Meeting Title: Brainforge Interview w- Awaish Date: 2026-03-31 Meeting participants: Sebastien Henry, Awaish Kumar
WEBVTT
1 00:00:31.620 ⇒ 00:00:33.180 Sebastien Henry: Good afternoon.
2 00:00:34.250 ⇒ 00:00:34.870 Awaish Kumar: Hi.
3 00:00:36.690 ⇒ 00:00:37.570 Sebastien Henry: How’s it going?
4 00:00:38.270 ⇒ 00:00:40.460 Awaish Kumar: How are you doing?
5 00:00:41.390 ⇒ 00:00:43.229 Sebastien Henry: I’m good, thanks! What about you?
6 00:00:43.770 ⇒ 00:00:52.569 Awaish Kumar: I’m also good, okay, so for this interview, I…
7 00:00:52.820 ⇒ 00:00:58.389 Awaish Kumar: It’s a kind of introduction… introductory session between getting to know you.
8 00:00:58.580 ⇒ 00:01:07.220 Awaish Kumar: And, like, projects you have been working on. And also, it’s for… it’s an opportunity for you to ask any questions you have.
9 00:01:07.410 ⇒ 00:01:09.130 Awaish Kumar: Regarding brain foods.
10 00:01:09.890 ⇒ 00:01:10.530 Sebastien Henry: Okay?
11 00:01:13.570 ⇒ 00:01:23.370 Awaish Kumar: Okay, I can briefly introduce myself and the Brain Forge. So, my name is Avesh Kumar. I’m kind of leading data engineering.
12 00:01:23.750 ⇒ 00:01:30.370 Awaish Kumar: at Brainforge, And I have around 10 years of experience working as a data engineer.
13 00:01:31.100 ⇒ 00:01:34.899 Awaish Kumar: I’ve been working with startups, growth stage companies, leading to IPO.
14 00:01:35.160 ⇒ 00:01:42.550 Awaish Kumar: our experience, I have been kind of mentoring team members, building teams.
15 00:01:44.390 ⇒ 00:01:51.060 Awaish Kumar: Basically, doing hands-on engineering work, building… laying down the data foundations for startups.
16 00:01:52.350 ⇒ 00:01:56.539 Awaish Kumar: Yeah, this is basically what I’ve been doing here.
17 00:01:56.820 ⇒ 00:01:59.269 Awaish Kumar: And, for…
18 00:01:59.350 ⇒ 00:02:12.190 Awaish Kumar: BrainForge, the Brainforges data and AI consultancy firm, providing AI and data services to mid- to large-scale enterprises in the United States.
19 00:02:12.240 ⇒ 00:02:21.150 Awaish Kumar: most, like, obviously we are open to get clients from all over the world, but majority of our clients are right now from the United States.
20 00:02:21.240 ⇒ 00:02:27.499 Awaish Kumar: And… but we are a completely remote company. We have our employees, from all over the world.
21 00:02:29.140 ⇒ 00:02:35.510 Awaish Kumar: Operate 100% remote, and… And the mode of communications are, like, Slack.
22 00:02:36.060 ⇒ 00:02:45.159 Awaish Kumar: building as much documentation as possible, and using Zoom, and using linear for our project management.
23 00:02:45.380 ⇒ 00:02:48.670 Awaish Kumar: And yeah, that’s basically how we operate.
24 00:02:50.010 ⇒ 00:02:59.470 Awaish Kumar: Okay, so now… Yeah, you know, you can introduce yourself, and we can take on from there.
25 00:03:00.470 ⇒ 00:03:15.590 Sebastien Henry: Sure. So, I am Sebastian Henry. I was funded almost 8 years. Before that, I used to work in business intelligence, especially into bank environments in Montreal.
26 00:03:16.120 ⇒ 00:03:24.319 Sebastien Henry: During my journey at Indeed, I started as a simple business, business intelligence analyst to…
27 00:03:24.670 ⇒ 00:03:31.410 Sebastien Henry: to work into, product management, specialized about the Tableau,
28 00:03:31.550 ⇒ 00:03:40.089 Sebastien Henry: platform, where I was the innovation lead. You know, I designed a couple of solutions out of box.
29 00:03:40.090 ⇒ 00:03:52.510 Sebastien Henry: for example, connectors for… to connect to the big data source into Tableau, do some dashboard extension, do some proof of concepts with machine learning with Tableau.
30 00:03:53.030 ⇒ 00:04:08.530 Sebastien Henry: And after that, after a reorganization that you did two years ago, I come back to business intelligence, where I’ll be a senior data engineer and developer with Tableau.
31 00:04:09.380 ⇒ 00:04:25.620 Sebastien Henry: design several dashboards, and also, do some stuff with Snowflake, specialize with Vortex in order to create a chatbot linked to Slack, and with semantic layer on Snowflake.
32 00:04:25.880 ⇒ 00:04:31.090 Sebastien Henry: And after another reorg, now I’m project manager of Tableau Next.
33 00:04:31.340 ⇒ 00:04:35.530 Sebastien Henry: the new… new product from, Salesforce.
34 00:04:35.880 ⇒ 00:04:43.419 Sebastien Henry: where I try to evangelize the tools to the sales, to the marketing, and CS.
35 00:04:43.730 ⇒ 00:04:49.720 Sebastien Henry: And, in addition to that, I’m do… I’m preparing a master’s
36 00:04:49.790 ⇒ 00:05:08.749 Sebastien Henry: On, artificial intelligence. I hope I will get, the master’s soon. And, I do a couple of, hackathons, inside Indeed, outside Indeed, where I leverage AI for workflow with NN8, or to do some,
37 00:05:08.940 ⇒ 00:05:15.349 Sebastien Henry: To use a specific model in order to do some prediction, during, cowgirl competitions.
38 00:05:16.040 ⇒ 00:05:27.309 Sebastien Henry: And, the reason why I’m here is because, like I say, it’s been 8 years, I’ve worked for Indeed, several reorganizations. I think I do…
39 00:05:28.310 ⇒ 00:05:38.949 Sebastien Henry: I do overall what I… what I want to accomplish, indeed, so I need some new opportunities and new challenges. Is that the reason why I have front of you today?
40 00:05:40.650 ⇒ 00:05:47.220 Awaish Kumar: Okay, so, like, what are you looking for in a next role?
41 00:05:47.870 ⇒ 00:05:55.100 Awaish Kumar: Or what if I ask… if my… if I may ask, like, what are you looking for in your next
42 00:05:55.870 ⇒ 00:05:58.800 Awaish Kumar: two years, Where you want to be.
43 00:06:00.150 ⇒ 00:06:12.389 Sebastien Henry: I would like to be a senior engineer or solution architect, specialized in EI, because we do a lot of progress with AI, and I want to
44 00:06:12.820 ⇒ 00:06:24.150 Sebastien Henry: Use that and do… provide that to many customers as possible, to help them to their journey, to apply…
45 00:06:24.760 ⇒ 00:06:31.230 Sebastien Henry: Sorry, to create solution and workflow around AI, in other words, they can gain, productivity.
46 00:06:31.950 ⇒ 00:06:38.070 Awaish Kumar: Okay, so I… yeah, from that, I have one more question, then. So…
47 00:06:38.230 ⇒ 00:06:43.669 Awaish Kumar: Do you want to be, data engineer utilizing
48 00:06:43.880 ⇒ 00:06:58.310 Awaish Kumar: AI in their workflow to, like, speed up your development or optimize your development, or you are specifically looking for becoming an AI engineer, which basically
49 00:06:58.440 ⇒ 00:07:04.939 Awaish Kumar: builds products using AI agents, like, which are the… and actually.
50 00:07:05.610 ⇒ 00:07:12.740 Sebastien Henry: I am… I am, open for both, and also for, solution architecture.
51 00:07:17.460 ⇒ 00:07:26.179 Sebastien Henry: Because we know why I’m so difficult to find which, which niche I would like, because actually, I indeed…
52 00:07:26.180 ⇒ 00:07:40.119 Sebastien Henry: We… we do a lot of AI exploration, and we create semantic layer as an AI engineer, and also as a product manager, we try to evangelize what we create.
53 00:07:40.300 ⇒ 00:07:44.140 Sebastien Henry: But also, we do some architecture, because now…
54 00:07:44.260 ⇒ 00:07:51.139 Sebastien Henry: We have several semantics, several agents, and now think about the business context.
55 00:07:51.320 ⇒ 00:07:52.260 Sebastien Henry: So…
56 00:07:52.750 ⇒ 00:08:01.870 Sebastien Henry: we have to think about more architecture, and that’s… all this kind of positioning can be data engineer, or data governance for AI, or architecture.
57 00:08:02.110 ⇒ 00:08:08.600 Sebastien Henry: all kind of position, interests me, but I would say more architecture.
58 00:08:09.450 ⇒ 00:08:11.179 Sebastien Henry: I’m most interested.
59 00:08:12.410 ⇒ 00:08:29.669 Awaish Kumar: Okay, yeah. I’m asking this question because we have all these work streams at Brainforged. We have data engineering, analytics engineering, data analysts, AI engineers, and each have their own, defined roles, right?
60 00:08:30.080 ⇒ 00:08:41.250 Awaish Kumar: So, for example, if you are… you are working as an AI engineer, if you need any data help, or building models, or building semantic layer, you might get help from a data engineer.
61 00:08:41.360 ⇒ 00:08:58.020 Awaish Kumar: And so we have people for that work. Similarly, if you are a data engineer and you build the model AI, you build the, basically, data ingestion, modeling, all of that, and then you need some AI features. So we have a team for that, that can help you build
62 00:08:58.150 ⇒ 00:09:15.419 Awaish Kumar: AI part. So I have been asked… I’ve been asking these questions to actually figure out what… where you want… want to be, if we… because we have all these workshops, like, and, like, anyone can be… if… if it… if it, like, a win-win for both, you…
63 00:09:15.590 ⇒ 00:09:19.389 Awaish Kumar: Someone can be placed in any of the work streams where they fit.
64 00:09:20.430 ⇒ 00:09:21.140 Sebastien Henry: Okay.
65 00:09:21.740 ⇒ 00:09:23.379 Awaish Kumar: Okay,
66 00:09:23.550 ⇒ 00:09:32.270 Awaish Kumar: So, yeah, let’s talk about… but I have your profile in front of me, as an analytics engineer.
67 00:09:32.710 ⇒ 00:09:35.949 Awaish Kumar: I think, it’s… is it because maybe…
68 00:09:36.060 ⇒ 00:09:47.779 Awaish Kumar: Yeah, like, the recruiter’s team, maybe from your application, or if they reached out to you, they basically have a profile as an analytics engineer.
69 00:09:49.140 ⇒ 00:09:58.160 Sebastien Henry: Well, because all my main, skills are analytics engineer, and, because I didn’t have
70 00:09:58.350 ⇒ 00:10:11.150 Sebastien Henry: or I just have a few opportunities to do some EI at my current job, and that’s the reason why I do the master’s on the side, in order to have more experience, in order to
71 00:10:11.290 ⇒ 00:10:16.959 Sebastien Henry: have opportunities with VI, but also you need experience.
72 00:10:17.200 ⇒ 00:10:29.070 Sebastien Henry: And, that’s the reason you have my profile as an analytical engineer, but who knows, maybe in 6 months, or 1 year, or more years, my profile will change.
73 00:10:31.190 ⇒ 00:10:39.029 Awaish Kumar: Okay, for… And yeah, let’s talk about, maybe, one of the projects that you delivered end-to-end.
74 00:10:40.640 ⇒ 00:10:43.600 Awaish Kumar: And maybe we… I just want to be…
75 00:10:43.700 ⇒ 00:10:46.069 Awaish Kumar: I want to discuss that project in depth.
76 00:10:46.270 ⇒ 00:10:50.730 Awaish Kumar: Like, steps of that project, the flow.
77 00:10:51.060 ⇒ 00:10:59.870 Awaish Kumar: the exact tools and technologies used, and where you participated. If it was a team effort, then what exactly you were
78 00:11:00.330 ⇒ 00:11:01.500 Awaish Kumar: This one’s one…
79 00:11:03.220 ⇒ 00:11:05.269 Sebastien Henry: What kind of projects?
80 00:11:05.590 ⇒ 00:11:06.260 Sebastien Henry: You would like…
81 00:11:06.260 ⇒ 00:11:14.160 Awaish Kumar: I mean… Miao, any project that you have worked on recently, obviously, related to data.
82 00:11:14.560 ⇒ 00:11:18.680 Awaish Kumar: Is data… if it is a data analytics engineering or data engineering.
83 00:11:19.640 ⇒ 00:11:21.890 Sebastien Henry: For example,
84 00:11:22.890 ⇒ 00:11:36.649 Sebastien Henry: The… the dashboard, or for the dashboards I provide for legal, where, I meet, the clients, they told me about what they… they need, kind of data.
85 00:11:36.800 ⇒ 00:11:39.329 Sebastien Henry: Every time I ask,
86 00:11:39.450 ⇒ 00:11:56.320 Sebastien Henry: drive me what you want, because you know better than me the data, you know better than me. If you have the dashboard, what kind of analytics you want, in order, you can be proactive and just watch a few seconds the dashboard, and move. So, with this mocap, with this
87 00:11:56.440 ⇒ 00:12:03.089 Sebastien Henry: what kind of data they need, and also have something I can compare when this is done.
88 00:12:03.280 ⇒ 00:12:12.829 Sebastien Henry: in order to do the test, if I’m right or wrong. With this, I start to build, on Tableau.
89 00:12:13.410 ⇒ 00:12:25.219 Sebastien Henry: The data source creates kind of a UTL via Tableau Prep. They do all the aggregation cleaning on the back-end level in order to have very clean data source.
90 00:12:25.410 ⇒ 00:12:40.020 Sebastien Henry: and build the dashboard on that. The reason why is to have a maximum or pre-processed thing on the backend, and to have something that can load very fast on the dashboard in order to reach
91 00:12:40.280 ⇒ 00:12:45.199 Sebastien Henry: 56 seconds when it’s open.
92 00:12:45.830 ⇒ 00:12:55.620 Sebastien Henry: After that, I do some testing with what they give to me. We meet several times in order to do the UAT.
93 00:12:55.790 ⇒ 00:13:01.719 Sebastien Henry: And after that, we do some, some kickoff with,
94 00:13:01.950 ⇒ 00:13:09.360 Sebastien Henry: with a bigger community… the legal community, in other words, they can adopt the… the data product I have built for them.
95 00:13:10.150 ⇒ 00:13:13.979 Sebastien Henry: And also, provide some documentation and support.
96 00:13:15.360 ⇒ 00:13:19.660 Sebastien Henry: Generally, it’s… this kind of process is generally…
97 00:13:19.820 ⇒ 00:13:28.289 Sebastien Henry: It was generally 80% of my previous life before, I started to be a project manager.
98 00:13:28.820 ⇒ 00:13:32.680 Sebastien Henry: Now, do you want another example?
99 00:13:34.800 ⇒ 00:13:35.760 Awaish Kumar: Sounded?
100 00:13:35.760 ⇒ 00:13:39.910 Sebastien Henry: Do you want… do you want another example, or you’re good?
101 00:13:40.990 ⇒ 00:13:49.900 Awaish Kumar: No, I’m good, I… Yeah, so in terms of… that was mostly… Regarding building a dashboard.
102 00:13:50.080 ⇒ 00:14:00.229 Awaish Kumar: Have you experienced, like, building the models using dbt or SQL, right, inquiries? Do you have experience with that?
103 00:14:00.520 ⇒ 00:14:16.160 Sebastien Henry: I create some… I didn’t have a chance to use dbt, but I would be very open to learn dbt. I know at Indeed we use that, and of course, after that is a question of opportunities, and I didn’t have access to this.
104 00:14:16.240 ⇒ 00:14:26.650 Sebastien Henry: But into, studying and outside… work outside Indeed. I create some tables, I create some model, I create some schema, yes.
105 00:14:26.840 ⇒ 00:14:30.719 Awaish Kumar: Yeah, yeah, my… Yeah, DBT is not…
106 00:14:31.540 ⇒ 00:14:34.710 Awaish Kumar: A hard requirement, like, that you can learn in a…
107 00:14:35.670 ⇒ 00:14:49.149 Awaish Kumar: Yeah, right? So, the most important is that if you have exciting queries, that’s what, essentially, you would be doing in dbt as well. My question is, how would you rate yourself in SQL, for example?
108 00:14:50.900 ⇒ 00:14:55.430 Sebastien Henry: Well, I would say, on 10, would be between 8
109 00:14:55.480 ⇒ 00:15:12.120 Sebastien Henry: 8 and 9, because, you know, you don’t remember specific, functionality, like, you know, the advanced one, how to do window function, feel like this, and you check, the, the documentation on the website, is that the way how we do this? Okay, perfect.
110 00:15:12.120 ⇒ 00:15:17.489 Sebastien Henry: But now, with EI, you can ask help, and they can do that for you.
111 00:15:19.600 ⇒ 00:15:22.799 Awaish Kumar: Okay, yeah, so when you rate yourself.
112 00:15:22.970 ⇒ 00:15:28.769 Awaish Kumar: it’s not about writing code anymore, because obviously everybody’s using AI to write it, right?
113 00:15:29.130 ⇒ 00:15:32.859 Awaish Kumar: It’s more about, understanding the basics.
114 00:15:33.020 ⇒ 00:15:40.299 Awaish Kumar: Have a… have a… knowledge of, like, when AI writes something, you have a…
115 00:15:40.520 ⇒ 00:15:45.950 Awaish Kumar: Ability to review it and figure out if it is good enough, if it has issues.
116 00:15:46.690 ⇒ 00:15:52.479 Sebastien Henry: Yeah, I have this kind of, resentment because…
117 00:15:52.870 ⇒ 00:15:58.540 Sebastien Henry: since here, we use, heavily CureSoft, and,
118 00:15:58.640 ⇒ 00:16:10.189 Sebastien Henry: we build, I build, several solutions at Indeed, but also outside, also for my students, and,
119 00:16:10.540 ⇒ 00:16:24.159 Sebastien Henry: every time when I do the plant mode, analyze and challenge Cursor in order to have a better plant, in order to have the better solution possible.
120 00:16:25.460 ⇒ 00:16:26.320 Awaish Kumar: Okay.
121 00:16:26.490 ⇒ 00:16:34.310 Awaish Kumar: So, and I’m just familiar with the databases, data warehouses? So, what database… data warehouses you have?
122 00:16:34.640 ⇒ 00:16:35.800 Awaish Kumar: experience with…
123 00:16:37.040 ⇒ 00:16:49.710 Sebastien Henry: Snowflake, essentially. Now, Data360, because, I represent Tableau Next, and Tableau Next is inside the data 360 in Salesforce.
124 00:16:50.120 ⇒ 00:16:58.299 Sebastien Henry: In the past, I do Microsoft SQL, mongoDB, Oracle… But it’s been a while.
125 00:16:59.600 ⇒ 00:17:00.600 Awaish Kumar: Okay.
126 00:17:00.740 ⇒ 00:17:08.510 Awaish Kumar: So you have experience with Snowflake. Can you describe, some of the features of Snowflake?
127 00:17:10.980 ⇒ 00:17:25.139 Sebastien Henry: You have a cortex for the AI, you can… you can create semantic, semantic view, semantic layer, you have, you can create, some database schema.
128 00:17:25.980 ⇒ 00:17:27.659 Sebastien Henry: You know,
129 00:17:27.790 ⇒ 00:17:35.539 Sebastien Henry: you have also snow cones, snow pipe, things like this, like, in order to do some integration. You have…
130 00:17:36.740 ⇒ 00:17:42.010 Awaish Kumar: like, what… how it works? Like, when you run a query, what happens in Snowflake, or…
131 00:17:43.780 ⇒ 00:17:56.740 Sebastien Henry: Well, I don’t do… I don’t do specific work into Snowflake, to create tables, things like that, except semantic layer, but the only time I interact with Snowflake with a query is through Tableau.
132 00:17:57.420 ⇒ 00:17:58.110 Awaish Kumar: Okay.
133 00:17:58.520 ⇒ 00:17:59.080 Sebastien Henry: Yeah.
134 00:18:00.410 ⇒ 00:18:07.639 Sebastien Henry: Execute the query in the cloud, and you pay per query and per storage, and you get your result.
135 00:18:08.680 ⇒ 00:18:09.520 Awaish Kumar: Okay.
136 00:18:09.680 ⇒ 00:18:13.960 Awaish Kumar: So do you know what are virtual warehouses in Snowflake?
137 00:18:15.160 ⇒ 00:18:16.010 Sebastien Henry: Excuse me?
138 00:18:16.710 ⇒ 00:18:20.910 Awaish Kumar: Do you know what is the concept of virtual warehouse in Snowflake?
139 00:18:22.370 ⇒ 00:18:26.879 Sebastien Henry: I knew in the past, but I have to be honest with you, I don’t remember.
140 00:18:27.620 ⇒ 00:18:28.380 Awaish Kumar: Okay.
141 00:18:28.610 ⇒ 00:18:30.870 Awaish Kumar: Okay.
142 00:18:31.270 ⇒ 00:18:35.689 Awaish Kumar: So… How do you think,
143 00:18:37.740 ⇒ 00:18:48.140 Awaish Kumar: Yeah, how do you think the snowflake optimizes the optimizes the… the cost.
144 00:18:49.300 ⇒ 00:18:52.800 Awaish Kumar: For, like, the queries that we run,
145 00:18:54.010 ⇒ 00:19:05.350 Awaish Kumar: Obviously, it has a compute system where it is going to exit using that, but, snowflake,
146 00:19:05.780 ⇒ 00:19:11.419 Awaish Kumar: does have something to optimize our queries, likely.
147 00:19:11.670 ⇒ 00:19:16.219 Awaish Kumar: So, do you remember anything, like, how Snowflake does it?
148 00:19:16.900 ⇒ 00:19:18.549 Sebastien Henry: You mean a bad performance?
149 00:19:19.320 ⇒ 00:19:20.790 Awaish Kumar: The cost optimization.
150 00:19:21.370 ⇒ 00:19:22.400 Sebastien Henry: That’s what the musician?
151 00:19:22.870 ⇒ 00:19:23.529 Awaish Kumar: His way.
152 00:19:23.530 ⇒ 00:19:33.920 Sebastien Henry: You probably have a cache layer where you have the most, most common queries, you execute more and more, and instead, every time,
153 00:19:34.270 ⇒ 00:19:43.110 Sebastien Henry: hit the table and re-execute the same thing. You go directly on the cache, you already have the data up.
154 00:19:44.250 ⇒ 00:19:47.930 Sebastien Henry: Like a, like aWS with a redshift, and
155 00:19:48.120 ⇒ 00:19:51.999 Sebastien Henry: Or it depends how you build that, but Redshift do that too.
156 00:19:52.640 ⇒ 00:20:02.920 Awaish Kumar: Okay, so, if we talk about warehousing, then there is the concept of start a schema.
157 00:20:03.590 ⇒ 00:20:11.160 Awaish Kumar: affiliate schema. So, what is the difference between these two, and… where…
158 00:20:11.860 ⇒ 00:20:15.289 Awaish Kumar: Where… where… what is the use case for each of these?
159 00:20:18.010 ⇒ 00:20:26.530 Sebastien Henry: Snowflake schema is for analytic purposes. You have one fact table, around several dimension tables.
160 00:20:26.630 ⇒ 00:20:33.580 Sebastien Henry: And the snowflake one is… With more, more information.
161 00:20:35.540 ⇒ 00:20:50.359 Sebastien Henry: It can be one or two, several fact tables with different dimension tables around, but it can have several levels, and we say several levels, it means more complexity.
162 00:20:50.480 ⇒ 00:20:57.389 Sebastien Henry: Is that, for the Snowflake, schema?
163 00:20:59.650 ⇒ 00:21:05.550 Sebastien Henry: I should revise this, to be honest, and I don’t know.
164 00:21:06.900 ⇒ 00:21:07.670 Awaish Kumar: Okay.
165 00:21:08.320 ⇒ 00:21:09.320 Awaish Kumar: Okay.
166 00:21:09.770 ⇒ 00:21:16.090 Awaish Kumar: Okay, I think, yeah, we are… Almost, like, on time.
167 00:21:16.320 ⇒ 00:21:23.670 Awaish Kumar: We have 9 minutes left, so I have just a few questions regarding, how do you communicate with,
168 00:21:25.340 ⇒ 00:21:27.910 Awaish Kumar: the stakeholders, so…
169 00:21:28.890 ⇒ 00:21:38.169 Awaish Kumar: Obviously, we have stakeholders. Majority of time, the people we are working with are maybe C-level or execs.
170 00:21:38.310 ⇒ 00:21:49.640 Awaish Kumar: And not all of them are technical, right? Some are, but maybe not all of them. So when we are communicating our findings.
171 00:21:49.780 ⇒ 00:21:58.870 Awaish Kumar: with the non-technical stakeholders, maybe it can be CO, maybe it can be anyone on a VP level, or something.
172 00:21:59.080 ⇒ 00:22:06.229 Awaish Kumar: So, how… Do you communicate with them, your findings? And if there are disagreements, how do you resolve them?
173 00:22:06.890 ⇒ 00:22:23.570 Sebastien Henry: Well, first of all, in order to speak very well with them, I try to adopt their language, because depending on the level you are, you speak a specific language. And when… when we start to be a bit technical, I try to
174 00:22:23.840 ⇒ 00:22:27.949 Sebastien Henry: think is, I have,
175 00:22:28.080 ⇒ 00:22:39.940 Sebastien Henry: 5 years old boy in front of me, so that’s the reason I prefer to use image, you know, because image is better than 1,000 words.
176 00:22:40.110 ⇒ 00:22:47.190 Sebastien Henry: And, to… Present, technical points, the most simple way.
177 00:22:47.540 ⇒ 00:22:53.729 Sebastien Henry: And also, when I have to present something, I try to do… to… to tell a story.
178 00:22:54.360 ⇒ 00:22:58.689 Sebastien Henry: In order that, keep their interest.
179 00:22:59.040 ⇒ 00:23:08.270 Sebastien Henry: Provide the key information at the right moment in other message pass beller.
180 00:23:08.670 ⇒ 00:23:16.510 Sebastien Henry: After that, a conflict, you know, I am more into the communication and the dialogue,
181 00:23:17.050 ⇒ 00:23:21.049 Sebastien Henry: When you say conflict with the client, or one of my peers?
182 00:23:23.250 ⇒ 00:23:29.930 Awaish Kumar: I mean, when you have, Disagreements, not necessarily a conflict.
183 00:23:30.030 ⇒ 00:23:49.219 Awaish Kumar: Disagreement in a sense that sometimes, some stakeholders which are, doing, work in the market, have some bias of them being in a market and… and some domain knowledge, and based on that, they have their own,
184 00:23:49.890 ⇒ 00:24:03.659 Awaish Kumar: their own assumptions, their own numbers, their own understanding of the system. But on the other side, when you are trying to bring those facts from the data.
185 00:24:04.670 ⇒ 00:24:11.330 Awaish Kumar: there might be some disagreements, in a sense. They might say, okay, I think this number… this number looks…
186 00:24:11.510 ⇒ 00:24:22.119 Awaish Kumar: weird to me, or wrong to me, and things like that. While maybe you are correct, maybe the way you have done it is correct, but
187 00:24:22.940 ⇒ 00:24:32.839 Awaish Kumar: Maybe it’s wrong, maybe the definition is different, the way they define it, the way you define it, but, like, how do you dissolve these disagreements?
188 00:24:33.120 ⇒ 00:24:45.950 Sebastien Henry: Well, I am for the dialogue, and so I can… I can listen and heard the arguments from the other party, I can provide mine, and for…
189 00:24:46.000 ⇒ 00:24:58.019 Sebastien Henry: every part of this agreement, I try to do my best to prove… to provide the proof, so… in order to see. The reason why I say that is because you have this, you have this, and you have that.
190 00:24:58.200 ⇒ 00:25:07.149 Sebastien Henry: And and I try to orient this attention on this in order we can have a point where we can both agree. After that.
191 00:25:07.330 ⇒ 00:25:14.180 Sebastien Henry: One of us can change our minds and, oh, effectively, read this proof.
192 00:25:14.490 ⇒ 00:25:25.100 Sebastien Henry: You may… you’re probably right, or I’m… I’m probably right. And with this dynamic, I can decelerate, any kind of,
193 00:25:25.850 ⇒ 00:25:27.940 Sebastien Henry: conflict?
194 00:25:28.260 ⇒ 00:25:32.480 Sebastien Henry: If I can’t, probably we can find a compromise.
195 00:25:35.270 ⇒ 00:25:36.490 Sebastien Henry: Does that make sense?
196 00:25:37.220 ⇒ 00:25:38.590 Awaish Kumar: Okay, yeah.
197 00:25:40.330 ⇒ 00:25:47.979 Awaish Kumar: And, yeah, that’s it from my side. I will leave last few minutes for you two, if you have any other… if you have any questions.
198 00:25:48.360 ⇒ 00:25:52.219 Sebastien Henry: What is the reason why you choose to work for Bringforge?
199 00:25:53.590 ⇒ 00:26:04.880 Awaish Kumar: The reason I chose to work for Brain Forge is, Is that… I… I would say that,
200 00:26:05.740 ⇒ 00:26:08.569 Awaish Kumar: when I… I met with the…
201 00:26:08.980 ⇒ 00:26:12.500 Awaish Kumar: CEO on a DVD community channel.
202 00:26:12.630 ⇒ 00:26:17.899 Awaish Kumar: And… We exchanged our ideas of
203 00:26:18.190 ⇒ 00:26:24.750 Awaish Kumar: What… what he’s doing, the kind of work they are doing, and they… they need staff.
204 00:26:24.890 ⇒ 00:26:29.619 Awaish Kumar: For… for doing data engineering and analytics engineering.
205 00:26:31.450 ⇒ 00:26:33.530 Awaish Kumar: the… the consultancy…
206 00:26:33.700 ⇒ 00:26:43.069 Awaish Kumar: So this is a consultancy firm which is kind of really fast-paced, really different than how other people might operate. So…
207 00:26:43.560 ⇒ 00:26:45.120 Awaish Kumar: less friction.
208 00:26:45.360 ⇒ 00:26:47.890 Awaish Kumar: More of a flexibility?
209 00:26:48.940 ⇒ 00:26:50.450 Awaish Kumar: That also gives you…
210 00:26:51.040 ⇒ 00:27:00.480 Awaish Kumar: moral responsibility, accountability, but you have flexibility and ownership of the things the way you want to have it. You can… you can…
211 00:27:00.640 ⇒ 00:27:09.099 Awaish Kumar: have your own ideas on the table, and you will use AI, like, in every part of your workflow.
212 00:27:09.350 ⇒ 00:27:24.000 Awaish Kumar: And you will find support from the founders for all of these things, but only thing is that, at the end of the day, you have ownership of those things, and you are accountable for what you suggested, and the outcomes of those, right?
213 00:27:25.720 ⇒ 00:27:32.210 Awaish Kumar: Yeah, so, yeah, it gives me the flexibility and the speed at which I learn new things.
214 00:27:33.560 ⇒ 00:27:37.160 Sebastien Henry: And, how long did you work for this company?
215 00:27:37.990 ⇒ 00:27:43.290 Awaish Kumar: I have, like, I’ve been with them for… More than a year now?
216 00:27:43.460 ⇒ 00:27:44.550 Awaish Kumar: Okay.
217 00:27:45.550 ⇒ 00:27:50.559 Awaish Kumar: Yeah, so it’s… when we met, first time, so, yeah.
218 00:27:51.260 ⇒ 00:27:56.559 Sebastien Henry: And you are at the same position as you arrive in this company?
219 00:27:56.700 ⇒ 00:27:57.280 Sebastien Henry: Oh, indeed.
220 00:27:57.280 ⇒ 00:27:57.970 Awaish Kumar: Sonny?
221 00:27:58.510 ⇒ 00:28:04.329 Sebastien Henry: And did your role evolve, or you have the same role when you start.
222 00:28:04.740 ⇒ 00:28:06.479 Awaish Kumar: No, I shouted as well.
223 00:28:06.820 ⇒ 00:28:09.440 Awaish Kumar: I started as a contractor.
224 00:28:10.080 ⇒ 00:28:13.019 Awaish Kumar: And then I moved into a full-time data engineer.
225 00:28:13.190 ⇒ 00:28:17.769 Awaish Kumar: Now I’m kind of a… Leading data engineering.
226 00:28:18.950 ⇒ 00:28:27.420 Sebastien Henry: Okay, that’s good, that’s good. And, one… when you asked me about the difference between,
227 00:28:27.840 ⇒ 00:28:33.530 Sebastien Henry: Star schema, snowflake schema. What was the right answer?
228 00:28:33.730 ⇒ 00:28:38.420 Sebastien Henry: Because, I have, in my mind, I totally forget.
229 00:28:39.620 ⇒ 00:28:45.359 Awaish Kumar: So, I asked, difference between flat table schema and the star schema.
230 00:28:45.800 ⇒ 00:28:57.099 Awaish Kumar: Snowflake schema is also part of this, but I… that was not my question. But we can talk about it. So it’s part of all… all is part of a dimensional modeling, right?
231 00:28:57.230 ⇒ 00:29:02.279 Awaish Kumar: Flat table schema is that, like, you just have a flat table where
232 00:29:02.570 ⇒ 00:29:11.769 Awaish Kumar: all the data regarding something, for example, an order. So you have order data in there, you have product data in there, you have customer data in there, you have their.
233 00:29:11.970 ⇒ 00:29:17.499 Sebastien Henry: It’s a flat table, I thought… Okay.
234 00:29:17.700 ⇒ 00:29:27.009 Awaish Kumar: Right? And then a style schema is that you’d split that flat table into, maybe, dim and dimension and fact table, and then fact table is a center
235 00:29:27.060 ⇒ 00:29:42.579 Awaish Kumar: where you have all these transactions, right? Where most of the time, you only have primary keys, foreign keys in there, and then in mayors, and then it is connected via foreign keys to multiple tables, like DIM customer, DIM…
236 00:29:42.650 ⇒ 00:29:51.290 Awaish Kumar: products, calendar, right? Why we do that? Like, the style schema, we… when we want to achieve, like, the data.
237 00:29:51.430 ⇒ 00:29:56.459 Awaish Kumar: Integrity, we want to reduce the redundancy, right?
238 00:29:56.580 ⇒ 00:30:03.319 Awaish Kumar: And, when we want to have, Yeah, that’s the reason, right?
239 00:30:03.320 ⇒ 00:30:07.920 Sebastien Henry: Yeah, totally. I misunderstood your question, sorry, it’s my bad.
240 00:30:09.310 ⇒ 00:30:17.189 Sebastien Henry: Well, I don’t have, more question, or the last question, what would be the next, step?
241 00:30:17.390 ⇒ 00:30:20.869 Awaish Kumar: Yeah, the next steps are, like, we have,
242 00:30:21.420 ⇒ 00:30:28.770 Awaish Kumar: Yeah, we have recruited her, Kyla, she… she might be able… she will be… She will come back.
243 00:30:29.040 ⇒ 00:30:40.930 Awaish Kumar: For the next steps, but we, like, generally, we have a second intro session with one of my colleagues, then we have a take-home assignment, and after that, we have a…
244 00:30:41.650 ⇒ 00:30:51.109 Awaish Kumar: panel interview, mostly regarding that assignment and everything that goes into that. And… yeah, after that, there will be…
245 00:30:51.590 ⇒ 00:30:54.749 Awaish Kumar: Answer, offer, or whatever it is.
246 00:30:54.920 ⇒ 00:31:00.110 Awaish Kumar: Yeah, for the… for the next steps, like, Kayla will be in touch.
247 00:31:00.780 ⇒ 00:31:02.799 Sebastien Henry: Perfect. Thank you for your time.
248 00:31:03.260 ⇒ 00:31:05.940 Awaish Kumar: Okay, thank you so much. Thank you for your time.
249 00:31:06.410 ⇒ 00:31:07.409 Awaish Kumar: Good day, bud.