Like grapevines as well as roses there. So I was like learning how to print them before spring comes, and things of that nature so
12 00:02:46.750 ⇒ 00:02:49.560 Uttam Kumaran: Good. That’s like a weekend off the computer. That’s nice
13 00:02:49.560 ⇒ 00:02:53.300 Annie Yu: I know how was your weekend? I saw you went on a picnic
14 00:02:53.300 ⇒ 00:02:58.769 Uttam Kumaran: Yeah, we were just at the Park for a bit, and then we went to see a concert later that night on Saturday
15 00:02:58.770 ⇒ 00:02:59.170 Annie Yu: Oh!
16 00:02:59.170 ⇒ 00:03:06.380 Uttam Kumaran: I. I also live in a house, and my girlfriend just moved in. So we’re like cleaning a bunch of stuff up and
17 00:03:06.590 ⇒ 00:03:07.035 Annie Yu: Yeah.
18 00:03:07.480 ⇒ 00:03:15.473 Uttam Kumaran: And I’m I’m trying to spend more time in my garden as well. My dad’s visiting next weekend, and he’s like a big gardener. So I’m gonna get his help.
19 00:03:15.740 ⇒ 00:03:16.413 Annie Yu: So nice.
20 00:03:16.750 ⇒ 00:03:27.339 Uttam Kumaran: Yeah, cause I cook a lot. And I want to have. I want to have mint parsley types like, I want to have a lot of herbs available, because it’s also so expensive to buy it from the grocery store
21 00:03:27.340 ⇒ 00:03:28.600 Annie Yu: Totally. Yeah.
22 00:03:28.600 ⇒ 00:03:33.610 Uttam Kumaran: Like we get a lot of sun here. And so I think this weekend, I’m gonna do a lot of planting for that
23 00:03:33.910 ⇒ 00:03:39.349 Annie Yu: Oh, I love that well, let me know you planned, and what’s easiest to take
24 00:03:39.350 ⇒ 00:03:44.780 Uttam Kumaran: I will, I will. I have a lot of indoor plants. I did really. Well in with indoor plants. I like.
25 00:03:44.930 ⇒ 00:03:50.129 Uttam Kumaran: I learned, okay, what are the cadence of watering like where to put them? And the house house gets a lot of light.
26 00:03:50.539 ⇒ 00:03:58.630 Uttam Kumaran: But I think outdoor it’s tough because it gets like so hot in the summer. So sometimes I don’t know what to plan, and everything dies so
27 00:03:58.910 ⇒ 00:04:02.949 Annie Yu: Yeah. Well, I feel like it’s the opposite up here.
28 00:04:03.750 ⇒ 00:04:04.200 Uttam Kumaran: Yeah.
29 00:04:04.200 ⇒ 00:04:10.750 Annie Yu: To like. Get a lemon tree and learn that it’s gonna be hard to get them alive here in
30 00:04:10.750 ⇒ 00:04:13.480 Uttam Kumaran: Yes, yes.
31 00:04:16.050 ⇒ 00:04:22.630 Uttam Kumaran: Cool. Well, super super excited to kind of kick off this week.
32 00:04:22.820 ⇒ 00:04:26.839 Uttam Kumaran: I wanted to just start by, you know, kind of explaining.
33 00:04:26.940 ⇒ 00:04:35.319 Uttam Kumaran: maybe a little bit about the client. But also sort of talk more technically about like, what are the different tools that we have in the system
34 00:04:35.320 ⇒ 00:04:35.700 Annie Yu: I,
35 00:04:36.900 ⇒ 00:04:40.462 Uttam Kumaran: So I don’t know. Did you have a chance to? You’re you’re you’re in the
36 00:04:40.880 ⇒ 00:04:48.350 Uttam Kumaran: Javi Channel. You’re in one password and everything. Okay. So maybe one thing that I’ll do is maybe I’ll just talk a little bit through
37 00:04:48.742 ⇒ 00:04:53.549 Uttam Kumaran: and I’ll sort of poke around a couple of things and and share that with you about
38 00:04:55.180 ⇒ 00:04:58.085 Uttam Kumaran: how this client is working today. So right now, we have
39 00:04:58.630 ⇒ 00:05:05.110 Uttam Kumaran: we have a couple of different core systems we have portable, which is our Etl tool that brings in data into Snowflake.
40 00:05:05.716 ⇒ 00:05:10.010 Uttam Kumaran: I’ll just share that. Here.
41 00:05:11.380 ⇒ 00:05:16.450 Uttam Kumaran: So right now, we have a couple of different flows of like stuff into snowflake.
42 00:05:18.620 ⇒ 00:05:21.938 Uttam Kumaran: That all goes, of course, into Snowflake, and let me just
43 00:05:23.250 ⇒ 00:05:27.339 Uttam Kumaran: log in so you can see sort of what the situation is. There
44 00:05:27.340 ⇒ 00:05:30.780 Annie Yu: And you are the owner of this right, not the client
45 00:05:30.970 ⇒ 00:05:37.240 Uttam Kumaran: Yes, we well, I guess it depends on what you mean. Like we we are the ones building
46 00:05:37.380 ⇒ 00:05:38.860 Uttam Kumaran: on this exclusively
47 00:05:38.860 ⇒ 00:05:39.420 Annie Yu: Oh!
48 00:05:39.420 ⇒ 00:05:45.139 Uttam Kumaran: They? Of course they they’re they’re paying for it. But yeah, we’re the only our team is the only one in
49 00:05:45.490 ⇒ 00:05:47.350 Uttam Kumaran: portable and in Snowflake
50 00:05:48.160 ⇒ 00:05:49.910 Uttam Kumaran: Have you used Snowflake before?
51 00:05:49.910 ⇒ 00:05:55.469 Uttam Kumaran: Yes, yes, I’m familiar with Snowflake, but not on the back end. More so like being an end. User.
52 00:05:55.470 ⇒ 00:06:00.740 Uttam Kumaran: Okay. Okay. Well, let me just make sure you’re in. Let me just make sure you’re added here.
53 00:06:01.260 ⇒ 00:06:03.010 Uttam Kumaran: Now.
54 00:06:42.990 ⇒ 00:06:45.879 Uttam Kumaran: okay. And then I’ll just send this to you as well
55 00:06:52.220 ⇒ 00:06:56.830 Annie Yu: So what’s the difference between portable and Snowflake?
56 00:06:58.378 ⇒ 00:07:01.310 Uttam Kumaran: Portable. Just move the data in
57 00:07:01.310 ⇒ 00:07:02.110 Annie Yu: Okay.
58 00:07:02.110 ⇒ 00:07:03.290 Uttam Kumaran: To snowflake.
59 00:07:03.890 ⇒ 00:07:08.110 Uttam Kumaran: Like is the data warehouse, and where we actually run our SQL jobs
60 00:07:08.110 ⇒ 00:07:08.720 Annie Yu: Yeah.
61 00:07:11.110 ⇒ 00:07:16.540 Uttam Kumaran: And let me just share this URL with you.
62 00:07:18.380 ⇒ 00:07:21.429 Uttam Kumaran: So this password is temporary. It’ll tell you to change it
63 00:07:21.880 ⇒ 00:07:22.325 Annie Yu: Hmm.
64 00:07:46.113 ⇒ 00:07:47.760 Uttam Kumaran: Okay? So
65 00:07:48.260 ⇒ 00:08:01.320 Uttam Kumaran: right in snowflake, of course, like, if you go to data and databases, you’re gonna see where all of our data exists. One document that’s probably helpful to look at in our notion is we have a document called
66 00:08:01.864 ⇒ 00:08:05.549 Uttam Kumaran: how we structure. Dbt, are you familiar with Dbt at all?
67 00:08:06.498 ⇒ 00:08:12.059 Annie Yu: Just on the very high level in detail.
68 00:08:12.060 ⇒ 00:08:24.070 Uttam Kumaran: Dbt is basically a software that helps orchestrate. SQL, queries and so we have all of our code for Dbt in our repo. Here are you in Github. By the way.
69 00:08:24.070 ⇒ 00:08:25.760 Annie Yu: Yes, not.
70 00:08:26.360 ⇒ 00:08:32.029 Annie Yu: I think I provided my username, but I haven’t seen an invitation coming up
71 00:08:32.190 ⇒ 00:08:35.297 Uttam Kumaran: Okay, let me just make sure you’re in here now and
72 00:08:36.890 ⇒ 00:08:39.869 Uttam Kumaran: that way. Then I can figure it out later.
73 00:08:50.830 ⇒ 00:08:52.900 Annie Yu: Would you need my username
74 00:08:53.390 ⇒ 00:08:54.180 Uttam Kumaran: Yes.
75 00:08:54.420 ⇒ 00:08:59.849 Annie Yu: Okay, typing it through this zoom, zoom, chat.
76 00:09:19.720 ⇒ 00:09:26.420 Uttam Kumaran: Okay. How do I go out of seat here?
77 00:09:43.700 ⇒ 00:09:49.179 Uttam Kumaran: Okay, so you should. Now you should see an invite here.
78 00:09:50.050 ⇒ 00:10:10.429 Uttam Kumaran: And within this repo. I I think one of the things that we’ll probably try to get everyone to do is maybe go through like the Dbt certification process to get a little bit of understanding of Dbt, but basically, if you go into Dbt project and you go into models, you’ll actually see here all of our intermediate raw and Martz models.
79 00:10:10.490 ⇒ 00:10:21.129 Uttam Kumaran: Basically, this is just how we structure our like modeling environment where we do some transformation at the raw level. Most of it is just bringing data in
80 00:10:21.130 ⇒ 00:10:21.690 Annie Yu: Yeah.
81 00:10:21.690 ⇒ 00:10:27.140 Uttam Kumaran: There’s a lot of business logic at the intermediate level which is combining joins things like that.
82 00:10:29.170 ⇒ 00:10:30.005 Uttam Kumaran: Sorry
83 00:10:31.210 ⇒ 00:10:35.700 Uttam Kumaran: And then we have like, for example, if you go into in recharge payments, or
84 00:10:36.120 ⇒ 00:10:46.870 Uttam Kumaran: let’s see like order lines, you’ll see we’re doing like some logic like case whens joins, flattens things like that. And then finally, in the March models. These are like the models that
85 00:10:47.270 ⇒ 00:10:52.770 Uttam Kumaran: the bi tool and the analysts sort of have access to where it’s like really cleaned up
86 00:10:53.460 ⇒ 00:10:58.399 Uttam Kumaran: everything regarding recharge customers, everything regarding Amazon, kpis,
87 00:10:59.710 ⇒ 00:11:28.880 Uttam Kumaran: And so those will pair one to one with here with the marts intermediate and raw. The reason why there’s prod staging and and dev is that we have different environments. Meaning, if you’re a local developer, you can actually go right and make changes to development, but it will have no impact on the downstream bi tools. Very similarly, when we push a pull request, it will actually run the end to end pipeline and staging that we can see whether anything’s gonna break or not.
88 00:11:29.230 ⇒ 00:11:35.290 Uttam Kumaran: And then finally, the actual Bi tool sits on top of prod marts. And this is really our like production
89 00:11:36.050 ⇒ 00:11:38.130 Uttam Kumaran: tables that are available to query
90 00:11:39.110 ⇒ 00:11:39.790 Annie Yu: Okay.
91 00:11:42.220 ⇒ 00:11:48.520 Uttam Kumaran: So really, like all the core data is here. In prodmarts dot marts
92 00:11:50.480 ⇒ 00:11:52.520 Annie Yu: Of March, March. Okay.
93 00:11:54.430 ⇒ 00:12:00.439 Uttam Kumaran: And then one other item I’ll also share is our bi tool right now is Meta base. Have you used metabase before?
94 00:12:00.440 ⇒ 00:12:03.476 Uttam Kumaran: No, I’m familiar with tableau and looker.
95 00:12:03.910 ⇒ 00:12:07.750 Uttam Kumaran: Yeah, this is like a, this is like a baby version of like both of those so
96 00:12:07.750 ⇒ 00:12:08.079 Annie Yu: Okay.
97 00:12:08.970 ⇒ 00:12:09.543 Annie Yu: Be here.
98 00:12:09.830 ⇒ 00:12:14.629 Uttam Kumaran: Nothing like crazy. I’ll just make sure you’re in here as well
99 00:12:23.290 ⇒ 00:12:32.800 Annie Yu: And one quick question back to the snowflake, will my email be my username? Because I was trying to log in and I can’t
100 00:12:32.800 ⇒ 00:12:39.819 Uttam Kumaran: Your yeah, no, your your username will be just Annie U. All caps all.
101 00:12:40.180 ⇒ 00:12:42.430 Uttam Kumaran: No, no no breaks
102 00:12:42.430 ⇒ 00:12:44.200 Annie Yu: Okay, okay, that makes sense
103 00:13:06.370 ⇒ 00:13:09.150 Uttam Kumaran: Yeah, so just check check that you have access there.
104 00:14:06.980 ⇒ 00:14:09.349 Annie Yu: Yep, I did get it.
105 00:14:15.170 ⇒ 00:14:15.740 Uttam Kumaran: Okay.
106 00:14:17.550 ⇒ 00:14:22.789 Annie Yu: Yeah, just check that you have access to prod marts and that you can see that data. Okay.
107 00:14:22.790 ⇒ 00:14:23.390 Annie Yu: do.
108 00:14:40.410 ⇒ 00:14:43.920 Annie Yu: And what’s are we waiting for something
109 00:14:43.920 ⇒ 00:14:45.610 Uttam Kumaran: No, no, I’m just checking that. You’re
110 00:14:45.610 ⇒ 00:14:46.270 Annie Yu: Okay.
111 00:14:46.270 ⇒ 00:14:50.560 Uttam Kumaran: And everything, and that yes, I just invited you to metabase as well. Maybe you can see.
112 00:14:50.560 ⇒ 00:14:51.409 Uttam Kumaran: Check that out
113 00:14:51.650 ⇒ 00:14:52.540 Annie Yu: All right.
114 00:14:53.010 ⇒ 00:14:57.410 Uttam Kumaran: And I’m just gonna also I’ll send you the Github link so that you can confirm that you’re in there
115 00:14:57.980 ⇒ 00:14:58.840 Annie Yu: Hey?
116 00:15:05.726 ⇒ 00:15:13.410 Annie Yu: Yeah, I believe I’m in. I’m just clicking on a random dashboard, and I think it’s loading correctly.
117 00:15:13.410 ⇒ 00:15:14.160 Uttam Kumaran: Okay.
118 00:15:16.000 ⇒ 00:15:24.489 Uttam Kumaran: So really, I expect your home to be Meta base and like running queries in Snowflake.
119 00:15:25.250 ⇒ 00:15:40.453 Uttam Kumaran: and we’ll work to make sure that everyone sort of is trained on Dbt, and but that’ll take some time like don’t worry too much about getting to know that immediately. I think really our, our short term, immediate need that we’re we’ll probably talk about today.
120 00:15:41.420 ⇒ 00:15:48.960 Uttam Kumaran: is just like what work that needs to be done for for Javi in terms of new dashboards.
121 00:15:49.020 ⇒ 00:16:14.579 Uttam Kumaran: So one of the things that we’re working with the team on is basically creating dashboards across their core selling platforms, which is Amazon tick, tock, shopify and if you go here, and and one of the things also that could be helpful to work on is just like organization here, like, I don’t know. I guess there’s a key dashboards piece here. But like, basically, for example, we have a gross margin dashboard. This is like
122 00:16:14.700 ⇒ 00:16:21.540 Uttam Kumaran: the primary deliverable that we worked with them on. And basically
123 00:16:22.101 ⇒ 00:16:35.480 Uttam Kumaran: this just shows everything regarding their gross margin so gross margin overall how much money they’re bringing in new versus returning revenue new versus returning Aov gross profit per offer.
124 00:16:36.122 ⇒ 00:16:39.500 Uttam Kumaran: And just like everything around gross profit. Basically.
125 00:16:40.030 ⇒ 00:16:48.430 Uttam Kumaran: of course, like, yeah, like high level, it’s basically everything around high level revenue. And then the the definition for gross profit is
126 00:16:48.720 ⇒ 00:16:49.680 Uttam Kumaran: is here
127 00:16:50.110 ⇒ 00:16:52.870 Annie Yu: Okay, it’s noted. Noted. There, cool.
128 00:16:52.870 ⇒ 00:17:03.570 Uttam Kumaran: So I think, really spending today to get to like, understand the existing dashboards that I that are there would be really, really helpful and familiarizing yourself with Meta base.
129 00:17:04.014 ⇒ 00:17:18.329 Uttam Kumaran: I think if you can spend any time on that, that’d be helpful. I’m gonna I’m gonna double check on when their meeting is for Javi in order to talk as like a team. But that’s really the core pieces right now is one
130 00:17:18.460 ⇒ 00:17:27.499 Uttam Kumaran: most likely this to the maintenance, and the, you know, reporting off of this dashboard will be handed over to you. Robert is really the one owning that now.
131 00:17:27.993 ⇒ 00:17:36.210 Uttam Kumaran: The other piece. Is that we have a couple of more dashboards coming down the pipeline for Tiktok shop.
132 00:17:36.968 ⇒ 00:17:42.279 Uttam Kumaran: Also, for have you heard of? Attentive. It’s like an SMS marketing tool.
133 00:17:43.110 ⇒ 00:17:43.860 Annie Yu: No.
134 00:17:43.860 ⇒ 00:17:56.439 Uttam Kumaran: Okay. But basically like, it’s 1 of those tools where you can send offers via text, and you can like, basically, you do like SMS related marketing. And there’s another thing for Klaviyo Klaviyo is like email based marketing
135 00:17:56.440 ⇒ 00:17:59.089 Annie Yu: So is it kind of like Martech tool
136 00:17:59.090 ⇒ 00:18:00.510 Uttam Kumaran: Yeah, yeah, basically.
137 00:18:01.010 ⇒ 00:18:08.910 Uttam Kumaran: But ultimately, what comes out of that is like, customer receives a notification. They then go and purchase. You want to measure conversion rates
138 00:18:08.910 ⇒ 00:18:09.510 Annie Yu: Yeah, yeah.
139 00:18:09.510 ⇒ 00:18:10.987 Uttam Kumaran: That’s literally it.
140 00:18:11.690 ⇒ 00:18:26.979 Uttam Kumaran: so those are coming down the pipeline so ideally any new dashboard requests or like analysis requests again. It’ll maybe it’ll be up to you if it’s a dashboard or a more. One time, report will sort of come through you ideally that lives in Meta base
141 00:18:27.395 ⇒ 00:18:46.289 Uttam Kumaran: and so taking us, taking maybe a second to just go learn creating like one sample dashboard. And Meta base understanding like the features it has, as you can tell can do everything. It’s just the way they, the way it’s structured. You have to create this thing called questions. And then the questions are basically tiles
142 00:18:46.610 ⇒ 00:18:53.700 Annie Yu: But I would say, it’s it’s very similar to to anything that you’ve you’ve worked on. So if you click on new in the top right? You can click on new
143 00:18:54.368 ⇒ 00:19:10.120 Uttam Kumaran: Question. And then you can actually select from the table. So we have right here fraud marts, snowflake and you can actually just say, Okay, cool. I want to go from. I want to select from fax shipments. You’ll see that they’re like, you basically can pick all the different
144 00:19:10.910 ⇒ 00:19:25.309 Uttam Kumaran: you can pick any column you want to bring in here, and then you can do aggregations within here filters, and then you visualize, and it gives you one tile save that, and then it’s like similar to look, or where you save
145 00:19:26.210 ⇒ 00:19:28.129 Uttam Kumaran: what is it called? Like a
146 00:19:29.620 ⇒ 00:19:32.619 Uttam Kumaran: what are the individual blocks called in Looker. Do you remember
147 00:19:33.020 ⇒ 00:19:33.810 Annie Yu: And where.
148 00:19:33.810 ⇒ 00:19:34.890 Uttam Kumaran: Looker.
149 00:19:36.540 ⇒ 00:19:38.756 Uttam Kumaran: It’s like tiles, or like
150 00:19:39.200 ⇒ 00:19:42.850 Annie Yu: I know that in tablets, dimension, and measure, and then
151 00:19:42.850 ⇒ 00:19:45.820 Uttam Kumaran: Like in tab in tableau. You have sheets, and then you have the dashboard right
152 00:19:45.820 ⇒ 00:19:47.010 Annie Yu: Yeah, yeah.
153 00:19:47.010 ⇒ 00:19:49.789 Uttam Kumaran: In Looker. I forgot. It’s like you can save an individual thing
154 00:19:50.590 ⇒ 00:19:53.639 Uttam Kumaran: And then you can add them all to a dashboard. It’s very similar
155 00:19:53.640 ⇒ 00:19:54.950 Annie Yu: Yeah, okay.
156 00:19:57.140 ⇒ 00:20:10.629 Uttam Kumaran: So that’s probably like most of the stuff. I think you’ll have a lot of questions going through. We’re we’re sort of starting to work on a lot of documentation as well for this client. But I’ll maybe I’ll sort of leave you with that, and can spend today poking around
157 00:20:11.060 ⇒ 00:20:19.040 Uttam Kumaran: there and and understanding like what data is available and asking questions. I think that would be probably the most productive.
158 00:20:19.310 ⇒ 00:20:26.589 Uttam Kumaran: This week I’m spending, because I know I’m I’m off of a lot of like day to day work. I’m spending a lot of my time on documentation. So
159 00:20:26.590 ⇒ 00:20:26.910 Annie Yu: The.
160 00:20:28.030 ⇒ 00:20:31.719 Uttam Kumaran: There will be a lot of basic questions. I’m happy to answer anything about this client.
161 00:20:32.278 ⇒ 00:20:41.600 Uttam Kumaran: But ideally, yeah, most of your work will happen in just running running queries directly in in snowflake like, directly in a worksheet.
162 00:20:43.360 ⇒ 00:20:46.610 Uttam Kumaran: And then also in in metabase.
163 00:20:47.023 ⇒ 00:20:49.596 Uttam Kumaran: And then I think the big thing for today? I asked.
164 00:20:49.970 ⇒ 00:20:58.120 Uttam Kumaran: I asked Robert to to give me a sense of like what the what the core issues are coming up for
165 00:20:58.796 ⇒ 00:21:00.383 Uttam Kumaran: javi, are you in
166 00:21:00.940 ⇒ 00:21:05.759 Uttam Kumaran: Are you in linear? By the way, I just sent you an invite there, too.
167 00:21:06.700 ⇒ 00:21:07.790 Annie Yu: And which.
168 00:21:08.250 ⇒ 00:21:10.869 Uttam Kumaran: It’s called linear. It’s our project management software
169 00:21:12.350 ⇒ 00:21:13.030 Annie Yu: Let me just
170 00:21:13.030 ⇒ 00:21:17.210 Uttam Kumaran: See if you could open. Yeah, see if you could open it up on your end. And I can just maybe show you around
171 00:21:24.640 ⇒ 00:21:25.970 Annie Yu: Going?
172 00:21:36.240 ⇒ 00:21:37.400 Annie Yu: Yeah.
173 00:21:43.790 ⇒ 00:21:44.990 Annie Yu: yeah, I’m in
174 00:21:45.380 ⇒ 00:21:49.430 Uttam Kumaran: Okay, do you want to share your screen?
175 00:21:49.430 ⇒ 00:21:50.010 Annie Yu: Sure.
176 00:21:50.010 ⇒ 00:21:54.469 Uttam Kumaran: And I’ll just maybe show you where to go, because it may look different on mine.
177 00:21:55.940 ⇒ 00:21:58.150 Annie Yu: And let me see.
178 00:22:06.040 ⇒ 00:22:10.779 Annie Yu: I’m like learning how to use
179 00:22:10.780 ⇒ 00:22:11.920 Uttam Kumaran: No no problem.
180 00:22:12.383 ⇒ 00:22:13.310 Annie Yu: Wait. I
181 00:22:13.310 ⇒ 00:22:15.209 Uttam Kumaran: On the left side you should see
182 00:22:15.210 ⇒ 00:22:17.749 Annie Yu: Alright privacy. Okay.
183 00:22:17.900 ⇒ 00:22:20.670 Uttam Kumaran: Oh, it may have you! It may have you quit out and come back
184 00:22:21.260 ⇒ 00:22:24.349 Annie Yu: Oh, yeah, I’m gonna do that. I’ll be back
185 00:22:24.350 ⇒ 00:22:24.970 Uttam Kumaran: Okay.
186 00:22:45.440 ⇒ 00:22:46.150 Annie Yu: Okay?
187 00:22:46.300 ⇒ 00:22:48.850 Annie Yu: And then my screen.
188 00:22:51.020 ⇒ 00:22:52.070 Annie Yu: This one.
189 00:22:59.000 ⇒ 00:23:02.180 Annie Yu: Why not that here
190 00:23:08.060 ⇒ 00:23:11.659 Uttam Kumaran: Okay. Great. Yep. So these are all the active tickets on
191 00:23:12.110 ⇒ 00:23:32.880 Uttam Kumaran: on Javi right now. As you as you can see, I I don’t know. I’m I’m sort of interested to ask you to like, how was the project management process at the places you worked before, but kind of the one thing that Robert is the product owner on this client meaning he’s the voice of the client, like
192 00:23:33.100 ⇒ 00:23:42.489 Uttam Kumaran: basically like, whatever ask they have. He translates to our team, and it’s up to us to sort of break it down into technically what needs to happen. So what I basically said is like.
193 00:23:42.820 ⇒ 00:23:48.200 Uttam Kumaran: I just want you to list out like what the next questions that we have to solve for the customers are.
194 00:23:48.360 ⇒ 00:23:52.469 Uttam Kumaran: And really these are all the tasks that are there right now?
195 00:23:53.224 ⇒ 00:23:57.890 Uttam Kumaran: For example, like, How do email and SMS impact sales conversions?
196 00:23:58.730 ⇒ 00:24:05.840 Uttam Kumaran: So that’s something where, like, I do, you want to click on that I don’t know even know whether there is additional context.
197 00:24:05.950 ⇒ 00:24:08.820 Uttam Kumaran: But basically, this is sort of what we have
198 00:24:09.030 ⇒ 00:24:13.110 Annie Yu: So it’s for up for us to kind of think about. Okay, what data do we need
199 00:24:13.260 ⇒ 00:24:27.759 Uttam Kumaran: Right. And so I think the best thing to do here is one. Today we’ll sort of talk a little bit about who who’s owning which task, and then, ideally, you can take one and say, Okay, cool. What? In order to answer this, like what metrics and dimensions do I need
200 00:24:28.540 ⇒ 00:24:35.090 Uttam Kumaran: And then that’s what you can pass to the analytics engineering team to then say, Make go, make this table happen for me
201 00:24:35.290 ⇒ 00:24:36.140 Uttam Kumaran: right
202 00:24:36.140 ⇒ 00:24:42.609 Annie Yu: Oh, okay, so so the engineer will be the one to kind of
203 00:24:42.940 ⇒ 00:24:46.409 Annie Yu: get that table ready for visualization. Is that
204 00:24:46.410 ⇒ 00:24:49.990 Uttam Kumaran: So so it’s actually like 2 things. One is like, I think
205 00:24:50.680 ⇒ 00:25:15.840 Uttam Kumaran: there’s people everybody in the team, I think, should be able to handle all of these tasks right? But ultimately we each have like specializations. So of course, like, we have a process in Dbt to like, select to select data to make it available in Prodmart. But you can actually probably do a lot of data exploration before that process. Right? So if I have Klavio data, which is our email data. And that’s sitting in the raw database.
206 00:25:15.960 ⇒ 00:25:18.829 Uttam Kumaran: I’m sure if you’re like, okay, let me go look at what’s in there.
207 00:25:19.060 ⇒ 00:25:26.330 Uttam Kumaran: And then I can let the the analytics engineering team know. Hey, I need these like 10 row. I need these 10 columns
208 00:25:26.490 ⇒ 00:25:29.351 Uttam Kumaran: to make it into some sort of final table
209 00:25:30.050 ⇒ 00:25:32.880 Uttam Kumaran: And if you can provide some of the specifications, it will make
210 00:25:33.350 ⇒ 00:25:47.819 Uttam Kumaran: the Ae’s life super easy to go build that because you have the ultimate context of the business problem that you’re solving. The analytics. Engineers have to take that context, make sure the logic and the speed of the table operates, and that it’s clean
211 00:25:48.377 ⇒ 00:25:52.422 Uttam Kumaran: and then there’s testing, and then they’ll make it available for you.
212 00:25:53.230 ⇒ 00:25:58.830 Uttam Kumaran: is that sort of how it’s worked in the past or in the past, has all the data sort of been available
213 00:25:59.960 ⇒ 00:26:08.499 Annie Yu: No I would say, back in like it was really messy. So some of the extra metrics were created
214 00:26:08.880 ⇒ 00:26:14.879 Annie Yu: in Snowflake, and some of them are just created in tableau. So that’s also
215 00:26:14.880 ⇒ 00:26:15.600 Uttam Kumaran: Oh, okay.
216 00:26:15.600 ⇒ 00:26:22.130 Annie Yu: Wanted to bring up to you, do we always, I guess, strive to create all the metrics in the
217 00:26:22.130 ⇒ 00:26:22.570 Uttam Kumaran: Yes.
218 00:26:22.570 ⇒ 00:26:24.000 Annie Yu: Data tables. Okay.
219 00:26:24.000 ⇒ 00:26:29.350 Uttam Kumaran: Yes, this is a great point. And of course, this is a very classic data team problem where
220 00:26:30.120 ⇒ 00:26:41.679 Uttam Kumaran: the and like the analysts. But also, it’s a healthy back and forth like the analyst team may want to move fast, so they create the metric wherever. But of course you can never maintain that logic, and you can’t reuse that logic.
221 00:26:41.800 ⇒ 00:26:51.999 Uttam Kumaran: And then it doesn’t go through testing so ultimately as if we could. I want to centralize 100 of the logic and the repo on any given moment. Maybe that’s like 80 to 100
222 00:26:52.360 ⇒ 00:26:52.720 Annie Yu: Yes.
223 00:26:52.720 ⇒ 00:26:57.709 Uttam Kumaran: You may be working on a on a dashboard. And you’re like, Well, I need this case when I’m just gonna build it.
224 00:26:57.840 ⇒ 00:27:15.759 Uttam Kumaran: But then your job is to once it works to tell the Ae team, hey, make the go, make this logic available. The alternate is to be like you have to wait. But then I don’t want the client to. I don’t want you to spend 3 days waiting for something. It’s like a 1 line, you know, issue so
225 00:27:18.070 ⇒ 00:27:18.899 Annie Yu: Okay, that makes
226 00:27:18.900 ⇒ 00:27:20.110 Uttam Kumaran: Yeah, yeah.
227 00:27:20.820 ⇒ 00:27:32.299 Uttam Kumaran: So I think for I I think what I think this this would be what we spend time I think today talking about, and I’m gonna just double check in the Channel on. If there’s a preference of what to work on. First, st
228 00:27:32.410 ⇒ 00:27:36.559 Uttam Kumaran: the other pieces at the bottom here, which is like client update
229 00:27:37.460 ⇒ 00:27:45.660 Uttam Kumaran: Amazon dashboard weekly updates. These are some like small updates to the, to the existing dashboards that I think
230 00:27:46.310 ⇒ 00:27:55.479 Uttam Kumaran: that that, the client requested. I’m gonna just send a message to understand which ones that are the priority and which ones that maybe can get handed off to you.
231 00:27:57.030 ⇒ 00:28:00.870 Uttam Kumaran: But this is really all the scope of work that we have right now for for Joby
232 00:28:01.500 ⇒ 00:28:03.349 Annie Yu: Got it, and
233 00:28:03.520 ⇒ 00:28:15.599 Annie Yu: I know that. You mentioned that Robert is the one that communicates with the team, the client team. So who? What’s the process of these questions
234 00:28:15.930 ⇒ 00:28:21.220 Annie Yu: coming up? Is it like discussion through Robert and an analyst
235 00:28:23.070 ⇒ 00:28:41.770 Uttam Kumaran: Yeah. So so right now, we have a couple of people are on the team. So we have a, we have a product owner. We have a project manager. And then we, of course, we have the development team. So the project manager is Steven. Steven is basically making sure that everything is up to date here. He’s getting updates from everybody on whether things are done.
236 00:28:42.150 ⇒ 00:29:01.749 Uttam Kumaran: Robert is really working directly with the client to figure out what’s next and taking our work. And then basically like coming alongside you and presenting right? So ultimately like, it would be great if Robert is actually somebody on the client. Because then we’re working like directly with them. But for now it’s it’s mainly Robert. So his job is just to build a backlog
237 00:29:02.370 ⇒ 00:29:10.769 Uttam Kumaran: Work with Steven to make sure that those get prioritized. And those have all the information that you guys need. So that’s sort of the 3 legged stool for this client
238 00:29:11.200 ⇒ 00:29:12.630 Annie Yu: Okay, got it?
239 00:29:13.214 ⇒ 00:29:23.859 Annie Yu: And one more question is, let’s say, if I wanna do this question. And so, where can I find this data is, is it also in snowflake
240 00:29:26.560 ⇒ 00:29:27.910 Uttam Kumaran: Which which one
241 00:29:28.462 ⇒ 00:29:29.990 Annie Yu: Like the email, one
242 00:29:29.990 ⇒ 00:29:48.762 Uttam Kumaran: Yeah. So this is a good thing is like to start. I think one of the things that you’ll need to figure out is like, is this data available so ideally? Yes, it’s in raw right now. I know it’s not there because we have. The team hasn’t worked on ingesting it yet. So one of the one of the things for this question, and I’m gonna tell. So if you go to raw, yeah, under
243 00:29:49.120 ⇒ 00:29:52.509 Uttam Kumaran: under portable underscore, you’ll see all these sources.
244 00:29:52.960 ⇒ 00:30:03.929 Uttam Kumaran: So one of the things that I’m gonna explain to Steven is that for anytime there’s a new source we need to work on tickets that relate to ingestion. So I’m gonna create a ticket today. That’s like Ingest
245 00:30:04.150 ⇒ 00:30:11.540 Uttam Kumaran: Klavio data into snowflake. But that’ll be like a sub ticket underneath. This task
246 00:30:11.700 ⇒ 00:30:14.999 Annie Yu: Yeah, yeah, I, yeah. I still got lots to explore. Here.
247 00:30:15.000 ⇒ 00:30:16.500 Uttam Kumaran: Yeah. Yeah. Totally.
248 00:30:17.480 ⇒ 00:30:20.564 Annie Yu: Okay, that will make sense.
249 00:30:26.220 ⇒ 00:30:30.690 Annie Yu: okay, so I guess my today’s focus will be
250 00:30:30.830 ⇒ 00:30:37.119 Annie Yu: exploring the data in Snowflake and then try just play around with the Meta Base
251 00:30:39.530 ⇒ 00:30:40.170 Uttam Kumaran: Correct.
252 00:30:41.328 ⇒ 00:30:50.989 Annie Yu: Can you help me understand again? For this Mars, you said, this is where for, like Bi Uses.
253 00:30:51.110 ⇒ 00:30:56.550 Annie Yu: so in what case will I come here and create something
254 00:30:57.400 ⇒ 00:31:02.989 Uttam Kumaran: Yeah. So so I guess it depends on your use case. If you’re gonna be creating tables
255 00:31:03.450 ⇒ 00:31:11.699 Uttam Kumaran: Then all anything that gets created in production needs to go through. Dbt, but you’re you can feel free to go create anything in the development
256 00:31:12.010 ⇒ 00:31:17.230 Uttam Kumaran: schema or in I can create for you like, you see, there’s Dev Marts
257 00:31:17.940 ⇒ 00:31:32.659 Uttam Kumaran: So you can feel free to create something in dev Marts or create something in a playground schema. But I guess, like I need to understand the use cases right now. We don’t have anybody outside. We don’t have anybody creating tables like on the fly. Everything goes through
258 00:31:32.900 ⇒ 00:31:36.479 Uttam Kumaran: creating it in Dvt, and then basically pushing a Pr
259 00:31:37.850 ⇒ 00:31:40.040 Uttam Kumaran: but if you let me know your use case.
260 00:31:40.330 ⇒ 00:31:46.249 Uttam Kumaran: then, like we can create a playground area where you can explore and create tables.
261 00:31:47.580 ⇒ 00:31:52.749 Uttam Kumaran: or I can show you actually like, if you want to create a table through Dbt process, you can push a Pr
262 00:31:53.359 ⇒ 00:31:57.430 Annie Yu: Okay, I, yeah, I think I I do need spend some time
263 00:31:57.430 ⇒ 00:31:58.300 Uttam Kumaran: Spend some time
264 00:31:58.300 ⇒ 00:31:58.640 Annie Yu: Familiar.
265 00:31:58.640 ⇒ 00:32:01.849 Uttam Kumaran: Ask. Ask any question in the data team channel
266 00:32:02.020 ⇒ 00:32:04.860 Uttam Kumaran: or in in the Java Channel. It’ll get answered
267 00:32:05.410 ⇒ 00:32:06.520 Annie Yu: Okay. Okay.
268 00:32:06.770 ⇒ 00:32:07.320 Uttam Kumaran: Okay.
269 00:32:12.662 ⇒ 00:32:27.920 Annie Yu: And speaking of data team I spoke with Marian about the recurring meetings. And she did mention that for data data team meetings. You are the one to send out the invites
270 00:32:28.290 ⇒ 00:32:38.679 Uttam Kumaran: Yeah. So that’s actually changing. I don’t think I’ve mentioned to Marianne, but the Pm’s are starting to own. So actually, you just should see that that Steven just sent a daily
271 00:32:38.680 ⇒ 00:32:39.050 Annie Yu: Please stand.
272 00:32:39.050 ⇒ 00:32:41.029 Uttam Kumaran: Stand up starting tomorrow.
273 00:32:41.718 ⇒ 00:32:49.429 Uttam Kumaran: I think probably the one thing to work with him on is like, what’s the best time for that. I know that’s pretty early. Your guys time. Steven’s also in La
274 00:32:49.860 ⇒ 00:32:52.580 Annie Yu: Oh, yeah, he he did. He did ask us
275 00:32:52.580 ⇒ 00:32:53.760 Uttam Kumaran: Okay. Okay, okay?
276 00:32:54.536 ⇒ 00:33:05.279 Uttam Kumaran: Cause that’s when I had it. And I was like, but then I know Kyle and some people are Europe time. So I think that’s fine. But that’ll be the meeting where we go through everything
277 00:33:05.410 ⇒ 00:33:06.559 Uttam Kumaran: on the board
278 00:33:07.960 ⇒ 00:33:10.490 Annie Yu: Okay? And I do have one more question.
279 00:33:10.490 ⇒ 00:33:11.010 Uttam Kumaran: There!
280 00:33:11.010 ⇒ 00:33:16.850 Annie Yu: Might, I might have missed this. You said that Robert was sharing. What’s the core issue
281 00:33:17.050 ⇒ 00:33:21.550 Annie Yu: with the Joby team? And I can you help me
282 00:33:21.550 ⇒ 00:33:31.900 Uttam Kumaran: Yeah, I guess what I was saying is that Robert owns the the creation of the backlog, and all of that is in linear right now, I think the one thing is a lot of those tickets don’t have many details
283 00:33:31.900 ⇒ 00:33:32.410 Annie Yu: Yeah, yeah.
284 00:33:32.410 ⇒ 00:33:36.750 Uttam Kumaran: One question is, as you look into those tickets, feel free to ask, follow up questions
285 00:33:37.640 ⇒ 00:33:40.649 Uttam Kumaran: And that way he can go. He can answer those
286 00:33:40.950 ⇒ 00:33:42.689 Annie Yu: That sounds good. Okay.
287 00:33:42.880 ⇒ 00:33:48.380 Annie Yu: I will do that. And this is great. I appreciate this session cause I I was
288 00:33:48.380 ⇒ 00:33:49.040 Uttam Kumaran: Yeah.
289 00:33:49.040 ⇒ 00:33:52.359 Annie Yu: I wanted to make sure I prioritize my time right. But then
290 00:33:52.360 ⇒ 00:34:11.759 Uttam Kumaran: No, no, this is perfect, and you know, a lot of stuff we have is not documented. Well, that’s actually like my new job, cause. I’m getting out of a lot of the day to day. So I’m I’m going client by client, by client, and documenting, building up like for all of our developers everything you need to know about every part of our stack.
291 00:34:12.010 ⇒ 00:34:20.809 Uttam Kumaran: So I’m gonna be working on that so things will get easier. But feel free like. I want this to be an air a time where, like everybody, needs to ask more questions
292 00:34:20.810 ⇒ 00:34:21.300 Annie Yu: Like.
293 00:34:21.300 ⇒ 00:34:27.060 Uttam Kumaran: Don’t wait for a meeting. Ask the question. Talk in slack like. That’s the only way we’re all gonna learn together.
294 00:34:27.524 ⇒ 00:34:31.099 Uttam Kumaran: So feel free like there’s no bad questions at all. So
295 00:34:31.449 ⇒ 00:34:35.739 Annie Yu: Okay, awesome. And is there a data meeting later?
296 00:34:36.409 ⇒ 00:34:47.189 Uttam Kumaran: So right. So it I think that this is our only meeting between me and you today. I’m happy to hop on later today. If you’d like. I think tomorrow we’re gonna start with the Javi stand ups
297 00:34:47.190 ⇒ 00:34:47.570 Annie Yu: Yeah.
298 00:34:47.570 ⇒ 00:34:54.798 Uttam Kumaran: I I wanna I’m gonna start to host like a data team only meeting where it’s like all the engineers.
299 00:34:55.280 ⇒ 00:35:14.710 Uttam Kumaran: I haven’t decided on when that is because I sort of just like cleared my schedule with a lot of stuff. I want to make sure that everybody is assigned to a client and is working well. And then I’m gonna start to talk a little bit more about our data platform, how we do data here, things like that. So that’ll be. That’ll be what I’m working on this week.
300 00:35:14.870 ⇒ 00:35:16.770 Annie Yu: All right. That’ll be awesome.
301 00:35:16.770 ⇒ 00:35:26.240 Uttam Kumaran: Yeah. And yeah, let me know any questions. I think, yeah, I think you’ll get the hang of Meta base really quickly, I think, really what’s probably going to be. The challenge is like, what’s in Snowflake. What’s not in Snowflake
302 00:35:26.880 ⇒ 00:35:29.900 Annie Yu: Yeah, that’s something I will work on, too.
303 00:35:29.900 ⇒ 00:35:32.350 Uttam Kumaran: It’s clean, I will say it’s pretty clean like
304 00:35:32.350 ⇒ 00:35:33.370 Annie Yu: That’s amazing.
305 00:35:33.370 ⇒ 00:35:37.390 Uttam Kumaran: The tables are are named properly, we have these environments.
306 00:35:37.810 ⇒ 00:35:47.729 Uttam Kumaran: So we’re getting that we’re getting. I think the one thing that isn’t there is like what like document, for example, like, I should be able to point you to a document to look at what’s in Snowflake right now. We don’t have that.
307 00:35:47.960 ⇒ 00:35:50.960 Uttam Kumaran: But like we’re getting better at that. So
308 00:35:50.960 ⇒ 00:36:02.960 Annie Yu: Yeah, that sounds good. And I I do have one small note, too, the friendly reminder. I I’ve been seeing people talking about time, and people are still using standard time. And I
309 00:36:02.960 ⇒ 00:36:06.960 Uttam Kumaran: Oh, oh, really, yeah.
310 00:36:07.690 ⇒ 00:36:21.839 Uttam Kumaran: Oh, you should remind people. Because, yeah, I just look at the clock. I don’t even know I don’t. Yeah, maybe some people are. Wait. Maybe it’s in Europe. Some people are in Europe and they translate it. I don’t know. But yeah, if you see, people are saying the wrong time, just just ping them
311 00:36:21.840 ⇒ 00:36:26.590 Annie Yu: I usually just skip the the middle one. I just do like CTPT.
312 00:36:26.590 ⇒ 00:36:29.250 Uttam Kumaran: Yeah, yeah. I mean, that’s exactly what I do, too.
313 00:36:29.250 ⇒ 00:36:29.694 Annie Yu: Yeah.
314 00:36:31.390 ⇒ 00:36:41.169 Annie Yu: okay, awesome. Thank you so much. And I might or might not hit you up again. But we will surely talk tomorrow first.st
315 00:36:41.170 ⇒ 00:36:41.840 Uttam Kumaran: Okay.
316 00:36:42.515 ⇒ 00:36:43.190 Annie Yu: Okay.
317 00:36:43.190 ⇒ 00:36:44.950 Uttam Kumaran: Cool, alright. Thank you.
318 00:36:44.950 ⇒ 00:36:47.139 Annie Yu: Thank you. Have a good day.
319 00:36:47.140 ⇒ 00:36:47.680 Uttam Kumaran: Bye.