Meeting Title: Brainforge Data Engineer Interview Date: 2026-03-06 Meeting participants: Awaish Kumar, Ajibade Adeleke
WEBVTT
1 00:00:47.160 ⇒ 00:00:47.910 Awaish Kumar: Hi.
2 00:00:48.930 ⇒ 00:00:49.520 Ajibade Adeleke: Hello.
3 00:00:52.910 ⇒ 00:00:57.010 Awaish Kumar: I’m good. I’m not… okay, I’m… I can see you now.
4 00:00:57.340 ⇒ 00:00:59.880 Awaish Kumar: How do you… how can I pronounce your name?
5 00:01:01.640 ⇒ 00:01:02.899 Ajibade Adeleke: Yeah, I’m…
6 00:01:05.510 ⇒ 00:01:06.180 Awaish Kumar: Sunday?
7 00:01:08.200 ⇒ 00:01:09.800 Ajibade Adeleke: Sai, please give me a minute.
8 00:01:19.600 ⇒ 00:01:20.840 Ajibade Adeleke: Zach, can you hear me?
9 00:01:21.820 ⇒ 00:01:26.120 Awaish Kumar: Yeah, I can hear you. I said, how can I pronounce your name?
10 00:01:26.430 ⇒ 00:01:30.979 Ajibade Adeleke: Hi, my name is Ajivo, you can call me AJ, for short.
11 00:01:31.220 ⇒ 00:01:41.060 Awaish Kumar: NG, okay, so, nice to meet you, AJ. We are… this is an introductory interview, like, the…
12 00:01:41.250 ⇒ 00:01:53.470 Awaish Kumar: In this interview, we are just going to talk a little bit about myself and the company, and then we are going to deep dive into your expertise and what you’ve been doing so far in your career.
13 00:01:55.680 ⇒ 00:01:58.440 Awaish Kumar: Yeah, that’s… That’s the agenda.
14 00:01:58.860 ⇒ 00:02:05.240 Awaish Kumar: And, yeah, to… from… for myself, like, I’m Aishk Kumar, and I’ve been,
15 00:02:06.100 ⇒ 00:02:10.450 Awaish Kumar: I’m here at Brainforge, working as a data engineering lead.
16 00:02:11.090 ⇒ 00:02:18.319 Awaish Kumar: And, and the Brainforge is a, like, the data and AI consultancy services company.
17 00:02:18.440 ⇒ 00:02:24.599 Awaish Kumar: Provides consultancy services to… Mid to large enterprise, enterprises.
18 00:02:25.080 ⇒ 00:02:37.509 Awaish Kumar: And most of our clients are U.S.-based, but the employees are all from all over the world. So we have people working from Europe, US,
19 00:02:38.310 ⇒ 00:02:43.450 Awaish Kumar: Asia… So, from everywhere, and we work remotely.
20 00:02:43.590 ⇒ 00:02:44.860 Awaish Kumar: And,
21 00:02:45.700 ⇒ 00:02:55.880 Awaish Kumar: yeah, that’s basically it. And, yeah, for the… for the, engagement part, we basically do some type of overlapping with the U.S.
22 00:02:56.570 ⇒ 00:03:00.270 Ajibade Adeleke: hours, because, like, the Eastern hours, mostly.
23 00:03:00.270 ⇒ 00:03:04.000 Awaish Kumar: Because our… most of our clients are in the same time zone.
24 00:03:04.720 ⇒ 00:03:05.530 Awaish Kumar: Okay.
25 00:03:05.980 ⇒ 00:03:12.559 Awaish Kumar: Okay, that’s all for the brain forge. So, can you please introduce yourself?
26 00:03:13.500 ⇒ 00:03:16.729 Ajibade Adeleke: Okay, thank you very much, thank you for the opportunity.
27 00:03:17.020 ⇒ 00:03:24.160 Ajibade Adeleke: My name is Ajuvade, or you can call me AG for short, right? I’m a data and analytics engineer
28 00:03:24.320 ⇒ 00:03:28.750 Ajibade Adeleke: With over 5 years of experience building end-to-end data systems.
29 00:03:30.830 ⇒ 00:03:41.600 Ajibade Adeleke: augmented messy data into trusted… trusted reporting… government reporting layers, right? In my current tool, I own over 20 analytics systems.
30 00:03:41.850 ⇒ 00:03:47.210 Ajibade Adeleke: across FinTech, HPAC, and public sector clients, right, from the…
31 00:03:48.530 ⇒ 00:03:52.050 Ajibade Adeleke: pipeline to building… The pricing is…
32 00:03:52.050 ⇒ 00:03:53.760 Awaish Kumar: Cutting out, so…
33 00:03:54.600 ⇒ 00:03:56.920 Ajibade Adeleke: oh my no, can you hear me?
34 00:03:59.090 ⇒ 00:04:04.060 Awaish Kumar: I can hear you, but sometimes during when you’re talking, it just… Goods.
35 00:04:04.360 ⇒ 00:04:05.370 Awaish Kumar: Oh, yeah.
36 00:04:05.650 ⇒ 00:04:06.300 Ajibade Adeleke: Okay.
37 00:04:06.500 ⇒ 00:04:10.519 Ajibade Adeleke: Apologies on that, I’m probably having some technicalities.
38 00:04:11.140 ⇒ 00:04:16.970 Awaish Kumar: And also, maybe… Change your AirPods, maybe? I… It’s…
39 00:04:18.070 ⇒ 00:04:18.899 Ajibade Adeleke: Okay.
40 00:04:20.810 ⇒ 00:04:25.090 Awaish Kumar: Yeah, can I just… yeah, this is…
41 00:04:25.090 ⇒ 00:04:27.039 Ajibade Adeleke: Oh, by no, he understand.
42 00:04:27.040 ⇒ 00:04:28.069 Awaish Kumar: Swear to know.
43 00:04:28.550 ⇒ 00:04:40.860 Ajibade Adeleke: Okay, awesome, awesome. So, apologies for that. Again, starting over again, my name is Ajibadi, but you can call me AJ for short. I’m a data and analytics engineer with over 5…
44 00:05:07.090 ⇒ 00:05:11.230 Ajibade Adeleke: Hello, can you hear me? Yeah, sorry for that, I had to change my network.
45 00:05:12.620 ⇒ 00:05:13.640 Awaish Kumar: Okay,
46 00:05:16.020 ⇒ 00:05:18.950 Awaish Kumar: Okay… And what?
47 00:05:27.350 ⇒ 00:05:28.740 Ajibade Adeleke: Hello, can you hear me?
48 00:05:28.910 ⇒ 00:05:29.820 Awaish Kumar: Yes.
49 00:05:29.820 ⇒ 00:05:37.770 Ajibade Adeleke: Yeah, so apologies for that, I had to change my network. My Starlink is having some technical issues, so I had to switch some network. Hope that’s fine.
50 00:05:38.090 ⇒ 00:05:39.510 Awaish Kumar: Okay, yep, that’s fine.
51 00:05:39.750 ⇒ 00:05:43.120 Ajibade Adeleke: Hi, sorry about that. Can you hear me clearly very well?
52 00:05:43.120 ⇒ 00:05:44.300 Awaish Kumar: Yeah, I can now.
53 00:05:44.750 ⇒ 00:06:04.619 Ajibade Adeleke: Okay, okay, perfect. So, as I was saying, my name is Aji Padi, but you can call me AJ for short. I’m a data and analytics engineer with over 5 years building end-to-end data systems that transform raw, fragmented, messy data into trusted government reporting layer. In my current role.
54 00:06:04.690 ⇒ 00:06:23.950 Ajibade Adeleke: I own over 20 analytics systems across fintech, ed tech, and public sector clients, you know, from designing ingestion pipeline, to building dbt model mats, and building business intelligence reports that basically cut your manual reporting by 60%.
55 00:06:23.950 ⇒ 00:06:34.419 Ajibade Adeleke: What excites me about Going Forge is a consultancy model. I mean, you are not just building pipelines, you’re embedding with the client to actually solve their business problem.
56 00:06:34.530 ⇒ 00:06:48.630 Ajibade Adeleke: And that is exactly how I work, you know? I partner across functionality with finance, HR, compliance, operations, and product leaders to translate their questions into a scalable data solution.
57 00:06:48.650 ⇒ 00:07:01.059 Ajibade Adeleke: One of my quarter’s achievements is centralizing over $1.7 billion financial claim into a single analytics layer, by which reduces the cancellation by 35%,
58 00:07:01.060 ⇒ 00:07:05.750 Ajibade Adeleke: I’ve also worked on, analytics SLE dashboard.
59 00:07:05.750 ⇒ 00:07:23.129 Ajibade Adeleke: that basically improves it… improves compliance by 22%. And most recently, I recently designed a DBT mat for work, for a lending payment, financial platform that basically oversees their work, their analytics layer.
60 00:07:24.460 ⇒ 00:07:35.179 Ajibade Adeleke: How I like to work is, I ship, I deliverables first, I proactively update stakeholders, and I’m obsessed with data quality and observability.
61 00:07:36.960 ⇒ 00:07:42.670 Awaish Kumar: So, while… Like, are you working at a consultancy right now?
62 00:07:44.320 ⇒ 00:07:55.310 Ajibade Adeleke: Yeah, yeah, so my coins, well, my current company, they deal with, basically, data and digital transformation to companies. So, basically, it offers companies data and digital transformation.
63 00:07:57.590 ⇒ 00:08:00.889 Awaish Kumar: Okay, and and where are you located right now?
64 00:08:01.480 ⇒ 00:08:03.690 Ajibade Adeleke: I’m currently located in Lagos, Nigeria.
65 00:08:04.820 ⇒ 00:08:06.190 Awaish Kumar: Nigeria, okay.
66 00:08:06.320 ⇒ 00:08:14.399 Awaish Kumar: And so, like, can you just take one project as an example?
67 00:08:15.260 ⇒ 00:08:18.850 Awaish Kumar: Where you have built complex data pipelines.
68 00:08:19.230 ⇒ 00:08:23.889 Awaish Kumar: And Love walked me through on what tools and technologies used, how…
69 00:08:24.290 ⇒ 00:08:29.340 Awaish Kumar: How did you plan that project, and then how, basically.
70 00:08:30.110 ⇒ 00:08:36.039 Awaish Kumar: What the team looked like, what was your role, how that project… And it…
71 00:08:36.250 ⇒ 00:08:38.410 Awaish Kumar: And what was your end deliverable?
72 00:08:39.450 ⇒ 00:08:51.690 Ajibade Adeleke: Okay, you know, over time, based on my experience… so what’s… so to begin with, what’s basically unique about my experience is because, you know, I’ve worked across business units, right?
73 00:08:51.690 ⇒ 00:09:08.920 Ajibade Adeleke: in the country where I work, it’s part of a business unit of a larger company. So, you know, business name basically means that what it almost acts like a separate client engagement. We have different data sources, different stakeholders, and different KPIs. So that has basically made me adaptable.
74 00:09:08.970 ⇒ 00:09:12.740 Ajibade Adeleke: So one of the… one of the projects that comes into my mind is recently
75 00:09:12.920 ⇒ 00:09:25.270 Ajibade Adeleke: building a… a analytics system for a lending payment app, which is called Advancedly. So basically, they do not have any,
76 00:09:25.550 ⇒ 00:09:43.009 Ajibade Adeleke: any scalable reporting infrastructure in place. They are basically doing a manual process, that is, an Excel report, right? So, there’s no how they’ll be able to, look deeper into the analytics about, okay, which users are likely to pay back to the pace of loan
77 00:09:43.010 ⇒ 00:09:57.259 Ajibade Adeleke: which users are more likely for us to dispose some loan, right? So this is where my team comes in. Now, based on the… and that is just based on the business problem side of things. Now, for the solution side of things,
78 00:09:57.350 ⇒ 00:10:12.879 Ajibade Adeleke: Now, their… their transactional process is hosted in a Postgres Square database. So, Fivetran is used as an ingestion pipeline to move that data across into a, what, into a BigQuery data warehouse.
79 00:10:12.980 ⇒ 00:10:31.830 Ajibade Adeleke: Now, when that data lands into a BigQuicker warehouse, then DBT is used for transformation. So we… we looked at several, metrics, based on, okay, how many… what’s the, the payment metric, what’s the, okay, what’s the conceptivity.
80 00:10:31.830 ⇒ 00:10:47.249 Ajibade Adeleke: the payment, loan, I mean, is there a customer repaying their metrics every month? Which customer are more likely to miss their payment, and stuff like that. So, in my own case, I serve as a, what, as a sole data.
81 00:10:47.730 ⇒ 00:10:57.540 Ajibade Adeleke: Sole data, parsing, because, it is a startup, so there’s a lot of ownership involved, and most of this work goes unsupervised.
82 00:10:57.540 ⇒ 00:11:15.850 Ajibade Adeleke: And that’s just part of things. You know, there’s also… there are other some projects where we work with some public sector, a government public sector, where, you know, we’re building power apps, which is basically… so this thing… this is why I partner with the product manager, the product design, to come up with the YFML.
83 00:11:15.850 ⇒ 00:11:19.009 Awaish Kumar: How do you optimize your queries for BigQuery?
84 00:11:19.950 ⇒ 00:11:28.020 Ajibade Adeleke: How do I optimize my query? So, first thing, first thing I like to say is, I avoid using correlated short queries.
85 00:11:28.310 ⇒ 00:11:45.129 Ajibade Adeleke: Now, because correlated subway is an expensive query, that is, they basically… for each group being on, the correlated subway is being on, right? And also, I also use indexing. Indexing based on, most of the, the most queried
86 00:11:45.130 ⇒ 00:11:48.499 Ajibade Adeleke: attributes. I also use…
87 00:11:48.500 ⇒ 00:11:50.749 Awaish Kumar: How do you use indexing in BigQuery?
88 00:11:51.820 ⇒ 00:12:06.369 Ajibade Adeleke: Okay, so basically, using index in BigQuery, this is where you set up, it’s just like, it’s almost the same functionality as PostgreSQL, where you basically create… assign it an index to a particular column.
89 00:12:07.820 ⇒ 00:12:08.270 Awaish Kumar: Okay.
90 00:12:08.270 ⇒ 00:12:19.009 Ajibade Adeleke: Or, in a sense, also by partitioning as well. So I also use partitioning based on, if it is a timestamp dataset, I use partitioning based on a payload window.
91 00:12:20.110 ⇒ 00:12:24.950 Awaish Kumar: But can you, assign index in the BigQuery column?
92 00:12:26.100 ⇒ 00:12:31.079 Ajibade Adeleke: So… so on that,
93 00:12:31.390 ⇒ 00:12:36.009 Ajibade Adeleke: This is… so actually, on that note,
94 00:12:36.290 ⇒ 00:12:47.340 Ajibade Adeleke: you can… you can actually set it up, right? So, this is… I remember, it was set up by my manager at that time, right? But,
95 00:12:47.600 ⇒ 00:12:55.809 Ajibade Adeleke: I really do not… I’m only used to setting up an index to an attribute in a Postgres sequel, but in BigQuery, not so much.
96 00:12:56.590 ⇒ 00:13:04.979 Awaish Kumar: Okay, how has your experience look like in Vickery? Like, do you find yourself expert, advanced.
97 00:13:05.210 ⇒ 00:13:08.040 Awaish Kumar: Or intermediate or junior level.
98 00:13:08.530 ⇒ 00:13:11.290 Ajibade Adeleke: I’ll say… I’ll say intermediate.
99 00:13:12.970 ⇒ 00:13:20.120 Awaish Kumar: Okay, okay, and do you have any experience with any other data warehouses? They’d be curious.
100 00:13:21.110 ⇒ 00:13:26.739 Ajibade Adeleke: I have also worked one of our clients, where we had to…
101 00:13:26.930 ⇒ 00:13:33.520 Ajibade Adeleke: build a data infrastructure for using snowflakes, right? But.
102 00:13:33.520 ⇒ 00:13:34.110 Awaish Kumar: What do you need for…
103 00:13:34.110 ⇒ 00:13:40.589 Ajibade Adeleke: Snowflake is just… so, for myself, so in Snowflakes, I’ll say a beginner to intermediate learning.
104 00:13:40.730 ⇒ 00:13:50.600 Ajibade Adeleke: Right, so this is just… what I just basically did in Snowflake is, applying a dbt transformation layer on top of Snowflake’s data warehouse.
105 00:13:51.870 ⇒ 00:13:52.630 Awaish Kumar: Okay.
106 00:13:52.970 ⇒ 00:13:54.090 Awaish Kumar: Hi.
107 00:13:55.140 ⇒ 00:13:58.329 Awaish Kumar: How did you rate yourself, then, in using dbt?
108 00:13:59.390 ⇒ 00:14:01.930 Ajibade Adeleke: Indigen, I’m very proficient in using DBT.
109 00:14:02.630 ⇒ 00:14:03.660 Awaish Kumar: Okay.
110 00:14:03.990 ⇒ 00:14:06.540 Awaish Kumar: So, what are the seeds in DVD?
111 00:14:07.910 ⇒ 00:14:14.689 Ajibade Adeleke: So, what are these seats? So, I think, when we say seats, do you mean.
112 00:14:14.900 ⇒ 00:14:16.220 Awaish Kumar: DVTs.
113 00:14:16.900 ⇒ 00:14:18.400 Awaish Kumar: DBT seeds.
114 00:14:19.350 ⇒ 00:14:31.419 Ajibade Adeleke: So, DBT seed, I think, when we mentioned seed, I think this involves, I think something like traits, right? So DBT traits. So, that means, I think we have…
115 00:14:32.000 ⇒ 00:14:42.350 Ajibade Adeleke: Even when it doesn’t… I hardly work on seeding, dbt, but…
116 00:14:43.590 ⇒ 00:14:47.959 Ajibade Adeleke: I… I do not know. I don’t. I can’t… I can’t remember what…
117 00:14:47.960 ⇒ 00:14:51.050 Awaish Kumar: Have you worked on macros and DVD?
118 00:14:51.050 ⇒ 00:14:52.840 Ajibade Adeleke: Yes, yes, I’ve worked on Marcus.
119 00:14:53.120 ⇒ 00:14:55.710 Ajibade Adeleke: So, macro are basically a usable function.
120 00:14:56.860 ⇒ 00:15:02.690 Awaish Kumar: Yeah, but, okay. And, yeah, so,
121 00:15:03.080 ⇒ 00:15:14.030 Awaish Kumar: And yeah, so that’s it. My next question is, like, once you create your dashboard or a model, how…
122 00:15:14.460 ⇒ 00:15:18.870 Awaish Kumar: How do you convey… communicate that with the stakeholders?
123 00:15:19.560 ⇒ 00:15:29.269 Ajibade Adeleke: Alright, so on that, based on my experience, I’ve all… I’ve interacted a lot with stakeholders, right? Most of the BI tool abuse is Power BI.
124 00:15:29.340 ⇒ 00:15:42.489 Ajibade Adeleke: Right? Now, when I’m working on the analytics, that is the BI report side of things, I make sure that when I’m building a report, it should be what… it should be… it should be able to communicate itself.
125 00:15:42.490 ⇒ 00:16:02.050 Ajibade Adeleke: Right? I don’t need to come in to start experiencing some KPI metrics, some metrics charts, and all. So, you know, a stakeholder might say, hey, we want to see, payloads, and… or we want to see some certain KPI. And one of the, tricks, or I would say advice I like using for my report is.
126 00:16:02.050 ⇒ 00:16:14.660 Ajibade Adeleke: having a table chat, because I noticed stakeholders like table chat a lot, so that also, you know, it gives them a whole column and role-level view of what… of their… of their, summarized
127 00:16:14.790 ⇒ 00:16:16.580 Ajibade Adeleke: Yes.
128 00:16:18.100 ⇒ 00:16:23.699 Awaish Kumar: Okay, have you experienced… do you have experience building decks for the stakeholders?
129 00:16:24.230 ⇒ 00:16:26.139 Ajibade Adeleke: Yes, ducks, yes, yes.
130 00:16:26.330 ⇒ 00:16:30.329 Ajibade Adeleke: I’m proficient with Power BI a lot. So, you know, I know it…
131 00:16:31.060 ⇒ 00:16:37.139 Awaish Kumar: In the spread… in the, like, the slides, the PowerPoint or Google Slides.
132 00:16:37.340 ⇒ 00:16:52.620 Ajibade Adeleke: Okay, okay, good slide, yes, yes, presentation, yes. Presentation is also involved. So, I remember one of the… there was one, one client we wanted to work with, a government body client. So, we needed to, they gave us one of their, what, one of their data.
133 00:16:52.620 ⇒ 00:17:11.770 Ajibade Adeleke: to make sense of it, you know? I think that was one of the robust reports I’ve ever worked on. It has about 35 pages. It’s a 35-page report. So, you know, these things, we were able to present it, and they liked it. So that was how we were able to actually, get to those clients and build their analytics layer across their 10 products.
134 00:17:12.380 ⇒ 00:17:21.839 Awaish Kumar: Okay. And, well, for example, How do you, Basically.
135 00:17:22.210 ⇒ 00:17:26.689 Awaish Kumar: If there is a disagreement with your stakeholder.
136 00:17:28.530 ⇒ 00:17:35.599 Awaish Kumar: On your findings, for example, you have worked on a model or a chart, or something.
137 00:17:35.840 ⇒ 00:17:50.340 Awaish Kumar: and your… your stakeholder disagrees with your findings. How… but… but your data shows exactly what you see. How do you convey, convince him? How do you defend yourself?
138 00:17:51.530 ⇒ 00:17:59.340 Ajibade Adeleke: Alright, that’s a very good question. There’s always an infamous saying, after God, trust in data.
139 00:17:59.800 ⇒ 00:18:19.699 Ajibade Adeleke: So first, for me to be able to confidently, convey my findings, accurate, right? So this involves me putting things in play, that is data validity, data quality checks, and all. So, there are some cases where, you know, stakeholders might disagree, but then the data speaks to itself.
140 00:18:19.800 ⇒ 00:18:34.990 Ajibade Adeleke: So in this sense, so I’m always… when I’m talking about my findings, I speak with confidence because, when I’m working on a project, the first thing I like to do is understand the business requirements. After understanding the business requirement, I understand the data.
141 00:18:35.100 ⇒ 00:18:53.310 Ajibade Adeleke: So, understanding the data in and out, you know, when stakeholders are busy… when we are communicating, and they’re asking me, oh, their data is not this, I’m always able to tell them that, hey, this data is not… the format of the data we currently have cannot measure what we want at the moment.
142 00:18:54.360 ⇒ 00:19:04.209 Ajibade Adeleke: So, you know, it’s always based on, annotative process, i.e. communication, a very, transparent communication, and, you know, the data speaks for itself as well.
143 00:19:07.190 ⇒ 00:19:07.920 Awaish Kumar: Okay.
144 00:19:08.280 ⇒ 00:19:20.310 Awaish Kumar: And if there is a conflict in the team on… on some… Technical solution, right?
145 00:19:21.230 ⇒ 00:19:28.300 Awaish Kumar: For example, you came up with a technical solution for one of the projects, and… And somebody disagrees, or…
146 00:19:29.520 ⇒ 00:19:34.940 Awaish Kumar: questions, like, within your team. How you then,
147 00:19:35.620 ⇒ 00:19:40.170 Awaish Kumar: Like, the… how do you handle the communication with the… With the team members.
148 00:19:40.730 ⇒ 00:19:49.690 Ajibade Adeleke: Okay, so first, even despite, half, half years of my experience has always been an ownership role.
149 00:19:49.900 ⇒ 00:20:04.219 Ajibade Adeleke: And also, before that, I’ve always been also a team player, you know? I’ve managed teams of, data teams, I’ve led them on some projects. So, one thing is, if there’s always a disagreement, you know, I’m open to challenges, so I see those
150 00:20:04.220 ⇒ 00:20:20.200 Ajibade Adeleke: opinion as a second challenge, you know, okay, is this optimal way of doing things? How is it better? This, this second opinion, how is it better than a current solution being offered? So, you know, I know the saying, two truths can coexist.
151 00:20:20.250 ⇒ 00:20:30.499 Ajibade Adeleke: Right? So this is just me comparing and finding the best way to actually approach a goal. I’m always open to feedback for my team,
152 00:20:30.600 ⇒ 00:20:36.129 Ajibade Adeleke: always open to their opinions, you know. You know, it also improves this learning.
153 00:20:38.820 ⇒ 00:20:39.580 Awaish Kumar: Okay.
154 00:20:40.320 ⇒ 00:20:44.010 Awaish Kumar: Okay, I think that’s it from my side.
155 00:20:44.280 ⇒ 00:20:46.230 Awaish Kumar: Did you have any other questions?
156 00:20:47.070 ⇒ 00:20:53.800 Ajibade Adeleke: I understand that, Brain Forge works under a consultancy name, so…
157 00:20:53.850 ⇒ 00:21:04.120 Ajibade Adeleke: Most… what does your, probably your data pipeline usually looks like for different clients? Is it… is it always almost the same thing? I mean…
158 00:21:04.120 ⇒ 00:21:17.450 Ajibade Adeleke: I know, you know, since it is in a consultancy model, you know, there’s always an opportunity to work with defense tax, but what is that one SIM tax that is being advised? I mean, as a consultant, you really don’t want to advise on.
159 00:21:17.450 ⇒ 00:21:25.919 Ajibade Adeleke: their data infrastructure. So, I would say that, what do you always… how would you actually advise most clients and, you know.
160 00:21:25.990 ⇒ 00:21:28.410 Awaish Kumar: I would usually advise them staying on?
161 00:21:28.410 ⇒ 00:21:31.670 Ajibade Adeleke: setting up their data infrastructure. I would like to know that from them.
162 00:21:33.070 ⇒ 00:21:38.739 Awaish Kumar: So… For each client, we basically…
163 00:21:38.940 ⇒ 00:21:46.469 Awaish Kumar: Do some discovery sessions, where we talk to them, understand their pain points, understand the data volume.
164 00:21:46.860 ⇒ 00:21:50.229 Awaish Kumar: And, understand what,
165 00:21:51.330 ⇒ 00:22:08.509 Awaish Kumar: they are looking for in terms of… in the current scenario, but also in the future, and how the data are going to grow. So, keeping all these things in mind, and what data… yeah, and also the… what kind of different data sources we have to…
166 00:22:09.150 ⇒ 00:22:18.070 Awaish Kumar: bring data from, and things like that. So we basically… keeping all the… full scenario.
167 00:22:18.680 ⇒ 00:22:19.950 Awaish Kumar: In the view.
168 00:22:20.270 ⇒ 00:22:23.350 Awaish Kumar: And then we recommend tools,
169 00:22:23.770 ⇒ 00:22:27.810 Awaish Kumar: One by one, like, what we should use for ingestation.
170 00:22:28.010 ⇒ 00:22:34.300 Awaish Kumar: If there is an orchestration tool needed, how should we use it? If there are some CRP,
171 00:22:34.420 ⇒ 00:22:44.989 Awaish Kumar: two platforms needed, how can we use one? What BigQuery… what warehouse should we use, or what… how we process the data?
172 00:22:45.130 ⇒ 00:22:55.650 Awaish Kumar: that is common, like, DBT is the common processing layer for all the clients, normally, but then, how we are going to use,
173 00:22:55.990 ⇒ 00:23:01.340 Awaish Kumar: the BI tools, so this all depends, on the…
174 00:23:01.530 ⇒ 00:23:13.590 Awaish Kumar: the input we get from client. So, based on that, we are going to recommend the most optimal tools for the client’s use case, and what might, like, solve their needs.
175 00:23:14.300 ⇒ 00:23:16.909 Awaish Kumar: And that’s the reason we have…
176 00:23:17.160 ⇒ 00:23:31.289 Awaish Kumar: clients with multiple tech stack, although we have recommended the two tech stack to them. We have laid down the foundation, but still it varies for each client, because the use case for each client is different.
177 00:23:32.710 ⇒ 00:23:35.440 Ajibade Adeleke: Okay, okay, okay, that sounds very interesting.
178 00:23:36.300 ⇒ 00:23:37.620 Ajibade Adeleke: That sounds interesting.
179 00:23:38.080 ⇒ 00:23:38.750 Awaish Kumar: Okay.
180 00:23:40.980 ⇒ 00:23:46.100 Ajibade Adeleke: So, I think… I think that’s all for me. That’s all for me. That was… that was perfectly explained.
181 00:23:47.240 ⇒ 00:23:48.979 Awaish Kumar: Okay, yeah, thank you.
182 00:23:49.230 ⇒ 00:23:59.929 Awaish Kumar: for your time, I think it’s the end of the interview, I’m going to get my feedback back to the team, and they’re going to reach out to you.
183 00:24:02.110 ⇒ 00:24:06.500 Awaish Kumar: Like, maybe, maybe once, you know, in a week’s time, or something like that.
184 00:24:08.260 ⇒ 00:24:09.870 Ajibade Adeleke: confidence, right?
185 00:24:10.330 ⇒ 00:24:11.689 Ajibade Adeleke: Thank you for your time.