Meeting Title: Brainforge Interview w- Demilade Date: 2026-04-01 Meeting participants: Demilade Agboola, Hussein Diab
WEBVTT
1 00:00:49.720 ⇒ 00:00:51.020 Hussein Diab: Hello, hello?
2 00:00:53.460 ⇒ 00:00:55.150 Demilade Agboola: Hi, how are you?
3 00:00:55.450 ⇒ 00:00:57.030 Hussein Diab: I’m doing well, how are you?
4 00:00:57.030 ⇒ 00:01:01.659 Demilade Agboola: I’m doing very well. Name’s Hussein, right?
5 00:01:01.960 ⇒ 00:01:03.150 Hussein Diab: I’m Hussein, yes.
6 00:01:03.150 ⇒ 00:01:08.299 Demilade Agboola: Nice to meet you, Hussein. So my name is Dimla Day, I’m with the Brainforge team.
7 00:01:09.110 ⇒ 00:01:13.760 Demilade Agboola: And I work as, analytics Engineer on the team.
8 00:01:14.420 ⇒ 00:01:23.740 Demilade Agboola: And I’m just here to talk to you, get an idea of how you solve problems, and how, that can be applied to the Brainforge.
9 00:01:24.050 ⇒ 00:01:25.140 Demilade Agboola: just.
10 00:01:25.140 ⇒ 00:01:25.820 Hussein Diab: What?
11 00:01:26.980 ⇒ 00:01:34.610 Demilade Agboola: Okay, so just, like, high level, can I get to just, like, know you and, like, your work experience and, you know, what that entails?
12 00:01:37.300 ⇒ 00:01:38.729 Hussein Diab: Would you like me to start?
13 00:01:38.730 ⇒ 00:01:39.590 Demilade Agboola: Yes, please.
14 00:01:39.910 ⇒ 00:01:40.760 Hussein Diab: Alright.
15 00:01:40.940 ⇒ 00:01:43.160 Hussein Diab: Do you mind if I share my screen?
16 00:01:43.430 ⇒ 00:01:45.329 Demilade Agboola: Oh, sure, Mike, please go ahead.
17 00:01:45.880 ⇒ 00:01:47.000 Hussein Diab: Right.
18 00:01:50.530 ⇒ 00:01:52.430 Hussein Diab: Alright, can you see my screen well?
19 00:01:53.430 ⇒ 00:01:54.899 Demilade Agboola: Oh yeah, yes I can.
20 00:01:55.510 ⇒ 00:02:00.910 Hussein Diab: Awesome. Alright, so this will just help, put a face behind the resume.
21 00:02:01.240 ⇒ 00:02:08.479 Hussein Diab: So, I’m Hussein. I’m originally from Lebanon. I’ve been in Dallas for the past, 20 years or so.
22 00:02:08.759 ⇒ 00:02:09.409 Demilade Agboola: Nice.
23 00:02:09.410 ⇒ 00:02:19.679 Hussein Diab: My background is, started with automotive, then to, medical. I have a, biomedical engineering degree.
24 00:02:19.810 ⇒ 00:02:30.269 Hussein Diab: I published some research in cancer-related research papers, and then I joined Abbott Laboratory. I started working more with data
25 00:02:31.170 ⇒ 00:02:34.139 Hussein Diab: On a, bioanalyzers.
26 00:02:34.420 ⇒ 00:02:42.349 Hussein Diab: And then the past, two, three years, I joined Analytic Vision. It’s a consulting company.
27 00:02:42.880 ⇒ 00:02:48.690 Hussein Diab: And I will tell you more about my role there. I’m a family man. I have, two kids.
28 00:02:48.980 ⇒ 00:02:50.629 Hussein Diab: I love soccer.
29 00:02:51.560 ⇒ 00:02:57.939 Hussein Diab: I know you’re originally from Nigeria, I know there’s big, soccer in Nigeria, right?
30 00:02:58.210 ⇒ 00:03:00.460 Demilade Agboola: Yeah, I’m an Arsenal fan, so…
31 00:03:00.460 ⇒ 00:03:03.560 Hussein Diab: There you go. Arsenal. Alright.
32 00:03:04.370 ⇒ 00:03:13.390 Hussein Diab: Yeah, I, I love soccer, I play, coach, and watch soccer all the time. I, play video games, I design video games.
33 00:03:14.280 ⇒ 00:03:14.820 Demilade Agboola: Nice.
34 00:03:14.820 ⇒ 00:03:20.169 Hussein Diab: I see Milan supporter, as you can see. I love jiu-jitsu, I love the beach, the mountains.
35 00:03:20.340 ⇒ 00:03:26.329 Hussein Diab: And, when I’m not building data pipelines, I’ll be in my garage building these motorcycles.
36 00:03:26.510 ⇒ 00:03:29.540 Hussein Diab: I like to design them, build them from scratch.
37 00:03:30.940 ⇒ 00:03:32.089 Demilade Agboola: Sounds pretty cool.
38 00:03:33.730 ⇒ 00:03:36.759 Demilade Agboola: Seems like a lot of hobbies, like, also.
39 00:03:36.870 ⇒ 00:03:38.870 Demilade Agboola: Brazilian Jiu-Jitsu is also really cool.
40 00:03:39.390 ⇒ 00:03:42.380 Hussein Diab: Yeah. Yeah. I do have a lot of hobbies, that’s…
41 00:03:42.800 ⇒ 00:03:46.600 Hussein Diab: That’s, that’s only stuff I listed here, I have more.
42 00:03:48.770 ⇒ 00:04:00.759 Hussein Diab: This is some of my professional resume. These are some of the clients that I’ve served. Chick-fil-A, Delta Airlines, Kimberly Clark.
43 00:04:01.050 ⇒ 00:04:10.470 Hussein Diab: A lot of Fortune 500 companies, IHG, it’s an Intercontinental Hotel, it’s a hotel chain.
44 00:04:10.890 ⇒ 00:04:12.550 Hussein Diab: I’ve done…
45 00:04:12.750 ⇒ 00:04:24.240 Hussein Diab: different type of work for every client. Some of them, they needed data modernization, some of them, they needed, help understand their game.
46 00:04:24.290 ⇒ 00:04:34.800 Hussein Diab: Some of them, they just wanted help with the dashboard. Some of them, like, fully, they needed help understanding their data stack.
47 00:04:35.390 ⇒ 00:04:40.970 Hussein Diab: So, in my current role, I wear multiple hats, I do multiple things.
48 00:04:41.080 ⇒ 00:04:47.059 Hussein Diab: During my time, I’ve touched, multiple tools.
49 00:04:47.290 ⇒ 00:04:51.450 Hussein Diab: These are some of them. So for visualization.
50 00:04:52.310 ⇒ 00:04:56.960 Hussein Diab: I’ve used Power BI, Sigma, Tableau, and Looker.
51 00:04:57.440 ⇒ 00:05:10.489 Hussein Diab: For data engineering, I’ve used Azure, AWS, I’m certified in Google Cloud, Databricks, Snowflake, dbt for transformation, Fivetran for…
52 00:05:10.590 ⇒ 00:05:19.929 Hussein Diab: orchestration, ingestion, sorry. Multiple tools within Azure, multiple tools within GCP.
53 00:05:20.120 ⇒ 00:05:25.899 Hussein Diab: I’m pretty good with Python and SQL, that’s what I mostly use.
54 00:05:26.000 ⇒ 00:05:32.319 Hussein Diab: And I use AI a lot. Anytime I need help, or I don’t know how to code something, I use AI.
55 00:05:34.730 ⇒ 00:05:36.200 Demilade Agboola: That’s fair, that’s fair.
56 00:05:36.470 ⇒ 00:05:41.780 Demilade Agboola: This is very helpful, being able to see the certifications, being able to see, like, tools.
57 00:05:42.130 ⇒ 00:05:48.289 Demilade Agboola: And just, like, what you’ve worked and what you’ve done. That’s very helpful to actually just see it.
58 00:05:50.220 ⇒ 00:05:55.920 Demilade Agboola: I think my next question would be, what would you say the most… complex.
59 00:05:56.100 ⇒ 00:05:59.980 Demilade Agboola: pipeline that you’ve worked on was, and what made it complex?
60 00:06:00.990 ⇒ 00:06:10.920 Hussein Diab: Yeah, I have a good one, I have a good one. So, I just finished a project with Kimberly Clark.
61 00:06:11.390 ⇒ 00:06:18.519 Hussein Diab: So, Kimberly Clark is, the manufacturing company, they manufacture
62 00:06:18.680 ⇒ 00:06:27.139 Hussein Diab: toilet papers and different kind of products, and they, they, sell them to Walmart and Kroger and all of these companies.
63 00:06:27.300 ⇒ 00:06:35.440 Hussein Diab: So, they were paying PWC about $2 million a year for, Promotional planning tool.
64 00:06:35.690 ⇒ 00:06:41.790 Hussein Diab: So, they hired us to replace it with an internal solution they could own and improve.
65 00:06:41.990 ⇒ 00:06:53.299 Hussein Diab: So I helped design the data pipeline and the reporting foundation. Their data stack, was Snowflake, ADF, Databricks, dbt Core.
66 00:06:53.500 ⇒ 00:06:56.610 Hussein Diab: SQL Server, and Power BI.
67 00:06:57.130 ⇒ 00:06:58.230 Hussein Diab: So…
68 00:06:58.340 ⇒ 00:07:13.519 Hussein Diab: The result was KCE was able to move off external tool sharing, and starting in 2026, they roughly saved around $2 million a year. And they have a stronger, more flexible product.
69 00:07:14.260 ⇒ 00:07:19.089 Hussein Diab: The reason this was my most complex
70 00:07:19.690 ⇒ 00:07:25.210 Hussein Diab: Project, because they were in a lot of
71 00:07:25.650 ⇒ 00:07:30.979 Hussein Diab: technologies that they needed to implement. So,
72 00:07:32.490 ⇒ 00:07:41.709 Hussein Diab: ADF will orchestrate all the data pipeline, and will run Databricks notebooks within ADF.
73 00:07:42.450 ⇒ 00:07:45.950 Hussein Diab: Inside the Databricks notebooks, we’re running dbt Core.
74 00:07:46.650 ⇒ 00:07:50.609 Hussein Diab: And we’re taking the data that lives in Snowflake.
75 00:07:51.610 ⇒ 00:07:52.890 Hussein Diab: Okay.
76 00:07:53.230 ⇒ 00:07:58.559 Hussein Diab: the pipeline will end up, putting the data in SQL Server.
77 00:07:59.100 ⇒ 00:08:06.610 Hussein Diab: And then we have a software developer that will build a web app using the SQL Server data.
78 00:08:07.350 ⇒ 00:08:15.559 Hussein Diab: the reason… This project particularly was complex, because we were working on, different environments.
79 00:08:16.290 ⇒ 00:08:26.860 Hussein Diab: And secure versus non-secure environments as well. So, data can only live in Snowflake, not in Databricks. And…
80 00:08:27.020 ⇒ 00:08:31.000 Hussein Diab: the web app can only be built using SQL Server.
81 00:08:31.650 ⇒ 00:08:32.789 Demilade Agboola: Oh.
82 00:08:35.049 ⇒ 00:08:45.560 Hussein Diab: Yeah, it’s, it was fun, challenging, a lot of sleepless nights, but I learned a lot there.
83 00:08:45.840 ⇒ 00:08:57.850 Hussein Diab: what I learned the most is about, metadata generation and the difference how, databases will handle these metadata. So, for example.
84 00:08:59.050 ⇒ 00:09:16.339 Hussein Diab: for a valuable character in Snowflake, if you set it to, 128, let’s say, and the data you have in it is only a few characters, it will automatically chunk it up for you and make it last. But SQL Server doesn’t do that.
85 00:09:16.740 ⇒ 00:09:30.379 Hussein Diab: So I had to create a Databricks notebook to automatically translate these and put the minimum size requirement. That’s why we’re not using all of the space in SQL Server.
86 00:09:30.620 ⇒ 00:09:31.460 Demilade Agboola: Interesting.
87 00:09:31.700 ⇒ 00:09:36.819 Demilade Agboola: Yeah, so, like, little architecture things that make a huge difference, like, further down the line.
88 00:09:37.350 ⇒ 00:09:38.620 Hussein Diab: Yeah. Yeah.
89 00:09:38.620 ⇒ 00:09:39.630 Demilade Agboola: Okay, that’s fair.
90 00:09:40.830 ⇒ 00:09:47.119 Demilade Agboola: Alright, so let’s just work through, like, a hypothetical, like, design situation.
91 00:09:47.640 ⇒ 00:09:53.300 Demilade Agboola: So if we had a client that just wanted to have a daily revenue, Reporting month.
92 00:09:54.140 ⇒ 00:09:57.669 Demilade Agboola: And they wanted to ingest data from Stripe.
93 00:09:58.480 ⇒ 00:10:01.870 Demilade Agboola: Salesforce and, say, Google Ads.
94 00:10:03.590 ⇒ 00:10:04.290 Hussein Diab: Okay.
95 00:10:04.290 ⇒ 00:10:13.250 Demilade Agboola: how would you go about designing the solution? What are the steps, processes, and what questions and assumptions would you be thinking of?
96 00:10:15.820 ⇒ 00:10:17.559 Hussein Diab: Let’s see.
97 00:10:18.100 ⇒ 00:10:24.510 Hussein Diab: So we have data sources from a POC,
98 00:10:24.790 ⇒ 00:10:27.279 Hussein Diab: What’s their data stack? What do they have?
99 00:10:27.830 ⇒ 00:10:33.350 Demilade Agboola: Think of it as… Any choice that you want to integrate.
100 00:10:34.060 ⇒ 00:10:40.639 Hussein Diab: Fair enough. Do they have anything set right now, or we’re starting from scratch?
101 00:10:41.590 ⇒ 00:10:42.600 Demilade Agboola: From scratch.
102 00:10:42.980 ⇒ 00:10:49.650 Hussein Diab: From scratch, okay. I would start by recommending FiveTrand as a way to ingest it.
103 00:10:50.180 ⇒ 00:10:58.130 Hussein Diab: It’s pretty straightforward, it’s not that expensive, and it works well with a lot of tools.
104 00:10:58.750 ⇒ 00:11:15.450 Hussein Diab: So, we have already connectors for multiple different tools there. So I would start by ingesting the data using FiveTran, and I would move it from, you said Salesforce, Tribe, and, something else.
105 00:11:15.800 ⇒ 00:11:35.639 Hussein Diab: But we can use Google Ads, that’s right, Google Ads. So we can… we can use Fivetran for that, and then we will put the data in a warehouse. Now, this warehouse, we’re gonna talk more about it, we’re gonna see how big their data is, what kind of compute they need…
106 00:11:36.130 ⇒ 00:11:38.000 Hussein Diab: How…
107 00:11:38.040 ⇒ 00:11:57.390 Hussein Diab: Are we going to use this data? Is it going to be for machine learning? Are we visualization… visualizing the data to understand how we can make it better? How we can increase the profit, the return of investment? So this is where I would pick the…
108 00:11:57.390 ⇒ 00:11:58.860 Hussein Diab: data warehouse.
109 00:11:58.860 ⇒ 00:12:00.110 Hussein Diab: Accordingly.
110 00:12:01.540 ⇒ 00:12:04.460 Hussein Diab: I would probably use Snowflake, because…
111 00:12:04.880 ⇒ 00:12:13.389 Hussein Diab: I like Snowflake, and I’ve been recommending it to a lot of my clients. And for visualization,
112 00:12:16.180 ⇒ 00:12:25.850 Hussein Diab: I want to ask more questions about what they’re comfortable with, and what their budget, and what they’re looking to get, but…
113 00:12:26.400 ⇒ 00:12:31.319 Hussein Diab: I would… I would stop here recommending… recommending stuff before moving forward.
114 00:12:31.480 ⇒ 00:12:44.139 Demilade Agboola: Yeah, I mean, sure, definitely. Like, I also, like, the questions you ask also lets me know, like, what you’re thinking about, so sometimes it’s not always about the recommendation, it can also be about, like, what are you thinking about?
115 00:12:44.300 ⇒ 00:12:51.070 Demilade Agboola: before you make a recommendation. So that also lets me understand, you know, What you’re considering.
116 00:12:51.180 ⇒ 00:13:03.440 Demilade Agboola: And how you’re able to think about designing, you know, whatever for whatever clients, because ultimately, the best way to design is by understanding the use case.
117 00:13:04.440 ⇒ 00:13:05.490 Hussein Diab: Definitely.
118 00:13:07.800 ⇒ 00:13:11.679 Demilade Agboola: Alright, so say we’ve built this out.
119 00:13:11.810 ⇒ 00:13:21.790 Demilade Agboola: In the MAT models, would you be looking for a more, like, star schema, or, like, normalized schema, and…
120 00:13:21.960 ⇒ 00:13:26.320 Demilade Agboola: If you were to pick anyone, like, why would you, like, lean towards that one more?
121 00:13:30.850 ⇒ 00:13:38.030 Hussein Diab: So, we’re looking at 3 different sources.
122 00:13:38.600 ⇒ 00:13:43.249 Hussein Diab: That’s me and the data might be different, it needs a lot of cleanup.
123 00:13:43.810 ⇒ 00:13:52.239 Hussein Diab: So, I would start… I think I would implement a medallion architecture. I would do, bronze, silver, gold.
124 00:13:52.830 ⇒ 00:13:53.610 Demilade Agboola: Okay.
125 00:13:53.610 ⇒ 00:13:58.830 Hussein Diab: And I will unify the sources in the bronze.
126 00:14:00.100 ⇒ 00:14:18.599 Hussein Diab: bronze layer. I’ll probably use dbt Core, since it’s free, and we can implement it pretty good without… in our tools. I’ll clean up the bronze layer, make it all nice for silver, do the joins there, and then my gold layer will be more,
127 00:14:18.750 ⇒ 00:14:23.190 Hussein Diab: They’re ready for… Machine learning or visualization.
128 00:14:23.370 ⇒ 00:14:27.589 Hussein Diab: This would look more like Star Schema?
129 00:14:28.840 ⇒ 00:14:34.879 Hussein Diab: But… I think my approach would be more to the medallion architecture.
130 00:14:35.450 ⇒ 00:14:36.240 Demilade Agboola: Okay.
131 00:14:36.560 ⇒ 00:14:38.140 Demilade Agboola: Okay, fair enough, fair enough.
132 00:14:38.930 ⇒ 00:14:47.849 Demilade Agboola: Also, let’s just say we’ve built out this entire pipeline and entire workflow, And over time.
133 00:14:48.060 ⇒ 00:14:50.489 Demilade Agboola: This model, one of the models.
134 00:14:50.630 ⇒ 00:14:54.770 Demilade Agboola: gets really large, so we have, say, a 400 million row query.
135 00:14:55.900 ⇒ 00:14:57.710 Demilade Agboola: And it’s taking forever to run.
136 00:14:58.630 ⇒ 00:15:04.770 Demilade Agboola: what steps would you go about in terms of, optimizing that dbt model?
137 00:15:04.770 ⇒ 00:15:05.590 Hussein Diab: So…
138 00:15:05.710 ⇒ 00:15:17.040 Hussein Diab: So, I would first, before I even look at compute, or DBP, or any of this, first of all, I want to understand if we are using all of this data.
139 00:15:17.440 ⇒ 00:15:29.879 Hussein Diab: If I have a dashboard that’s using 4 columns and, looking at data in 2026 only, then I don’t want to bring data from 1990s.
140 00:15:30.030 ⇒ 00:15:47.139 Hussein Diab: So that’s gonna save me a lot of data. This is… this is my first layer of check. Like, hey, are we using all of this data? If the answer is yes, we need all of this data, 400 million rows, we do need all of them. First of all, I would use incremental logic in dbt.
141 00:15:47.210 ⇒ 00:15:52.129 Hussein Diab: I wanna make sure that we are not dumping and rebuilding these tables every single time.
142 00:15:55.300 ⇒ 00:16:06.479 Hussein Diab: You wanna make sure that we are using the micros correctly in dbt. We are utilizing as much automation as we can.
143 00:16:06.610 ⇒ 00:16:19.580 Hussein Diab: If the answer is yes to all of them, then I will start looking at our compute. Maybe it’s time to increase our compute, use more workers node, have a bigger cluster size,
144 00:16:20.720 ⇒ 00:16:26.829 Hussein Diab: Then we’ll go from there. After that, maybe I will look at how often we’re ingesting the data.
145 00:16:26.970 ⇒ 00:16:40.259 Hussein Diab: If we’re ingesting it daily, and we have a lot of data more often, and dbt is running daily, then maybe we can run it more often. Maybe we can increase the parallel work instead of having,
146 00:16:40.860 ⇒ 00:16:45.900 Hussein Diab: Two workflows work at the same time, maybe we’ll increase it to 4 or 8.
147 00:16:46.380 ⇒ 00:16:50.750 Hussein Diab: I want to see how long the refresh takes.
148 00:16:51.410 ⇒ 00:17:01.199 Hussein Diab: If the refresh is taking 5 hours, then maybe it’s time to look at the semantic models, to look at the queries, see what’s the bottleneck.
149 00:17:04.150 ⇒ 00:17:05.089 Hussein Diab: Yeah.
150 00:17:05.680 ⇒ 00:17:07.510 Demilade Agboola: Okay, that’s fair.
151 00:17:08.710 ⇒ 00:17:14.349 Demilade Agboola: Yeah, that makes a lot of sense, in terms of just being able to, like, think of the different ways in which we can handle…
152 00:17:15.310 ⇒ 00:17:18.990 Demilade Agboola: like, a long-running query, or a long-running dbt model.
153 00:17:19.480 ⇒ 00:17:20.200 Hussein Diab: Yeah.
154 00:17:20.200 ⇒ 00:17:27.770 Demilade Agboola: Okay, so say you were assigned to a client who… Once a dashboard.
155 00:17:28.220 ⇒ 00:17:38.029 Demilade Agboola: but doesn’t properly define metrics. How do you go about, like, bridging the gap in terms of getting requirements on how you want to model this data?
156 00:17:38.840 ⇒ 00:17:40.869 Hussein Diab: I would ask, what’s the pain point?
157 00:17:41.320 ⇒ 00:17:43.389 Hussein Diab: What’s the pain? Why are we here?
158 00:17:44.570 ⇒ 00:17:46.979 Hussein Diab: What are we trying to achieve?
159 00:17:47.650 ⇒ 00:17:52.539 Hussein Diab: Once we understand the pain point, then we can translate that to KPIs.
160 00:17:53.470 ⇒ 00:18:02.859 Hussein Diab: So… If… I am with a bank, and they’re telling us that, our sales are…
161 00:18:03.170 ⇒ 00:18:15.629 Hussein Diab: bad customers are leaving, then I would create some kind of churn KPI to see what’s the relationship between why customers are leaving and something else.
162 00:18:16.520 ⇒ 00:18:24.599 Hussein Diab: I would look at… I would show them graphs that will show them when it started and why.
163 00:18:25.770 ⇒ 00:18:31.709 Hussein Diab: It’s all about asking the right questions, understanding their, their pain.
164 00:18:32.500 ⇒ 00:18:37.820 Hussein Diab: Like, when clients approach us as a consulting company.
165 00:18:38.440 ⇒ 00:18:49.409 Hussein Diab: they’re doing it for a reason. They have a pain that they’re trying to understand. Maybe they know the pain, maybe they don’t know the pain. So our job would be to ask them the right question and serve them well.
166 00:18:52.850 ⇒ 00:18:53.340 Demilade Agboola: Okay.
167 00:18:53.340 ⇒ 00:18:55.830 Hussein Diab: Nice watch, by the way. I really like your watch.
168 00:18:55.830 ⇒ 00:19:03.940 Demilade Agboola: Oh, you’re… I was gonna ask, do you have a…
169 00:19:04.610 ⇒ 00:19:11.790 Demilade Agboola: a process? Do you have, like, a template that you use to ask certain questions, or do you just kind of go with…
170 00:19:12.440 ⇒ 00:19:23.489 Demilade Agboola: the flow? Do you just kind of understand, like, listen to them, and you’re following the, like, or do you just have a template? Do you try to crystallize it down to a way in which you can consistently, you know, get those answers?
171 00:19:23.970 ⇒ 00:19:27.440 Hussein Diab: Definitely. I use Miro. Have you used Miro before?
172 00:19:28.180 ⇒ 00:19:28.960 Demilade Agboola: Yes, I have.
173 00:19:29.250 ⇒ 00:19:33.209 Hussein Diab: Okay, so in Miro, I have a lot of frameworks that I use.
174 00:19:33.320 ⇒ 00:19:40.419 Hussein Diab: Some of them, I created them myself, some of them, they’re, billed from my consulting company.
175 00:19:40.480 ⇒ 00:19:53.640 Hussein Diab: Some of them I read in books, and I really liked them, and I started using them. One of the framework that I have, it’s a why, how, where,
176 00:19:54.130 ⇒ 00:20:02.540 Hussein Diab: So, I will start by asking these questions. Like, the first question is, why are we here? What are we trying to achieve?
177 00:20:03.510 ⇒ 00:20:20.319 Hussein Diab: And then I will start listing, like, what is our data stack? What are we dealing with? And then I will start asking the who. Who are my stakeholders? Who are my users? Who makes the decision? Who am I trying to help?
178 00:20:20.720 ⇒ 00:20:26.929 Hussein Diab: This framework helps me zoom out and look at the big picture, and…
179 00:20:27.290 ⇒ 00:20:32.180 Hussein Diab: It’s really one of the first steps that I do when I need a client for the first time.
180 00:20:33.920 ⇒ 00:20:34.560 Demilade Agboola: Okay.
181 00:20:36.730 ⇒ 00:20:42.559 Demilade Agboola: Follow-up question so that… so now you have an idea of what they want.
182 00:20:43.480 ⇒ 00:20:47.290 Demilade Agboola: They want things to come out quickly, you know, there’s time pressure.
183 00:20:48.340 ⇒ 00:20:55.630 Demilade Agboola: But the only way in which you can think of making it come out quickly is not a scalable way. Like, it just doesn’t scale.
184 00:20:56.190 ⇒ 00:21:07.569 Demilade Agboola: How do you go about… Managing technical scalability, versus, you know, quick delivery.
185 00:21:08.880 ⇒ 00:21:11.280 Hussein Diab: So it’s all about managing the expectation.
186 00:21:11.870 ⇒ 00:21:15.589 Hussein Diab: I will tell the client, I will talk about risk and trade-off.
187 00:21:16.540 ⇒ 00:21:21.500 Hussein Diab: If we want something quick, then…
188 00:21:22.390 ⇒ 00:21:30.890 Hussein Diab: that’s mean we didn’t test it enough, we didn’t pressure test it, we didn’t, do it the right way. If something…
189 00:21:31.070 ⇒ 00:21:39.000 Hussein Diab: needs to be high quality, then we’re going to spend more time on it to deliver it better. So this is where the trade-off and the risk comes.
190 00:21:39.580 ⇒ 00:21:46.150 Hussein Diab: Tell me more, tell me more about the problem, so I can… I can help.
191 00:21:46.340 ⇒ 00:21:54.780 Demilade Agboola: I mean, it’s deliberately just, like, trying to see how you think of it, but, like, there are a lot of things when it comes to scalability, so scalability can be around…
192 00:21:55.870 ⇒ 00:21:57.150 Demilade Agboola: Oh, hmm.
193 00:21:58.270 ⇒ 00:22:03.729 Demilade Agboola: It can be around the… The… what’s it called?
194 00:22:05.140 ⇒ 00:22:14.030 Demilade Agboola: our solution, the level of QA done, for instance, like, if we need something now by… in 3 days’ time.
195 00:22:15.910 ⇒ 00:22:28.229 Demilade Agboola: the QA might not be as scalable as possible. Like, there are different scenarios, for instance, for, you know, renewals and churn definition and all of that. It’s very possible that we might overlook certain criteria for churn.
196 00:22:28.290 ⇒ 00:22:39.740 Demilade Agboola: Right? Like, or renewals. We’ve not fully embedded all the logic of every single possible scenario that can happen. So it only works for the, you know, 70% of
197 00:22:40.140 ⇒ 00:22:48.350 Demilade Agboola: You know, scenarios or use cases that occur. But, like, the edge cases, the things that happen on the side are not, like, very clear-cut.
198 00:22:48.460 ⇒ 00:22:51.840 Demilade Agboola: The business logic does not, cover that.
199 00:22:51.970 ⇒ 00:22:59.780 Demilade Agboola: It can also be from a technical infrastructure or perspective. It can be things around documentation, it can be things around
200 00:23:00.180 ⇒ 00:23:01.590 Demilade Agboola: data governance.
201 00:23:02.530 ⇒ 00:23:02.990 Hussein Diab: Yeah.
202 00:23:02.990 ⇒ 00:23:08.479 Demilade Agboola: Just ensuring that, like, the right people that, like, have… I didn’t want to have access to that, you know.
203 00:23:08.850 ⇒ 00:23:10.000 Demilade Agboola: dashboard.
204 00:23:10.300 ⇒ 00:23:12.280 Demilade Agboola: Yeah, so the idea is just, like.
205 00:23:12.690 ⇒ 00:23:15.169 Demilade Agboola: If you’re in a scenario where there is…
206 00:23:15.410 ⇒ 00:23:18.609 Demilade Agboola: pressure. Like, everyone’s just like, we need this out by tomorrow.
207 00:23:19.260 ⇒ 00:23:20.070 Hussein Diab: Okay.
208 00:23:20.070 ⇒ 00:23:23.900 Demilade Agboola: And in terms of, like, a QA, just, like, general…
209 00:23:24.170 ⇒ 00:23:27.400 Demilade Agboola: Standard project that you want to get out the door.
210 00:23:27.960 ⇒ 00:23:33.329 Demilade Agboola: You know you can’t do every single thing on your checklist to be able to get this product out by tomorrow.
211 00:23:33.440 ⇒ 00:23:35.899 Demilade Agboola: How do you just, you know, manage that?
212 00:23:36.330 ⇒ 00:23:48.939 Hussein Diab: So, my first step is, whenever I am in these situations, I will over-communicate. That means every single time I’m doing something, I’m telling the client or the team.
213 00:23:49.590 ⇒ 00:23:53.030 Hussein Diab: It’s… it’s… I’ll be saved.
214 00:23:53.600 ⇒ 00:23:55.560 Hussein Diab: Say what you’re gonna do.
215 00:23:55.800 ⇒ 00:23:57.939 Hussein Diab: And do what you’re gonna say.
216 00:23:58.240 ⇒ 00:24:01.210 Hussein Diab: Communicate what you need to be successful.
217 00:24:01.600 ⇒ 00:24:15.959 Hussein Diab: So, I would make sure that every single time I’m about to do something, I’m telling the client. When I’m getting the results, the first results, I’m communicating this. If something is not running well.
218 00:24:16.280 ⇒ 00:24:18.839 Hussein Diab: I’m not leaving the client in the dark.
219 00:24:19.040 ⇒ 00:24:29.450 Hussein Diab: This is super important. You will get an email from a client at, you know, 1AM. They’re asking you something, freaking out about the problem.
220 00:24:30.350 ⇒ 00:24:40.029 Hussein Diab: I’m not going to go and work on it at 1AM, but what I’m gonna do is I’m going to email him and tell him, hey, I did receive your email.
221 00:24:40.220 ⇒ 00:24:46.360 Hussein Diab: I will work on it tomorrow, and I will give you an answer by end of day tomorrow.
222 00:24:46.870 ⇒ 00:24:49.299 Hussein Diab: This way, the client is not left in the dark.
223 00:24:49.620 ⇒ 00:24:57.319 Hussein Diab: they… they… they feel that they’re heard. They feel like someone is listening to them. I’m validating their… their question.
224 00:24:59.240 ⇒ 00:25:00.270 Hussein Diab: End.
225 00:25:00.350 ⇒ 00:25:12.829 Hussein Diab: Then, for the scalability and all of this, I would put it in chunks. I would start working in small areas. So I would probably start with a proof of concept before ingesting a large amount of data.
226 00:25:12.830 ⇒ 00:25:20.650 Hussein Diab: look at the past few months, look at proof of concept, if this is feasible, feasibility testing.
227 00:25:21.590 ⇒ 00:25:27.220 Hussein Diab: Yeah, there’s a lot of ways you can go around it. This is… this is… you know.
228 00:25:28.970 ⇒ 00:25:30.660 Demilade Agboola: Okay, that’s fair.
229 00:25:30.980 ⇒ 00:25:36.379 Demilade Agboola: I think that was just the last question I had. I’m not sure if you have any other, like, any questions yourself, or…
230 00:25:36.980 ⇒ 00:25:37.790 Demilade Agboola: For me.
231 00:25:38.960 ⇒ 00:25:43.470 Hussein Diab: Tell me about you. What do you enjoy about this? What’s your favorite part?
232 00:25:44.120 ⇒ 00:25:46.870 Demilade Agboola: About Brain Forge or working in data, which one?
233 00:25:47.050 ⇒ 00:25:47.780 Hussein Diab: Both.
234 00:25:48.750 ⇒ 00:25:56.030 Demilade Agboola: I think my favorite part of working in data is, like, problem solving. I find it…
235 00:25:56.300 ⇒ 00:26:02.600 Demilade Agboola: Deeply gratifying to see people make decisions and have the ability to be able to
236 00:26:03.110 ⇒ 00:26:11.289 Demilade Agboola: Go from not having any visibility or any clarity in what they’re doing to being able to know, hey,
237 00:26:11.740 ⇒ 00:26:17.750 Demilade Agboola: This is the section of customers that are more likely to churn. This is the section of customers that we need to…
238 00:26:18.120 ⇒ 00:26:23.559 Demilade Agboola: there’s potentially more value in, like, sending ads to things like that. Just being able to…
239 00:26:24.860 ⇒ 00:26:32.250 Demilade Agboola: Have a certain level of clarity to how you’re operating in… Your day-to-day.
240 00:26:33.130 ⇒ 00:26:37.920 Demilade Agboola: In terms of working with Brainforge, for me, I would say it’s just the ability to…
241 00:26:41.690 ⇒ 00:26:44.220 Demilade Agboola: Work without necessarily being micromanaged.
242 00:26:44.730 ⇒ 00:26:45.780 Demilade Agboola: So, like…
243 00:26:45.910 ⇒ 00:26:51.330 Demilade Agboola: No one is on your case, like, every single hour of the day, like, hey, what’s going on? What’s happening with this?
244 00:26:51.660 ⇒ 00:27:02.159 Demilade Agboola: I mean, obviously, there is communication, we do communicate, and we do, like, sync on the things that are going on, but it’s not to the point of, like, someone is, like, constantly over your shoulder, trying to…
245 00:27:02.290 ⇒ 00:27:04.799 Demilade Agboola: Figure out what is going on with things,
246 00:27:05.590 ⇒ 00:27:13.129 Demilade Agboola: And plus, the ability to now combine what I like about working with data to multiple companies in different spaces is just a…
247 00:27:13.290 ⇒ 00:27:15.100 Demilade Agboola: That’s a beautiful thing, so…
248 00:27:15.320 ⇒ 00:27:20.150 Demilade Agboola: for me, those are the things I really enjoy about, like, working in Brainforge and working in data.
249 00:27:21.280 ⇒ 00:27:22.090 Hussein Diab: Okay.
250 00:27:22.240 ⇒ 00:27:26.780 Hussein Diab: Who… what type of clients?
251 00:27:27.280 ⇒ 00:27:29.730 Hussein Diab: juicer, and…
252 00:27:30.060 ⇒ 00:27:35.400 Hussein Diab: You don’t have to tell me the names, I know there’s NDAs, but you can just tell me what type of work.
253 00:27:37.180 ⇒ 00:27:41.470 Demilade Agboola: I mean, we work with different types of clients, so,
254 00:27:42.390 ⇒ 00:27:46.070 Demilade Agboola: Generally, we do a lot of… e-commerce?
255 00:27:46.240 ⇒ 00:27:53.750 Demilade Agboola: Right now… But that isn’t the, like, the only clients we do. We also have clients that are…
256 00:27:53.950 ⇒ 00:28:01.679 Demilade Agboola: into… like… workflow development, so, like, software, like, proper SaaSes, like, just…
257 00:28:01.820 ⇒ 00:28:06.700 Demilade Agboola: They basically… so we have some SaaSes, we have e-commerce.
258 00:28:06.810 ⇒ 00:28:12.000 Demilade Agboola: Usually in terms of size, I would say most of them are about mid.
259 00:28:12.680 ⇒ 00:28:15.460 Demilade Agboola: Small to mid-sized companies, so we don’t…
260 00:28:20.700 ⇒ 00:28:22.720 Demilade Agboola: I’m not sure if, like, his body size or something.
261 00:28:22.720 ⇒ 00:28:24.859 Hussein Diab: Sorry, you were breaking up, I couldn’t hear the last…
262 00:28:27.110 ⇒ 00:28:37.160 Demilade Agboola: Oh, I have no idea why that happened. Yeah, so I was just saying that, yes, most of the companies are usually, like, mid, just, like, small to mid company sizes, usually, like, Series B+, sort of that.
263 00:28:37.160 ⇒ 00:28:37.710 Hussein Diab: Okay.
264 00:28:38.930 ⇒ 00:28:46.980 Demilade Agboola: And yeah, it’s usually… we’re coming in most of the time to usually either beat the data team, and just be like, hey, this is what we’re gonna do, and take over.
265 00:28:47.120 ⇒ 00:28:54.939 Demilade Agboola: Or, in some cases, they might have attempted a little bit of data, but there aren’t, like, strong data principles in place.
266 00:28:55.570 ⇒ 00:28:56.260 Hussein Diab: Okay.
267 00:28:56.500 ⇒ 00:29:00.830 Hussein Diab: What’s your favorite project you worked on so far?
268 00:29:03.880 ⇒ 00:29:07.030 Demilade Agboola: I think for me, it probably would just be…
269 00:29:08.520 ⇒ 00:29:11.500 Demilade Agboola: So we work with, pharmaceuticals.
270 00:29:13.780 ⇒ 00:29:21.560 Demilade Agboola: And for me, just being able to help the MC Like, the value in… the ads.
271 00:29:21.820 ⇒ 00:29:32.119 Demilade Agboola: Customer groups, and being able to link those two things together to know who to target more and who potentially could drive more revenue is probably one of the coolest things.
272 00:29:32.590 ⇒ 00:29:33.790 Demilade Agboola: Okay. Okay.
273 00:29:35.100 ⇒ 00:29:36.520 Hussein Diab: Yeah, I like that.
274 00:29:39.640 ⇒ 00:29:42.189 Hussein Diab: Oh, we’re running out of time. Yeah.
275 00:29:42.190 ⇒ 00:29:44.089 Demilade Agboola: Right. We’re right on time right now.
276 00:29:44.090 ⇒ 00:29:44.740 Hussein Diab: Yeah.
277 00:29:45.140 ⇒ 00:29:49.340 Demilade Agboola: And I’m also… I have a hard stop, so I have to hop to another meeting right now.
278 00:29:49.730 ⇒ 00:29:50.670 Hussein Diab: Anderson.
279 00:29:50.860 ⇒ 00:30:00.829 Demilade Agboola: Alright then, this was great. Thanks for taking time to talk to me. I’ll be sure to reach out to the HR, and then, you know, they’ll take the next steps from there.
280 00:30:01.480 ⇒ 00:30:03.200 Hussein Diab: Good. Nice to meet you.
281 00:30:03.200 ⇒ 00:30:04.930 Demilade Agboola: Nice to meet you. Thank you.