Meeting Title: CTA and Magic Spoon Project Sync Date: 2026-01-16 Meeting participants: Ashwini Sharma, Uttam Kumaran
WEBVTT
1 00:00:44.200 ⇒ 00:00:45.660 Uttam Kumaran: Hey man, you’re up late!
2 00:00:45.660 ⇒ 00:00:54.459 Ashwini Sharma: Yeah, yeah. I generally log off at around 2 sometimes. Sometimes, rarely before 2, but generally it’s.
3 00:00:54.460 ⇒ 00:00:55.070 Uttam Kumaran: Okay.
4 00:00:55.480 ⇒ 00:00:56.320 Ashwini Sharma: You’re after it.
5 00:00:56.320 ⇒ 00:01:07.940 Uttam Kumaran: No, I know, I keep, like, missing it, dude, it’s been, like… it’s, like, so… I feel so bad, I, like, I… that interview I was in went long, and then, dude, the guy I was interviewing, like, was so mean to me.
6 00:01:08.130 ⇒ 00:01:11.070 Uttam Kumaran: He’s like, so rude.
7 00:01:13.810 ⇒ 00:01:14.720 Uttam Kumaran: It’s like…
8 00:01:15.200 ⇒ 00:01:25.639 Uttam Kumaran: It’s so tough, because, like, I call so many people, and I don’t… it’s a… I understand that there are mean people in our world, and so, yes, they exist.
9 00:01:25.950 ⇒ 00:01:32.750 Uttam Kumaran: occasionally, they find their way into something, and I’m like… he kept, like, he was, like, so suspicious about, like.
10 00:01:32.870 ⇒ 00:01:35.000 Uttam Kumaran: How the company works, and like…
11 00:01:35.750 ⇒ 00:01:40.420 Uttam Kumaran: How we’re, like, growing, and it was just, like, so rude, and it was, like…
12 00:01:41.260 ⇒ 00:01:49.360 Uttam Kumaran: The worst. And I should’ve… should’ve just hung up and called you. Instead, I, like, let him go, and I, like, kind of, like.
13 00:01:50.030 ⇒ 00:01:53.869 Uttam Kumaran: I, like, tried to explain, and they just wouldn’t understand, and I was like.
14 00:01:55.280 ⇒ 00:02:00.880 Ashwini Sharma: I have only one, one weird experience of taking interviews.
15 00:02:00.880 ⇒ 00:02:01.420 Uttam Kumaran: Tell me!
16 00:02:02.040 ⇒ 00:02:10.660 Ashwini Sharma: And, like, I asked him questions, and then I asked him a, you know, question where he had to apply some thought and come up with an answer, right?
17 00:02:10.800 ⇒ 00:02:20.010 Ashwini Sharma: As looking at, you know, how comfortable he is with data structures and algorithms, and how he can come up with a certain data structure that is useful for this kind of situation, right?
18 00:02:20.750 ⇒ 00:02:23.380 Ashwini Sharma: And he could not do that.
19 00:02:23.720 ⇒ 00:02:28.549 Ashwini Sharma: And, you know, after the interview was over, you know, he went.
20 00:02:29.240 ⇒ 00:02:35.639 Ashwini Sharma: And it was almost towards the end of the day. This is pre-COVID, right? So, people were working from office.
21 00:02:35.950 ⇒ 00:02:48.389 Ashwini Sharma: And, it was time to go home, so I also went down, right? Around, like, 40, 50 minutes after the interview. So when I go down, the guy was waiting for him, me, over there, and he said, you know, I…
22 00:02:48.390 ⇒ 00:02:48.970 Uttam Kumaran: interview.
23 00:02:49.280 ⇒ 00:02:50.850 Ashwini Sharma: Yeah, he’ll be.
24 00:02:50.850 ⇒ 00:02:51.860 Uttam Kumaran: Oh my god.
25 00:02:51.860 ⇒ 00:03:05.270 Ashwini Sharma: He said, now I recall what was the answer to your question, and then he started talking about that answer over there, and I said, okay, guys, it’s fine, I’m okay with your interview, I have provided feedback, it’s okay.
26 00:03:05.450 ⇒ 00:03:05.990 Ashwini Sharma: Bold?
27 00:03:05.990 ⇒ 00:03:08.439 Uttam Kumaran: Oh my god, you thought he was gonna beat you up or something?
28 00:03:08.440 ⇒ 00:03:11.280 Ashwini Sharma: It was funny, man.
29 00:03:13.430 ⇒ 00:03:14.480 Uttam Kumaran: That’s a pleasure.
30 00:03:14.480 ⇒ 00:03:19.659 Ashwini Sharma: I would have never expected somebody to stay 40 minutes just to clarify his answer, man.
31 00:03:20.720 ⇒ 00:03:23.710 Uttam Kumaran: I do, but some people really have, like, emotional, like…
32 00:03:24.180 ⇒ 00:03:26.080 Uttam Kumaran: I don’t know, it’s really tough, and…
33 00:03:26.230 ⇒ 00:03:29.179 Uttam Kumaran: you know how we interviewed, like, when I called you, I was like.
34 00:03:29.370 ⇒ 00:03:31.599 Uttam Kumaran: Well, tell me about, like, what you’re doing, and…
35 00:03:31.880 ⇒ 00:03:35.599 Uttam Kumaran: It’s not like a… we don’t… you know, we don’t do formal, really formal interviews.
36 00:03:35.600 ⇒ 00:03:36.140 Ashwini Sharma: Right.
37 00:03:36.140 ⇒ 00:03:37.959 Uttam Kumaran: Because I’m not trying to figure out
38 00:03:38.150 ⇒ 00:03:41.950 Uttam Kumaran: if you’re formal, I actually need people that are like you, who are like.
39 00:03:42.540 ⇒ 00:03:51.920 Uttam Kumaran: You sort of figure it out, you know, and you dance, and it’s a process in order to identify people that can explain, but also, like, be personable.
40 00:03:53.260 ⇒ 00:03:57.609 Uttam Kumaran: And some people just don’t, they’ve never, they’re like, no, there’s something is scam here.
41 00:03:57.730 ⇒ 00:04:03.899 Uttam Kumaran: You know? I’m like, okay, dude, you’re lost, like, whatever.
42 00:04:06.220 ⇒ 00:04:08.000 Uttam Kumaran: Cool.
43 00:04:08.340 ⇒ 00:04:08.670 Ashwini Sharma: So.
44 00:04:08.670 ⇒ 00:04:09.660 Uttam Kumaran: Yeah, I just wanted to…
45 00:04:09.660 ⇒ 00:04:12.400 Ashwini Sharma: Chandra Seishu joined, right? Shayshu Chandra.
46 00:04:12.400 ⇒ 00:04:16.390 Uttam Kumaran: Yeah, Shashu, he’s gonna help on the, operations side.
47 00:04:16.399 ⇒ 00:04:18.649 Ashwini Sharma: Oh, okay, not on the data engineering?
48 00:04:19.010 ⇒ 00:04:22.420 Uttam Kumaran: On the data side, yeah. He’s a young guy here in Texas.
49 00:04:22.450 ⇒ 00:04:23.710 Ashwini Sharma: Okay.
50 00:04:24.480 ⇒ 00:04:30.260 Uttam Kumaran: he’s actually friends with Pranav. Pranav is a full-stack And,
51 00:04:30.730 ⇒ 00:04:34.099 Uttam Kumaran: Yeah, he’s gonna come help us with some operations, so we’ve sort of done…
52 00:04:34.440 ⇒ 00:04:41.580 Uttam Kumaran: hiring on the operations side. I mean, overall, like, things should continue to get smoother on, like, Everything.
53 00:04:41.760 ⇒ 00:04:46.980 Uttam Kumaran: But we are still in the market for… more…
54 00:04:48.100 ⇒ 00:04:50.899 Uttam Kumaran: Probably more analytics engineers, more analysts.
55 00:04:51.240 ⇒ 00:04:52.839 Ashwini Sharma: Okay, cool.
56 00:04:54.880 ⇒ 00:04:55.480 Uttam Kumaran: So…
57 00:04:55.480 ⇒ 00:04:56.440 Ashwini Sharma: Alright, so I’ll tell me.
58 00:04:56.440 ⇒ 00:04:58.530 Uttam Kumaran: Tell me about, like… yeah, yeah, go ahead.
59 00:04:58.730 ⇒ 00:05:01.650 Ashwini Sharma: No, no, I mean, if I come across somebody, I’ll refer definitely.
60 00:05:01.650 ⇒ 00:05:02.020 Uttam Kumaran: Please.
61 00:05:02.020 ⇒ 00:05:07.120 Ashwini Sharma: But yeah, right now, right now, I don’t have anyone who…
62 00:05:08.530 ⇒ 00:05:11.060 Ashwini Sharma: Yeah, and we’re doing, and we’re doing…
63 00:05:11.060 ⇒ 00:05:13.959 Uttam Kumaran: We’re doing incentives for referrals.
64 00:05:13.960 ⇒ 00:05:14.880 Ashwini Sharma: Okay.
65 00:05:14.880 ⇒ 00:05:20.049 Uttam Kumaran: So, if there’s somebody in your past life that you’re like, okay, could be a shot, let me know.
66 00:05:20.050 ⇒ 00:05:22.429 Ashwini Sharma: I mean, even if they’re data engineers.
67 00:05:22.630 ⇒ 00:05:27.479 Uttam Kumaran: Like, they still… we still… Kinda need them to also be able to do some modeling.
68 00:05:28.470 ⇒ 00:05:29.230 Ashwini Sharma: Yeah.
69 00:05:29.840 ⇒ 00:05:30.390 Ashwini Sharma: Cool.
70 00:05:30.930 ⇒ 00:05:31.870 Ashwini Sharma: Yeah, I’ll…
71 00:05:31.870 ⇒ 00:05:32.370 Uttam Kumaran: Hell no.
72 00:05:32.370 ⇒ 00:05:34.739 Ashwini Sharma: Somebody… somebody wants to join, yeah.
73 00:05:35.090 ⇒ 00:05:35.710 Uttam Kumaran: Yeah.
74 00:05:35.810 ⇒ 00:05:39.139 Uttam Kumaran: How do you, tell me about the last two weeks, like, how’s everything going?
75 00:05:39.460 ⇒ 00:05:43.849 Ashwini Sharma: Last two weeks, what did I do? It was mostly Magic Spawns.
76 00:05:44.320 ⇒ 00:05:49.760 Ashwini Sharma: At least this week was mostly magic spoons. Last week, also, it was…
77 00:05:50.570 ⇒ 00:05:54.750 Ashwini Sharma: Yeah, it was mainly Magic Spawns only, like, because CTA has been off.
78 00:05:54.750 ⇒ 00:05:57.039 Uttam Kumaran: What about eat-in stuff? No eat-in stuff, really?
79 00:05:57.040 ⇒ 00:06:04.229 Ashwini Sharma: So, Eden stuff, Zoran reaches out on and off, asking questions about some Eden data.
80 00:06:05.160 ⇒ 00:06:15.930 Ashwini Sharma: But yeah, there has been one particular issue that Robert asked me to look into, but they were two different things, and I have to get back to him on this one.
81 00:06:16.080 ⇒ 00:06:27.729 Ashwini Sharma: it was mainly, like, you know, he’s looking at a Tableau report, and then there was a spike in Tableau. The reason for that spike was completely different, right? And then he’s asking, like, why does this data in Tableau
82 00:06:28.410 ⇒ 00:06:29.730 Ashwini Sharma: Sorry.
83 00:06:29.880 ⇒ 00:06:45.400 Ashwini Sharma: this data in Northbeam does not match with our tablet reports. And then I looked into it, and again, those are two different things, right? The one in North Beam is the attribution-related data, which is, like, one-day kind of data, right? So if certain criteria matches.
84 00:06:45.720 ⇒ 00:06:47.640 Ashwini Sharma: We kind of show it in North Beam.
85 00:06:47.750 ⇒ 00:06:56.549 Ashwini Sharma: But the data in, in, in BASC, it’s, it’s, overall data, so definitely it’s going to have a larger volume, more,
86 00:06:56.940 ⇒ 00:06:58.600 Ashwini Sharma: More number of orders.
87 00:06:59.170 ⇒ 00:07:09.209 Ashwini Sharma: So I’ll kind of explain after that. I didn’t get to join the stand-up today, he didn’t stand-up, but I think on Monday I’m going to explain. And then there are two tickets that were created for me.
88 00:07:09.670 ⇒ 00:07:11.809 Ashwini Sharma: Or some Eden-related work.
89 00:07:12.330 ⇒ 00:07:14.869 Ashwini Sharma: That also I’ll address on Monday only now.
90 00:07:15.260 ⇒ 00:07:23.110 Ashwini Sharma: Yeah, and this guy was struggling for loading a CSV in Redshift.
91 00:07:23.850 ⇒ 00:07:26.289 Ashwini Sharma: And, that is completed today.
92 00:07:26.650 ⇒ 00:07:28.209 Uttam Kumaran: Okay, yeah, I saw that, so…
93 00:07:28.480 ⇒ 00:07:30.809 Uttam Kumaran: It seems like they move so slow, dude.
94 00:07:31.070 ⇒ 00:07:32.090 Ashwini Sharma: Yeah, yeah.
95 00:07:32.090 ⇒ 00:07:33.609 Uttam Kumaran: They seem okay with it, I don’t know.
96 00:07:34.280 ⇒ 00:07:38.569 Ashwini Sharma: Yeah, that… that was pretty slow, and it’s… it was such a, you know…
97 00:07:38.670 ⇒ 00:07:45.180 Ashwini Sharma: I mean, somebody who’s using Redshift or things… I have not used Redshift in my past life, right? I was mostly into
98 00:07:45.450 ⇒ 00:07:47.989 Ashwini Sharma: Snowflake and BigQuery.
99 00:07:49.350 ⇒ 00:07:53.109 Ashwini Sharma: And I was able to load it. I don’t know what they were doing it since such a long time.
100 00:07:53.110 ⇒ 00:07:58.330 Uttam Kumaran: No, I don’t know if they, like, have… don’t have enough resources or what, but.
101 00:07:58.530 ⇒ 00:08:00.010 Ashwini Sharma: No idea.
102 00:08:00.010 ⇒ 00:08:01.559 Uttam Kumaran: Yeah, I don’t know.
103 00:08:03.400 ⇒ 00:08:10.360 Ashwini Sharma: Yeah, and then I’ll work on QA starting Monday. That’s a lot of data over there.
104 00:08:11.190 ⇒ 00:08:11.920 Ashwini Sharma: Yeah.
105 00:08:12.370 ⇒ 00:08:16.469 Ashwini Sharma: At least we have the table now against which I can do the QA.
106 00:08:18.970 ⇒ 00:08:20.179 Uttam Kumaran: Okay, okay, great.
107 00:08:20.560 ⇒ 00:08:26.890 Uttam Kumaran: Yeah, I don’t know, like, I know we’re… you’re kind of gonna get looped into Element, I know you’re kind of split.
108 00:08:27.270 ⇒ 00:08:29.460 Uttam Kumaran: thin, so I want to find a way to…
109 00:08:30.290 ⇒ 00:08:37.280 Uttam Kumaran: probably, like, cap it there, or see, like, what we can do. I mean, when the next… when another engineer comes in, like, we can start to hand some stuff off.
110 00:08:37.700 ⇒ 00:08:41.470 Uttam Kumaran: Element is gonna start to grow, and CTA’s gonna grow.
111 00:08:41.470 ⇒ 00:08:42.030 Ashwini Sharma: Yeah.
112 00:08:42.039 ⇒ 00:08:45.329 Uttam Kumaran: You know, so I just want to make sure that you’re okay there.
113 00:08:45.910 ⇒ 00:08:54.579 Ashwini Sharma: So, like, I need to be involved in CTA, Magic Spoon, and, Eden as well, or, is that,
114 00:08:54.790 ⇒ 00:08:58.180 Ashwini Sharma: Eden will be all cashy, and… element.
115 00:08:59.400 ⇒ 00:09:03.759 Uttam Kumaran: I think we’re gonna bring on one more AE to support
116 00:09:04.160 ⇒ 00:09:05.729 Uttam Kumaran: On at least one of those.
117 00:09:05.860 ⇒ 00:09:09.099 Uttam Kumaran: Okay. So then you’ll be at 4, it’s kind of a lot.
118 00:09:09.790 ⇒ 00:09:10.400 Ashwini Sharma: Yeah.
119 00:09:11.950 ⇒ 00:09:17.539 Uttam Kumaran: But we’ll see, kind of, what happens with Magic Spoon. Like, after your part, it may be mostly Demulade.
120 00:09:18.760 ⇒ 00:09:19.270 Ashwini Sharma: Yeah, it’s.
121 00:09:19.270 ⇒ 00:09:19.970 Uttam Kumaran: Modeling, right?
122 00:09:19.970 ⇒ 00:09:22.099 Ashwini Sharma: Right, for modeling, it will be demilodi.
123 00:09:23.620 ⇒ 00:09:24.200 Uttam Kumaran: Yeah.
124 00:09:26.080 ⇒ 00:09:26.670 Uttam Kumaran: So…
125 00:09:26.670 ⇒ 00:09:30.750 Ashwini Sharma: They also have some plans for other connectors, right, which is…
126 00:09:30.750 ⇒ 00:09:31.389 Uttam Kumaran: Oh, okay.
127 00:09:31.390 ⇒ 00:09:35.779 Ashwini Sharma: Phase 2 of Magic Spoon’s TikTok and something else.
128 00:09:36.790 ⇒ 00:09:41.630 Ashwini Sharma: Okay. That was there in the roadmap, so… Yeah,
129 00:09:42.630 ⇒ 00:09:47.979 Ashwini Sharma: Magic spoons. CTA, I hope she will add some more data.
130 00:09:48.520 ⇒ 00:10:00.059 Ashwini Sharma: if she adds, then I can start working on the other stuff of that, report, right? This Power BI thing, have we done… is anybody… has anybody in the team done a Power BI report?
131 00:10:00.060 ⇒ 00:10:02.180 Uttam Kumaran: Maybe just me and… maybe just me and Awash.
132 00:10:02.470 ⇒ 00:10:03.130 Ashwini Sharma: Okay.
133 00:10:03.300 ⇒ 00:10:04.000 Ashwini Sharma: Cool.
134 00:10:05.040 ⇒ 00:10:13.670 Ashwini Sharma: So, yeah, I mean, if we have to do that Power BI thing for a CTA, we have at least a wish, right, who can guide.
135 00:10:13.670 ⇒ 00:10:14.310 Uttam Kumaran: Yeah.
136 00:10:14.310 ⇒ 00:10:14.920 Ashwini Sharma: Okay.
137 00:10:16.770 ⇒ 00:10:21.409 Ashwini Sharma: That’s there then, what else was there?
138 00:10:22.750 ⇒ 00:10:24.080 Ashwini Sharma: It’s a CTA…
139 00:10:24.280 ⇒ 00:10:33.550 Ashwini Sharma: Yeah, CTA, if data comes, then only I’ll be able to proceed, right? Magic Spoon, at least, I have something on the plate, and then I have something on Eden that I can work on next week, yeah.
140 00:10:34.850 ⇒ 00:10:36.000 Uttam Kumaran: Yes.
141 00:10:39.500 ⇒ 00:10:40.050 Ashwini Sharma: Yep.
142 00:10:40.560 ⇒ 00:10:41.330 Uttam Kumaran: Okay.
143 00:10:41.600 ⇒ 00:10:43.280 Ashwini Sharma: Alright.
144 00:10:43.280 ⇒ 00:10:48.159 Uttam Kumaran: Cool, dude, so, like, maybe let’s try to chat on Monday, and we can do some further CTA planning.
145 00:10:48.500 ⇒ 00:10:49.150 Ashwini Sharma: Yeah, yeah, yeah.
146 00:10:49.150 ⇒ 00:10:51.600 Uttam Kumaran: So, like, I think we’re in a really good spot, though.
147 00:10:52.220 ⇒ 00:10:52.790 Ashwini Sharma: Yep.
148 00:10:52.920 ⇒ 00:10:53.660 Ashwini Sharma: Cool.
149 00:10:53.800 ⇒ 00:10:58.669 Uttam Kumaran: I feel like what we’re finding is, like, some clients move really fast, some move slow, and we’re, like, figuring out…
150 00:10:58.910 ⇒ 00:11:04.100 Ashwini Sharma: Yeah, this, I had a question about this polyatomic rate, so…
151 00:11:04.410 ⇒ 00:11:10.969 Ashwini Sharma: when they bring out a new connector, like, for example, they are working on the Salesforce Marketing Cloud connector, right?
152 00:11:11.260 ⇒ 00:11:11.760 Uttam Kumaran: Yeah.
153 00:11:11.920 ⇒ 00:11:16.499 Ashwini Sharma: How confident are we about the data that Polyatomic will move?
154 00:11:18.460 ⇒ 00:11:23.719 Uttam Kumaran: Well, I guess this is where we have to think about options, right?
155 00:11:24.240 ⇒ 00:11:31.650 Uttam Kumaran: The alternative is to go with Fivetran, but no way Fivetran’s gonna build some of the connectors that CTA needs, right?
156 00:11:32.370 ⇒ 00:11:35.380 Ashwini Sharma: Right, they will not, they will not, definitely.
157 00:11:35.690 ⇒ 00:11:46.980 Ashwini Sharma: No, my question is, like, you know, even if they do it, right, sorry, not… about polyatomic, right? So they come… normally, like, this was the experience with climate transfer.
158 00:11:46.980 ⇒ 00:11:50.329 Uttam Kumaran: So, so far… yeah, so you got it, you got it.
159 00:11:50.330 ⇒ 00:11:54.090 Ashwini Sharma: Yeah, yeah, so when I was developing connectors with Fivetran, right.
160 00:11:54.190 ⇒ 00:11:56.870 Ashwini Sharma: It used to take at least 2-3 months.
161 00:11:57.090 ⇒ 00:11:57.880 Ashwini Sharma: And…
162 00:11:58.600 ⇒ 00:12:07.399 Ashwini Sharma: at the end of two to three months, it used to be open for only one customer, right? We called that a private preview kind of thing. And then,
163 00:12:08.070 ⇒ 00:12:08.640 Ashwini Sharma: one…
164 00:12:08.640 ⇒ 00:12:10.920 Uttam Kumaran: And then public preview, and then GA.
165 00:12:10.920 ⇒ 00:12:14.840 Ashwini Sharma: Yeah, and then it went to beta, and then G, right?
166 00:12:15.040 ⇒ 00:12:19.009 Ashwini Sharma: But, even when it was in GA, we still had bugs.
167 00:12:20.710 ⇒ 00:12:25.950 Ashwini Sharma: So, I expect the similar kind of situation with polyatomic, right?
168 00:12:28.480 ⇒ 00:12:30.159 Uttam Kumaran: Yeah, and this is where, like.
169 00:12:30.800 ⇒ 00:12:38.160 Uttam Kumaran: we’re gonna have to see. I mean, so far, when we’ve requested new connectors, there have been issues, but it’s been resolved quickly.
170 00:12:38.460 ⇒ 00:12:39.210 Ashwini Sharma: Okay.
171 00:12:39.970 ⇒ 00:12:46.940 Uttam Kumaran: So, it’s sort of, like, worst of two evils, dude.
172 00:12:47.800 ⇒ 00:12:52.170 Uttam Kumaran: Because what do we do? Recommend 2? Like, I don’t know, it’s, like, so stupid, but…
173 00:12:54.120 ⇒ 00:12:54.510 Ashwini Sharma: They’re.
174 00:12:54.510 ⇒ 00:12:54.910 Uttam Kumaran: Definitely.
175 00:12:54.910 ⇒ 00:12:56.010 Ashwini Sharma: polyatomic,
176 00:12:56.010 ⇒ 00:12:56.909 Uttam Kumaran: Yeah, they’re cheaper.
177 00:12:56.910 ⇒ 00:12:58.870 Ashwini Sharma: cheaper than Fivetran, or…
178 00:12:58.870 ⇒ 00:13:02.139 Uttam Kumaran: They’re much cheaper, and we have a direct line to support.
179 00:13:02.890 ⇒ 00:13:05.100 Uttam Kumaran: I mean, look, maybe if it’s, like.
180 00:13:07.290 ⇒ 00:13:13.239 Uttam Kumaran: We’re sending them so much business, I should just tell them that we need a dedicated support person, I don’t know.
181 00:13:14.350 ⇒ 00:13:16.580 Ashwini Sharma: True, yeah, they should, they should.
182 00:13:17.460 ⇒ 00:13:20.560 Uttam Kumaran: Yeah, so that’s something that I’ll talk to Golub about, maybe, and…
183 00:13:22.410 ⇒ 00:13:27.310 Uttam Kumaran: Yeah, but overall, they’ve been good, and they’re good engineers, like, Really good.
184 00:13:28.950 ⇒ 00:13:29.700 Uttam Kumaran: So…
185 00:13:32.260 ⇒ 00:13:36.409 Ashwini Sharma: Yeah, and they also… CTA also had some…
186 00:13:36.580 ⇒ 00:13:39.419 Ashwini Sharma: Shopify thing, right? What is that,
187 00:13:40.080 ⇒ 00:13:44.559 Ashwini Sharma: Was it setting up a Shopify shop, or was it setting up a connector?
188 00:13:44.920 ⇒ 00:13:46.380 Uttam Kumaran: No, setting up a shop.
189 00:13:49.370 ⇒ 00:13:52.499 Ashwini Sharma: Does anyone have experience of doing that?
190 00:13:53.380 ⇒ 00:13:56.010 Uttam Kumaran: We would bring someone in.
191 00:13:56.280 ⇒ 00:13:56.950 Ashwini Sharma: Okay.
192 00:13:57.410 ⇒ 00:13:59.620 Uttam Kumaran: Yeah.
193 00:14:00.070 ⇒ 00:14:04.170 Uttam Kumaran: We have some people that we worked with in the past who will handle it, yeah.
194 00:14:04.430 ⇒ 00:14:05.470 Ashwini Sharma: Okay, cool.
195 00:14:05.470 ⇒ 00:14:10.260 Uttam Kumaran: But, yeah.
196 00:14:10.940 ⇒ 00:14:17.250 Uttam Kumaran: Weird. So we’ll see, but I just think they don’t have a lot of support, so we’re kind of seeing where we can be helpful, so…
197 00:14:19.360 ⇒ 00:14:20.440 Uttam Kumaran: Alright.
198 00:14:23.330 ⇒ 00:14:29.790 Ashwini Sharma: Yeah, so… yeah, so CTA is kind of, you know… Slow moving because of…
199 00:14:30.340 ⇒ 00:14:36.229 Ashwini Sharma: The connectors are not yet ready, and all we can do is work on some of the remembers data, and…
200 00:14:36.900 ⇒ 00:14:39.110 Ashwini Sharma: And the member engagement report, right?
201 00:14:41.790 ⇒ 00:14:45.240 Ashwini Sharma: Yeah, next week onwards, there will be some traction in CTA, definitely.
202 00:14:47.250 ⇒ 00:14:48.240 Uttam Kumaran: Yeah, I agree.
203 00:14:48.240 ⇒ 00:14:48.840 Ashwini Sharma: Yeah.
204 00:14:48.970 ⇒ 00:14:55.850 Ashwini Sharma: And once I’m done with QA, I think, the output of MMM? Was it MMM? Yeah, MMM.
205 00:14:55.850 ⇒ 00:14:56.490 Uttam Kumaran: Yeah.
206 00:14:56.490 ⇒ 00:15:02.270 Ashwini Sharma: Yeah, that’s going to determine whether we’ll get some extra work or not, or alright.
207 00:15:02.550 ⇒ 00:15:03.569 Ashwini Sharma: If you have any…
208 00:15:03.570 ⇒ 00:15:04.420 Uttam Kumaran: Yeah, we are.
209 00:15:04.420 ⇒ 00:15:05.330 Ashwini Sharma: Okay.
210 00:15:05.710 ⇒ 00:15:06.290 Uttam Kumaran: We are.
211 00:15:07.090 ⇒ 00:15:09.139 Uttam Kumaran: Yeah, so I’m gonna work with him next month on that.
212 00:15:09.530 ⇒ 00:15:10.220 Ashwini Sharma: Okay.
213 00:15:12.940 ⇒ 00:15:14.179 Ashwini Sharma: Good,
214 00:15:17.280 ⇒ 00:15:27.119 Ashwini Sharma: Does Eden, Eden, Eden thinks… Eden is just bugs now, on and off, so no… Oh, yeah, there is a major bug, Eden OS, right? So, some.
215 00:15:27.790 ⇒ 00:15:28.639 Uttam Kumaran: Yeah, yeah, yeah.
216 00:15:28.640 ⇒ 00:15:31.059 Ashwini Sharma: Yeah, yeah, that project is there.
217 00:15:32.060 ⇒ 00:15:36.650 Ashwini Sharma: Man, the project is there, but there is no bandwidth, I don’t know how…
218 00:15:37.630 ⇒ 00:15:42.609 Uttam Kumaran: Well, that’s the thing, I think we probably need one more data engineer and one more, analytics engineer.
219 00:15:42.910 ⇒ 00:15:43.650 Ashwini Sharma: Right.
220 00:15:48.350 ⇒ 00:15:50.340 Uttam Kumaran: Okay, alright, I’m gonna run to Pacific.
221 00:15:50.340 ⇒ 00:15:50.700 Ashwini Sharma: Excellent.
222 00:15:50.700 ⇒ 00:15:54.859 Uttam Kumaran: But, yeah, it’s good to chat, dude. Cool. So yeah, like, let’s chat again on Monday.
223 00:15:54.860 ⇒ 00:15:55.500 Ashwini Sharma: Okay, man.
224 00:15:55.500 ⇒ 00:15:56.520 Uttam Kumaran: If anything today.
225 00:15:56.680 ⇒ 00:15:57.030 Ashwini Sharma: Okay.
226 00:15:57.030 ⇒ 00:15:57.550 Uttam Kumaran: Okay.
227 00:15:57.720 ⇒ 00:15:58.050 Ashwini Sharma: Alright.
228 00:15:58.050 ⇒ 00:15:58.650 Uttam Kumaran: Okay.
229 00:15:58.820 ⇒ 00:16:00.299 Ashwini Sharma: Thanks so much, Freddy. Bye.
230 00:16:00.300 ⇒ 00:16:01.640 Uttam Kumaran: Yeah, you too. Bye.