Meeting Title: Brainforge x Ellie Onboarding Sync Date: 2025-08-26 Meeting participants: Awaish Kumar, Shreya Chowdhury
WEBVTT
1 00:00:38.380 ⇒ 00:00:42.630 Awaish Kumar: Can you go into the….
2 00:00:50.360 ⇒ 00:00:51.739 Shreya Chowdhury: Hey, how’s it going?
3 00:00:53.580 ⇒ 00:00:55.010 Awaish Kumar: Good, how about you?
4 00:00:55.480 ⇒ 00:00:56.690 Shreya Chowdhury: Good, good.
5 00:01:00.350 ⇒ 00:01:05.409 Awaish Kumar: Hey. So, how has been your time so far at Bradford?
6 00:01:06.110 ⇒ 00:01:25.069 Shreya Chowdhury: Not too bad. Yesterday was mostly just in a lot of meetings, so I’m just still trying to get my lay of the land here, and I already started working on some stuff for Ellie, so I’m just trying to get that ready by my check-in with Robert today for the client.
7 00:01:27.600 ⇒ 00:01:33.360 Awaish Kumar: Judy started with some… Work-related meetings, or is it just, like, one-on-one?
8 00:01:34.250 ⇒ 00:01:53.939 Shreya Chowdhury: Both, so it was a lot of onboarding, and then I’m working on a data tracking plan to get Ellie, like, so they can have a proper data foundation, and then, I’ll have to ask Robert. I’m not sure who will be in charge of implementing the tracking, if that will be us, or if that will be, …
9 00:01:54.410 ⇒ 00:01:56.240 Shreya Chowdhury: Like, someone on their team.
10 00:01:57.810 ⇒ 00:02:00.849 Awaish Kumar: Okay, yeah, that’s for all my thoughts, sir.
11 00:02:00.980 ⇒ 00:02:03.899 Awaish Kumar: Okay, yeah, for this meeting, I don’t have any…
12 00:02:04.390 ⇒ 00:02:17.730 Awaish Kumar: my first agenda is just one-on-one to know each other and, understand your past experiences and what you have been doing so far. Maybe tell us something more… some… few things about me as well.
13 00:02:18.660 ⇒ 00:02:19.310 Shreya Chowdhury: Yeah.
14 00:02:19.890 ⇒ 00:02:27.329 Awaish Kumar: How has the, like… like, past work experiences.
15 00:02:27.740 ⇒ 00:02:42.709 Shreya Chowdhury: Yeah, so I spent the last 4 years working at Shopify, and I worked as a product data scientist there. So my work there was very full stack, so it was a lot of,
16 00:02:42.720 ⇒ 00:02:50.900 Shreya Chowdhury: data analyst work, data engineering work, and then anything else in between. So, like, whatever they needed data-wise.
17 00:02:51.280 ⇒ 00:02:55.900 Shreya Chowdhury: And it was fun. I was there for three and a half years, ….
18 00:02:56.160 ⇒ 00:02:56.510 Awaish Kumar: I….
19 00:02:57.090 ⇒ 00:03:02.889 Shreya Chowdhury: Yeah, until June of this month, so a couple months ago.
20 00:03:02.890 ⇒ 00:03:07.380 Awaish Kumar: There’s not… doesn’t Shopify have all different streams, like…
21 00:03:07.520 ⇒ 00:03:11.400 Awaish Kumar: Data analytics engineers, data engineers, data analysts.
22 00:03:11.400 ⇒ 00:03:12.410 Shreya Chowdhury: Yeah, so they….
23 00:03:12.410 ⇒ 00:03:13.060 Awaish Kumar: Cross.
24 00:03:13.320 ⇒ 00:03:32.159 Shreya Chowdhury: Yeah, so they have all different streams. That happened after I joined, so when I initially joined, it was like, you’re gonna be doing a little bit of everything. And then, even after they broke up those streams, like, the product data analy… like, or the product data scientist work was very, like, it was…
25 00:03:32.540 ⇒ 00:03:35.490 Shreya Chowdhury: Sort of analytics, but, like.
26 00:03:36.250 ⇒ 00:03:41.479 Shreya Chowdhury: you just did a little bit of what everybody else did. So that’s kind of how it was split up.
27 00:03:42.090 ⇒ 00:03:49.269 Shreya Chowdhury: It was the least… I think, as far as, like, we had data analysts, we had, …
28 00:03:49.590 ⇒ 00:03:54.339 Shreya Chowdhury: Or we had business analysts, and we also had data engineers, analytics engineers.
29 00:03:54.450 ⇒ 00:04:05.799 Shreya Chowdhury: For a product data scientists, we didn’t do any analytics engineering work, but we were the least specific craft, so it’s like we did a little bit of what everybody else was doing.
30 00:04:07.970 ⇒ 00:04:08.590 Awaish Kumar: Perfect.
31 00:04:08.910 ⇒ 00:04:09.680 Shreya Chowdhury: Yeah.
32 00:04:10.070 ⇒ 00:04:16.420 Awaish Kumar: As a product data analytic, data scientist, like, thank you.
33 00:04:16.880 ⇒ 00:04:25.430 Awaish Kumar: What exactly, … Like, we’re responsible for setting up goes, like, complete to GFR,
34 00:04:25.900 ⇒ 00:04:29.099 Awaish Kumar: Mixed panels, segment, or things like that, or….
35 00:04:30.260 ⇒ 00:04:30.940 Shreya Chowdhury: That one….
36 00:04:30.940 ⇒ 00:04:33.789 Awaish Kumar: I was more likely implementing Tableau model here.
37 00:04:34.840 ⇒ 00:04:39.910 Shreya Chowdhury: Yeah, so I have less experience doing that. It was more like…
38 00:04:40.810 ⇒ 00:04:54.190 Shreya Chowdhury: So if we’re thinking about launching a new feature or implementing something new, I would be in charge of, like, exploring if that would be a good decision, and then look into the data there and see, like, how will this impact
39 00:04:54.190 ⇒ 00:05:08.769 Shreya Chowdhury: some of our North Star metrics. So that was, like, it would be things like that. Sometimes it would be, like, after we launched a feature, it would be, like, oh, let’s analyze, like, what the impact of this was, if it was good or bad or whatever.
40 00:05:08.820 ⇒ 00:05:09.530 Shreya Chowdhury: ….
41 00:05:09.530 ⇒ 00:05:10.110 Awaish Kumar: Okay.
42 00:05:10.650 ⇒ 00:05:19.459 Shreya Chowdhury: So, there was that. There was, sometimes we would… I would set up A-B tests for experiments, …
43 00:05:20.050 ⇒ 00:05:25.299 Shreya Chowdhury: And then, other than that, I would be building a lot of data pipelines and data modeling work in dbt.
44 00:05:26.170 ⇒ 00:05:32.870 Awaish Kumar: So it was mainly a… Oh, it was mainly, like, analyzing the product features, or…
45 00:05:33.000 ⇒ 00:05:35.359 Awaish Kumar: If you have a new feature, how…
46 00:05:35.480 ⇒ 00:05:42.850 Awaish Kumar: or a different page, like, for example, for A-B testing, how that impacts the conversion, all that.
47 00:05:42.850 ⇒ 00:05:43.790 Shreya Chowdhury: Yeah, exactly.
48 00:05:43.790 ⇒ 00:05:45.050 Awaish Kumar: like that, right?
49 00:05:45.050 ⇒ 00:05:45.600 Shreya Chowdhury: Yeah.
50 00:05:47.110 ⇒ 00:05:48.280 Awaish Kumar: Probably, yeah.
51 00:05:48.510 ⇒ 00:05:54.959 Awaish Kumar: I’ve done, … some similar work, like, when I was at… OU Vacation Homes?
52 00:05:55.650 ⇒ 00:05:56.480 Shreya Chowdhury: Okay.
53 00:05:56.650 ⇒ 00:06:03.079 Awaish Kumar: like, my title was a data engineer, but I was doing… working as a full-stack data person, so…
54 00:06:03.280 ⇒ 00:06:11.450 Awaish Kumar: Data from data analysts to… Engineer to scientist, and formal requirement gatherings, everything.
55 00:06:11.650 ⇒ 00:06:16.270 Awaish Kumar: Yeah. Like, normally we were doing that, like, one day, They were…
56 00:06:16.470 ⇒ 00:06:27.210 Awaish Kumar: Creating a new website, new brand, and… A-B testing between new, new pages, and… versus…
57 00:06:27.490 ⇒ 00:06:34.840 Awaish Kumar: Previous websites and things like that, and analyzing how… how the conversion happens.
58 00:06:35.120 ⇒ 00:06:39.829 Awaish Kumar: Yeah. Like, from your title, are we… I understood more, like, you are more…
59 00:06:40.420 ⇒ 00:06:43.400 Awaish Kumar: Into the tagging and tracking, and….
60 00:06:44.350 ⇒ 00:06:53.020 Shreya Chowdhury: Yeah, well… When I, yeah, when I joined here, my title here is, like, now Lead Product Analyst,
61 00:06:53.040 ⇒ 00:07:08.369 Shreya Chowdhury: Because, I think that’s, like, where they need the most help right now. I think Robert’s a little bit overloaded on his plate, so he’s looking to offload a lot of that analytics work to me, so he can basically just focus on sales. …
62 00:07:08.880 ⇒ 00:07:26.430 Shreya Chowdhury: And… yeah, like, I mean, that’s a space that I’m comfortable and familiar in, so it’s not really an issue, but I… I did tell them, like, when we were doing the interviews and the hiring process, I was like, yeah, I also have, like, experience in data engineering, like, if it’s in dbt or SQL, like.
63 00:07:26.430 ⇒ 00:07:30.930 Shreya Chowdhury: I can also pitch in there, given that I have bandwidth.
64 00:07:31.040 ⇒ 00:07:40.259 Shreya Chowdhury: I don’t have experience in analytics engineering, so I’m not good at the setup stuff, but once everything is set up, I can, like, crank out some data models here and there.
65 00:07:42.400 ⇒ 00:07:44.880 Shreya Chowdhury: But yeah, I think when….
66 00:07:45.110 ⇒ 00:07:50.770 Awaish Kumar: So most of the work we are doing for Ali, isn’t it more about, Like.
67 00:07:51.170 ⇒ 00:08:00.570 Awaish Kumar: I, An Utam, myself, we have both worked on the data, which is coming from these, platforms, like MixedPanel or
68 00:08:00.970 ⇒ 00:08:06.400 Awaish Kumar: amplitude or GFO, So, like, somebody is there to set that up.
69 00:08:06.590 ⇒ 00:08:21.130 Awaish Kumar: And when the events are being captured, the data is coming to BigQuery, and then we are… we have worked with that data to produce the insights and, like, models or pipelines. But the setting up those platforms, like, we don’t have any
70 00:08:21.550 ⇒ 00:08:26.200 Awaish Kumar: Like, hands-on experience with the….
71 00:08:26.910 ⇒ 00:08:40.740 Shreya Chowdhury: Yeah, so I don’t… again, I don’t know if we’ll be in charge of setting up that data, or if it will be someone else. Right now, I haven’t… I’m still just working on, like, getting together a tracking plan, so that’s mainly just
72 00:08:40.900 ⇒ 00:08:56.740 Shreya Chowdhury: like, an overview of what data we want to have and what format we want to have it in, so it’ll just be the number of data models, the different flows. So, like, pre-purchase, during purchase, after purchase, those three flows, then the customer flows,
73 00:08:57.130 ⇒ 00:09:08.449 Shreya Chowdhury: So we’re just mapping out, like, what we want the data models to look like, and what they’ll have, and what the properties are, and… once I have that, I’m gonna check in with Robert, see how he likes it, and then…
74 00:09:08.760 ⇒ 00:09:12.440 Shreya Chowdhury: after that, basically, I think we’ll figure out, like.
75 00:09:12.610 ⇒ 00:09:32.310 Shreya Chowdhury: who will be setting it up. Also, I haven’t… I haven’t had a chance to dig into, where that data is coming from, and how it’ll be, like, sourced for the pipeline. I… basically, right now, I’m just clicking through their website and figuring out, like, what the business processes are, so I can map them in data.
76 00:09:33.200 ⇒ 00:09:33.850 Awaish Kumar: Goodbye.
77 00:09:34.480 ⇒ 00:09:47.010 Awaish Kumar: Yeah, like, that… like, somebody has to set up that… those, tools, so that the kind of events you are triggering on the website are being captured somehow.
78 00:09:47.200 ⇒ 00:09:48.529 Awaish Kumar: In the back end here.
79 00:09:49.210 ⇒ 00:09:50.230 Awaish Kumar: Okay.
80 00:09:50.450 ⇒ 00:10:01.990 Awaish Kumar: … So, like, before, … Shopify? Like, I don’t know, how… how much, like, total experience do I have?
81 00:10:02.100 ⇒ 00:10:03.290 Awaish Kumar: How many years?
82 00:10:04.250 ⇒ 00:10:18.440 Shreya Chowdhury: So before Shopify, I had an internship for 8 months, or 9 months, and before that, I was in school full-time, so I was working on getting, like…
83 00:10:18.630 ⇒ 00:10:22.610 Shreya Chowdhury: Yeah, yeah, I went to Berkeley, so I wasn’t….
84 00:10:22.610 ⇒ 00:10:23.559 Awaish Kumar: I was on my time.
85 00:10:23.930 ⇒ 00:10:26.149 Shreya Chowdhury: Bachelor’s, I was getting my bachelor’s there.
86 00:10:26.430 ⇒ 00:10:35.510 Shreya Chowdhury: And then after that, I worked for the last, like, three and a half years, and yeah.
87 00:10:36.420 ⇒ 00:10:39.390 Awaish Kumar: So you grew up in, the U.S?
88 00:10:39.710 ⇒ 00:10:53.619 Shreya Chowdhury: Yeah, I grew up here. I grew up, in the Bay Area, so I don’t know where you’re based, but I’m in, … I’m very close to San Francisco. My parents, like, came here…
89 00:10:53.980 ⇒ 00:11:07.710 Shreya Chowdhury: they came to Fremont, I want to say, in 2001, and then 2003, so I’ve kind of been here since then. And then I went to school in the Bay Area, and then now I’m… I’m pretty much still here.
90 00:11:09.440 ⇒ 00:11:15.010 Awaish Kumar: Okay, … I… I’m right now in Kyrgyzstan.
91 00:11:17.820 ⇒ 00:11:21.679 Awaish Kumar: But, like, yeah, I have been in, …
92 00:11:22.190 ⇒ 00:11:27.419 Awaish Kumar: in Denmark, for 3 years, I worked there, then I moved to Canada for some time.
93 00:11:27.720 ⇒ 00:11:28.220 Shreya Chowdhury: Oh, nice.
94 00:11:28.220 ⇒ 00:11:30.719 Awaish Kumar: Right now, I’m staying in Pakistan.
95 00:11:31.230 ⇒ 00:11:32.859 Shreya Chowdhury: Okay, nice. Wow.
96 00:11:34.380 ⇒ 00:11:38.710 Awaish Kumar: I have, like, around 8 years of ex- Data engineer?
97 00:11:39.690 ⇒ 00:11:40.929 Shreya Chowdhury: Wow, okay.
98 00:11:41.910 ⇒ 00:11:47.410 Awaish Kumar: Yeah, I mean, majority of my time has… has just been spent on, like, startups.
99 00:11:47.570 ⇒ 00:11:52.489 Awaish Kumar: Working with remote, companies and, like, startup companies.
100 00:11:52.920 ⇒ 00:11:53.840 Shreya Chowdhury: Wow.
101 00:11:54.030 ⇒ 00:11:55.629 Awaish Kumar: on God’s Day tonight.
102 00:11:58.230 ⇒ 00:12:00.289 Awaish Kumar: Only, only one…
103 00:12:00.970 ⇒ 00:12:06.940 Awaish Kumar: company was really an enterprise where I worked for some time, but yeah, the pace was not…
104 00:12:08.980 ⇒ 00:12:17.260 Awaish Kumar: like, aligned with my work style, like, there was an enterprise and really, really very slow in moving.
105 00:12:17.470 ⇒ 00:12:20.409 Awaish Kumar: Yeah. Yeah, lots of people there, and…
106 00:12:21.250 ⇒ 00:12:25.950 Awaish Kumar: So, like, even, even, like, small decisions will take, take a long time when you deploy.
107 00:12:27.300 ⇒ 00:12:27.960 Shreya Chowdhury: Yeah.
108 00:12:28.960 ⇒ 00:12:44.900 Shreya Chowdhury: Yeah, I think, the work style is definitely different, like, if it’s a big tech company versus if it’s, like, smaller. I’ve worked with a smaller company once, and then big tech for the last 3 years, and now I thought, maybe I’ll switch back and see how it is.
109 00:12:47.080 ⇒ 00:12:55.520 Awaish Kumar: Yeah, I’ve been, … yeah, the company I’m talking about, like, they were, like, more about shipping companies, but yeah.
110 00:12:58.730 ⇒ 00:13:02.680 Awaish Kumar: Less, like, not just… not a fully, kind of a…
111 00:13:03.480 ⇒ 00:13:06.709 Awaish Kumar: technical, like Google or something, like…
112 00:13:06.960 ⇒ 00:13:13.590 Awaish Kumar: But my experience, like, maybe these companies, like, the tech companies, like.
113 00:13:14.240 ⇒ 00:13:17.870 Awaish Kumar: Google, AWS, VR, much more.
114 00:13:18.210 ⇒ 00:13:20.340 Awaish Kumar: Efficient in handling things.
115 00:13:20.590 ⇒ 00:13:22.530 Shreya Chowdhury: Yeah. 10 of those, yeah.
116 00:13:23.360 ⇒ 00:13:35.980 Shreya Chowdhury: Yeah, I honestly had the opposite experience. I feel like big tech, they tend to, like, move very fast, so it’s like, whatever you’re working on, it has to be done super fast. They want… they want it to be very efficient. …
117 00:13:36.240 ⇒ 00:13:37.859 Shreya Chowdhury: So… but….
118 00:13:37.860 ⇒ 00:13:48.360 Awaish Kumar: Like, it depends, … like, the… we have been in Shopify, right? Shopify’s business completely depends on…
119 00:13:49.120 ⇒ 00:13:51.529 Awaish Kumar: On tech services, right?
120 00:13:51.530 ⇒ 00:13:52.580 Shreya Chowdhury: Yeah.
121 00:13:52.610 ⇒ 00:13:56.950 Awaish Kumar: They want their customers to cruise the plate farm.
122 00:13:57.070 ⇒ 00:14:06.600 Awaish Kumar: build their websites and run their businesses. This is completely… so the companies which are… which are completely tech-focused.
123 00:14:07.310 ⇒ 00:14:09.540 Awaish Kumar: They might be much more efficient.
124 00:14:10.100 ⇒ 00:14:15.520 Awaish Kumar: But the other enterprises, like, for example, Bags are…
125 00:14:16.080 ⇒ 00:14:22.170 Awaish Kumar: logistic companies, our insurance company, I’ve seen them, like,
126 00:14:23.110 ⇒ 00:14:27.530 Awaish Kumar: And they’re a lot slow in their processes.
127 00:14:27.760 ⇒ 00:14:30.560 Awaish Kumar: At least that was my experience.
128 00:14:35.950 ⇒ 00:14:44.689 Awaish Kumar: Yeah, so… And, apart from, work, like, What, what are your activities?
129 00:14:46.080 ⇒ 00:14:50.730 Shreya Chowdhury: Apart from work these days, …
130 00:14:51.410 ⇒ 00:15:01.149 Shreya Chowdhury: Not… not a whole lot. I feel like I’ve just been trying to, like, get things cleaned up and, like, reset, so I finished moving. …
131 00:15:01.240 ⇒ 00:15:11.800 Shreya Chowdhury: I was living by myself for a while, and I just… I just moved into a new… a new place, so I’m working on getting things set up. Other than that, I try and…
132 00:15:12.230 ⇒ 00:15:18.630 Shreya Chowdhury: stay active, I’ll go to the gym, or go to, like, an exercise class here and there, …
133 00:15:19.160 ⇒ 00:15:22.210 Shreya Chowdhury: See my friends sometimes, …
134 00:15:22.970 ⇒ 00:15:33.969 Shreya Chowdhury: I feel like after work and doing that, that takes up, like, most of my time. There’s only, like, a finite amount of free time I have left after work and sleep and whatever else.
135 00:15:34.450 ⇒ 00:15:37.389 Shreya Chowdhury: So, yeah, just try and spend it.
136 00:15:37.390 ⇒ 00:15:39.179 Awaish Kumar: And if I don’t know.
137 00:15:39.390 ⇒ 00:15:47.290 Shreya Chowdhury: Yeah, yeah. Honestly, like, outside of work, these days, I try and just relax as much as possible. I feel like.
138 00:15:47.290 ⇒ 00:15:48.040 Awaish Kumar: Okay.
139 00:15:48.230 ⇒ 00:15:54.419 Shreya Chowdhury: In my last job, like, I had so little time to do that that I’m trying to just make up for it now.
140 00:15:54.780 ⇒ 00:15:55.790 Shreya Chowdhury: ….
141 00:15:55.970 ⇒ 00:16:01.650 Awaish Kumar: I… I would really get very, like, depressed if I had to stay.
142 00:16:01.770 ⇒ 00:16:04.800 Awaish Kumar: At home on the weekends.
143 00:16:05.190 ⇒ 00:16:06.780 Awaish Kumar: Probably North Belt.
144 00:16:07.820 ⇒ 00:16:09.179 Shreya Chowdhury: I think it depends…
145 00:16:09.700 ⇒ 00:16:21.840 Shreya Chowdhury: sometimes I really like a weekend in just all to myself. I don’t think… I don’t think I stay in the house all day, but it’s, like, just a whole day for me to, like, one, finish up things I don’t get to do during the week, like…
146 00:16:21.840 ⇒ 00:16:30.059 Shreya Chowdhury: laundry, errands, that kind of thing, and then once I’m done with those chores, I just… I just want to stay home and watch a movie or something, like…
147 00:16:30.610 ⇒ 00:16:33.190 Shreya Chowdhury: less… Energy draining.
148 00:16:34.730 ⇒ 00:16:35.310 Awaish Kumar: Okay.
149 00:16:41.450 ⇒ 00:16:41.990 Shreya Chowdhury: Yeah.
150 00:16:42.420 ⇒ 00:16:43.639 Shreya Chowdhury: What about you?
151 00:16:45.590 ⇒ 00:16:48.789 Awaish Kumar: Yeah, I like in traveling, …
152 00:16:51.050 ⇒ 00:16:52.909 Awaish Kumar: So I like to go out.
153 00:16:54.370 ⇒ 00:17:04.390 Awaish Kumar: with friends if I’m in the city, but I also like, long travels, like, on weekends, maybe I would just go out of the city to somewhere else, and,
154 00:17:05.200 ⇒ 00:17:07.089 Awaish Kumar: Have a little drive.
155 00:17:09.819 ⇒ 00:17:16.999 Awaish Kumar: I like… Like, I’ve been… I would, like, love to see, like, every place I, like…
156 00:17:17.140 ⇒ 00:17:21.889 Awaish Kumar: I don’t have any… preferences, but I just like feminine, so I don’t…
157 00:17:22.109 ⇒ 00:17:26.589 Awaish Kumar: I can go to the north, like, to see the mountains, I could go to…
158 00:17:26.940 ⇒ 00:17:31.029 Awaish Kumar: see the desert on, like, take me anywhere.
159 00:17:31.540 ⇒ 00:17:33.770 Awaish Kumar: I just don’t wanna stay home.
160 00:17:35.380 ⇒ 00:17:36.160 Shreya Chowdhury: Nice.
161 00:17:36.300 ⇒ 00:17:38.750 Shreya Chowdhury: Yeah, I like traveling, too.
162 00:17:39.100 ⇒ 00:17:40.649 Shreya Chowdhury: I feel like…
163 00:17:40.760 ⇒ 00:17:50.760 Shreya Chowdhury: I need breaks, though, from traveling. Like, sometimes, like, after I’ve been traveling for a while, I’m like, oh, I just want to be at home, be able to do laundry, sleep in my own bed.
164 00:17:52.500 ⇒ 00:17:54.909 Shreya Chowdhury: But yeah… That’s nice.
165 00:17:56.020 ⇒ 00:17:59.440 Awaish Kumar: Okay, I’m, like… What kind of, …
166 00:18:04.070 ⇒ 00:18:09.160 Awaish Kumar: Tuesday, having… having been losing to your last job.
167 00:18:10.590 ⇒ 00:18:30.550 Shreya Chowdhury: Yeah, so our tech stack, we had a lot of different tools, like, it just depended on your craft and, like, what you’re able to use, so… we used… for dashboarding, we mainly used Looker. We used to use Mode Analytics, which I liked better, but we deprecated that. …
168 00:18:30.960 ⇒ 00:18:48.239 Shreya Chowdhury: And we used, like, the Google Cloud Console for, like, our data models and querying, and BigQuery. And, we used Cursor for, like, building a lot of those pipelines, or VS Code. For AI tools, we had
169 00:18:48.350 ⇒ 00:18:58.579 Shreya Chowdhury: all of the GPT models, the Gemini models, and Claude. So I’m excited to, like, start work here and see, like, what toolset we have.
170 00:18:58.800 ⇒ 00:19:02.870 Shreya Chowdhury: But yeah, I think that was the majority of our tech stack.
171 00:19:03.220 ⇒ 00:19:13.219 Shreya Chowdhury: Or at least the one that I would work with mostly. There is, like, so many other tools, but those are mainly the ones that were relevant to my workflows.
172 00:19:15.500 ⇒ 00:19:19.619 Awaish Kumar: Yeah, so in terms of, tools, …
173 00:19:20.800 ⇒ 00:19:24.789 Awaish Kumar: like, you can get access to Custer, for example, ChatGPT,
174 00:19:25.160 ⇒ 00:19:31.869 Awaish Kumar: Yeah, I… I think Utam is really… Very encouraging of my….
175 00:19:32.290 ⇒ 00:19:32.960 Shreya Chowdhury: Yeah, he is.
176 00:19:32.960 ⇒ 00:19:40.419 Awaish Kumar: So, he would get you anything, if it can, like, speed up the process.
177 00:19:40.420 ⇒ 00:19:41.230 Shreya Chowdhury: Yeah.
178 00:19:41.990 ⇒ 00:19:42.770 Shreya Chowdhury: Yeah.
179 00:19:45.370 ⇒ 00:19:45.949 Awaish Kumar: Oh, boy.
180 00:19:47.910 ⇒ 00:19:59.869 Shreya Chowdhury: But yeah, I’m still getting my… I’m gonna figure out what tools we have and can use, and then if there’s anything else I need, I’ll reach out to him and see if it’s possible to bring it here.
181 00:20:00.180 ⇒ 00:20:04.140 Awaish Kumar: Yeah, like, if you have been using those AI tools for a long time.
182 00:20:04.530 ⇒ 00:20:07.030 Awaish Kumar: We would love to have,
183 00:20:07.710 ⇒ 00:20:10.960 Shreya Chowdhury: Yeah. I think we do have, …
184 00:20:11.110 ⇒ 00:20:14.460 Shreya Chowdhury: We do have Claude and Cursor, I believe.
185 00:20:15.010 ⇒ 00:20:21.000 Shreya Chowdhury: Actually, I’m not sure. I think we do have cursor, I haven’t fully set it up yet, but… ….
186 00:20:21.010 ⇒ 00:20:31.860 Awaish Kumar: We do have pressure. I’m just saying, like, maybe if you have been using it at Shopify, some tools, some AI tools which can really improve the team’s productivity.
187 00:20:32.270 ⇒ 00:20:32.740 Awaish Kumar: Yeah.
188 00:20:32.960 ⇒ 00:20:39.499 Awaish Kumar: It would be really nice if you can share that knowledge with us, so… Yeah. Have a session.
189 00:20:39.620 ⇒ 00:20:40.170 Awaish Kumar: For that.
190 00:20:40.170 ⇒ 00:20:47.370 Shreya Chowdhury: Yeah, I think I’m gonna spend this week, like, once I finish up this data tracking plan, just, like, jumping into the workflows and seeing, like.
191 00:20:47.370 ⇒ 00:21:01.089 Shreya Chowdhury: what tools I try and integrate into most of my work, and seeing, like, oh, which ones we can make better, or which ones we can add that would make these better. And then, by the end of the week, or next week, I’ll probably have a better idea of that kind of stuff.
192 00:21:01.110 ⇒ 00:21:04.479 Shreya Chowdhury: And I can share my learnings more widely with the team.
193 00:21:06.960 ⇒ 00:21:13.840 Awaish Kumar: Okay, fine. So right now, you are spending, like, 20 hours with Rain Forge, or…?
194 00:21:14.050 ⇒ 00:21:23.100 Shreya Chowdhury: So, I think it’s… it’s, like, 20 to 30, like, depending on what is needed per week, and then, they’ll see, like, if…
195 00:21:23.440 ⇒ 00:21:30.759 Shreya Chowdhury: you know, if I’m doing… if I’m a good fit for the role, and, like, if there’s enough work for me to be doing more than that, then, like.
196 00:21:30.900 ⇒ 00:21:35.040 Shreya Chowdhury: I’ll most likely transition to full-time.
197 00:21:36.860 ⇒ 00:21:39.740 Awaish Kumar: And how’s your, like, experience,
198 00:21:42.100 ⇒ 00:21:45.919 Awaish Kumar: with Tableau, Power BI, some of these tools.
199 00:21:45.920 ⇒ 00:22:04.689 Shreya Chowdhury: Yeah, so, with Tableau and Power BI… Tableau more so, I have used it, just not recently so much. So, like, in the past year, year and a half, I haven’t used it, because our tech stack didn’t have Tableau, we mostly used, like, Mode and Looker Studios. Power BI, a little bit less.
200 00:22:04.690 ⇒ 00:22:16.410 Shreya Chowdhury: But yeah, I mean, it is there, but also not recent. So for those tools, like, I’m… I’m not uncomfortable to use them, but I would need, like.
201 00:22:16.410 ⇒ 00:22:26.719 Shreya Chowdhury: I would say there would be, like, a little bit of a relearning curve there for me, so I would probably need, like, a little bit time to refresh and onboard myself on that, but, …
202 00:22:26.790 ⇒ 00:22:30.389 Shreya Chowdhury: Yeah, I would say experiences there, but just a while back.
203 00:22:30.920 ⇒ 00:22:34.569 Awaish Kumar: You know… So, you mentioned Tableau, right?
204 00:22:35.170 ⇒ 00:22:35.680 Shreya Chowdhury: Yeah.
205 00:22:35.680 ⇒ 00:22:37.919 Awaish Kumar: However, pretty much. Okay.
206 00:22:38.590 ⇒ 00:22:49.539 Awaish Kumar: I don’t, like… why, … teams have been using Looker, like, it’s really… really, very complex.
207 00:22:49.960 ⇒ 00:22:58.499 Shreya Chowdhury: Yeah, Looker has, like, capacity for a lot of features, but it’s not intuitive to use. I never liked it.
208 00:22:58.930 ⇒ 00:23:12.489 Shreya Chowdhury: I resent the fact that I’m better at using Looker than Tableau, or whatever, but that’s just because that’s, like, I didn’t have a choice, like, I had to use Looker. It’s not my favorite.
209 00:23:13.550 ⇒ 00:23:19.240 Shreya Chowdhury: I don’t have a lot… Good things to say about it for, like, general reporting.
210 00:23:20.110 ⇒ 00:23:23.680 Awaish Kumar: So, like, I did work for a company to…
211 00:23:24.800 ⇒ 00:23:27.689 Awaish Kumar: like, I’ve been migrating the local
212 00:23:28.050 ⇒ 00:23:31.509 Awaish Kumar: This mother told and, …
213 00:23:31.770 ⇒ 00:23:37.230 Awaish Kumar: Like, it’s so much easier to handle the transformation out of
214 00:23:37.510 ⇒ 00:23:41.280 Awaish Kumar: BI tool in Tableau or Python or whatever.
215 00:23:41.870 ⇒ 00:23:53.039 Awaish Kumar: And use the, like, BI tool as a BI tool, like, for small transformations, and most major… mainly for, like, dashboarding.
216 00:23:53.340 ⇒ 00:23:57.630 Awaish Kumar: And, and reporting, and that, that may merit, like, much more…
217 00:23:58.270 ⇒ 00:24:01.849 Awaish Kumar: Faster to develop things and penetrate.
218 00:24:02.290 ⇒ 00:24:07.470 Awaish Kumar: In Looker, like, you have to, first of all, learn the language.
219 00:24:08.900 ⇒ 00:24:13.560 Awaish Kumar: If you want to do any kind of transformation or anything, yeah.
220 00:24:14.930 ⇒ 00:24:24.360 Shreya Chowdhury: Yeah. The other thing that I didn’t love about Looker, and I don’t know if this is different in other applications, or just it was bad the way that we were using it, but it was, like…
221 00:24:24.610 ⇒ 00:24:26.599 Shreya Chowdhury: You would have to, like…
222 00:24:27.530 ⇒ 00:24:34.430 Shreya Chowdhury: load a lot of the data separately, or write separate queries for each visualization. …
223 00:24:34.740 ⇒ 00:24:45.809 Shreya Chowdhury: Versus when I would use, like, Mode Analytics or a Python Jupyter notebook, it’s like, I can load the data into, like, one data frame that I like, and then just, like.
224 00:24:46.690 ⇒ 00:24:50.679 Shreya Chowdhury: adjust that one to make the visuals that I need. …
225 00:24:50.800 ⇒ 00:24:55.259 Shreya Chowdhury: So I wouldn’t have to go back and write, like, 10 different queries or something, but….
226 00:24:57.900 ⇒ 00:24:58.979 Awaish Kumar: Okay, got it.
227 00:25:01.320 ⇒ 00:25:07.700 Awaish Kumar: Yeah, now people are moving more towards, like, VIS support, It goes like that.
228 00:25:08.330 ⇒ 00:25:11.060 Awaish Kumar: Real data or something, right?
229 00:25:11.220 ⇒ 00:25:17.199 Awaish Kumar: Which are… which… Which are still, like, kind of starters, but they are… they are going to take over.
230 00:25:17.580 ⇒ 00:25:20.290 Awaish Kumar: Because they are cheaper than I am.
231 00:25:20.610 ⇒ 00:25:24.369 Awaish Kumar: And faster, and, like, provide us with a…
232 00:25:25.080 ⇒ 00:25:28.990 Awaish Kumar: use it, I could use, like, SQL and, and…
233 00:25:29.240 ⇒ 00:25:34.480 Awaish Kumar: vinyl files to build, build anything, like dashboards, charts, everything.
234 00:25:37.020 ⇒ 00:25:37.740 Shreya Chowdhury: Yeah.
235 00:25:39.650 ⇒ 00:25:40.590 Shreya Chowdhury: Yeah.
236 00:25:41.540 ⇒ 00:25:44.410 Awaish Kumar: Okay, yeah, so it has been nice talking to you.
237 00:25:44.790 ⇒ 00:25:46.689 Shreya Chowdhury: Yeah, you too, thanks for setting this up.
238 00:25:48.210 ⇒ 00:25:50.929 Awaish Kumar: Yeah, this was, like, part of first, …
239 00:25:51.210 ⇒ 00:25:54.180 Awaish Kumar: Coffee chat we were discussing in the leads meeting.
240 00:25:54.180 ⇒ 00:25:55.500 Shreya Chowdhury: Yeah, yeah.
241 00:25:55.860 ⇒ 00:25:58.550 Awaish Kumar: Yeah, so I’ll just set it up myself, ….
242 00:25:58.550 ⇒ 00:26:03.669 Shreya Chowdhury: Yeah, thanks for doing that. I’m planning on setting up some more one-on-ones with other people on the team.
243 00:26:03.970 ⇒ 00:26:10.749 Shreya Chowdhury: As I get to know them, but yeah. I’ll probably do that later this week, and early next week.
244 00:26:12.560 ⇒ 00:26:13.770 Awaish Kumar: Okay, yeah.
245 00:26:13.930 ⇒ 00:26:16.690 Awaish Kumar: Thank you. It was nice talking to you.
246 00:26:16.690 ⇒ 00:26:17.450 Shreya Chowdhury: I second for you.
247 00:26:17.450 ⇒ 00:26:19.820 Awaish Kumar: If you have any feedback, like, just let me know.
248 00:26:20.130 ⇒ 00:26:21.629 Awaish Kumar: Yeah, will do. Thank you.
249 00:26:21.640 ⇒ 00:26:22.570 Shreya Chowdhury: Bye.