Meeting Title: Brainforge Interview w- Awaish Date: 2026-03-16 Meeting participants: Awaish Kumar, Dennis Oliver
WEBVTT
1 00:00:09.290 ⇒ 00:00:10.000 Awaish Kumar: Right?
2 00:00:14.560 ⇒ 00:00:16.070 Dennis Oliver: Hey, how you doing?
3 00:00:16.620 ⇒ 00:00:17.920 Awaish Kumar: Hey, Linus, how are you?
4 00:00:18.110 ⇒ 00:00:19.379 Dennis Oliver: Oh, pretty good.
5 00:00:19.500 ⇒ 00:00:20.659 Dennis Oliver: Nice to meet you.
6 00:00:21.010 ⇒ 00:00:28.659 Awaish Kumar: Good to meet you, too. Yeah, so… For this… for the…
7 00:00:29.250 ⇒ 00:00:33.540 Awaish Kumar: This interview, like, the agenda is, like, to discuss.
8 00:00:34.340 ⇒ 00:00:39.809 Awaish Kumar: And understand more about Muju and your experiences and past projects.
9 00:00:40.130 ⇒ 00:00:49.649 Awaish Kumar: And also share… basically, anything you want to know about Brainforge, as a company and us, and…
10 00:00:50.050 ⇒ 00:00:53.299 Awaish Kumar: And yeah, that’s basically the plan.
11 00:00:54.460 ⇒ 00:00:56.929 Awaish Kumar: So, before I dive into, like, what…
12 00:00:57.470 ⇒ 00:01:02.890 Awaish Kumar: regarding my set of questions, I would love to know, like, if you know anything about Brain Forge.
13 00:01:03.870 ⇒ 00:01:08.800 Dennis Oliver: I would say, I feel like, at the moment, it’s probably just kind of surface level.
14 00:01:08.860 ⇒ 00:01:24.810 Dennis Oliver: I know that it’s kind of a newer AI analytics company, from what I was understanding, that’s formulating. I know that it’s not as big of a team as what, I guess, like, it’s kind of newer, so it’s not a really big company at the moment, but kind of working on contracts and, like, growing.
15 00:01:24.860 ⇒ 00:01:41.090 Dennis Oliver: So I feel like parts, you know, some of the information that I’ve seen has been pretty exciting about it, but then also, I guess, like, I’m open to learning more and kind of seeing what you guys have planned, because all I can really do is kind of look at the website and see what’s out there, you know, so it’s a little different.
16 00:01:41.730 ⇒ 00:01:44.429 Awaish Kumar: So, what do you… what do you think, like,
17 00:01:46.140 ⇒ 00:01:52.940 Awaish Kumar: Like, you yourself see as a… what do you see yourself, like,
18 00:01:53.270 ⇒ 00:01:55.490 Awaish Kumar: From, like, 5 years from now.
19 00:01:58.470 ⇒ 00:02:03.760 Awaish Kumar: And this role at Brainforge can, like, can help you be the at that point.
20 00:02:04.240 ⇒ 00:02:14.250 Dennis Oliver: Yeah. I mean, for me, I guess, I really would like to get to the point of becoming, like, more of a data architect, with a full abreast of, like, I guess.
21 00:02:14.250 ⇒ 00:02:28.929 Dennis Oliver: data and knowledge. Right now, I work a lot… I’m more of an engineer status, so I work a lot on, a lot of different projects, but it’s… it’s more so, like, taking the concepts that I learned from the data architect, so looking at the things that they’ve kind of passed down.
22 00:02:28.930 ⇒ 00:02:41.929 Dennis Oliver: So I would like to kind of grow into that role, but more… with more of a focus on Adobe. I’ve been… I guess, like, I know Google, I know Adobe, I know a little bit of Celebis, I’ve worked some with Teelium, so it’s like, I know those…
23 00:02:41.930 ⇒ 00:02:52.219 Dennis Oliver: those data, I guess, like, I know those different, tools, and I have a little bit of a background as a software engineer from different tools that I’ve built on my own, and just websites and things like that.
24 00:02:52.220 ⇒ 00:03:06.989 Dennis Oliver: So, I guess, really, I would like to make these… make more of a holistic movement into becoming specialized in, really, data and Adobe, to bring customers the insights that they want through, I guess, their customer journeys, really, so…
25 00:03:07.250 ⇒ 00:03:23.249 Awaish Kumar: I see your profile as under, like, analytics engineer on my side, but you mentioned, like, being a data architect, so, like, do you want to be more, like, on a data engineering side, or an analytics engineering side?
26 00:03:23.730 ⇒ 00:03:27.730 Dennis Oliver: I guess, like, more analytics engineering, which…
27 00:03:27.770 ⇒ 00:03:38.430 Dennis Oliver: that’s kind of where we pretty much focus at, is, like, analyst engineering, but I think one of the things with me is, like, I’ve been kind of in my journey so far under tech, because I’ve just started
28 00:03:38.470 ⇒ 00:03:45.430 Dennis Oliver: So I did software engineering. I never really landed a role with software engineering, it was kind of like a passion, so I went back to school to learn how to do that.
29 00:03:45.430 ⇒ 00:03:58.539 Dennis Oliver: And from, doing that, I started applying for different jobs, and I ended up getting a role with, Search Discovery at the moment, that’s what the name of the company was, as an analytical engineer. And it’s kind of, like, it’s allowed me to grow and, like, kind of…
30 00:03:58.540 ⇒ 00:04:05.439 Dennis Oliver: I guess tried a lot of different things, so I was saying, through Google, Teleum, all those things, I was able to try those out and kind of learn about them.
31 00:04:05.500 ⇒ 00:04:07.270 Dennis Oliver: And I think, like…
32 00:04:07.270 ⇒ 00:04:10.670 Awaish Kumar: Do you like doing the analytics engineering work?
33 00:04:10.670 ⇒ 00:04:19.179 Dennis Oliver: Oh, yeah, yeah, yeah. No, I like the work that I do. It’s cool working with the clients, and I think one of the nice things between, like, analytics engineering, like.
34 00:04:19.630 ⇒ 00:04:34.080 Dennis Oliver: the software engineering, analyst engineering, all of this at the same time is that you see, the work that you do affect, like, others. So it’s like, the data that we’re able to collect affects how clients make choices on their websites, which we see
35 00:04:34.080 ⇒ 00:04:36.120 Dennis Oliver: Coming to real life, you know.
36 00:04:36.120 ⇒ 00:04:59.470 Dennis Oliver: changes, so it’s… I guess, like, for instance, I don’t know, I might be diving ahead of myself here in the conversation, but, like, working with, like, Johnson & Johnson with their, like, different flows and stuff like that. So, like, we work with them on how you track customers going through, like, your forms that they’re filling out, and it has real-life impact. You know, if customers are having trouble getting, like, being able to apply for their medicine.
37 00:04:59.760 ⇒ 00:05:07.300 Dennis Oliver: we’re helping the company find out about that earlier, so that they can make smarter decisions about their forms and things. So, I think, you know, it’s…
38 00:05:07.580 ⇒ 00:05:13.609 Dennis Oliver: the work that we do has meaning, and it gives… it helps the companies out that we work with, so I think that that’s the core.
39 00:05:14.190 ⇒ 00:05:18.830 Awaish Kumar: So how do you rate yourself in, an SQL item.
40 00:05:19.580 ⇒ 00:05:33.719 Dennis Oliver: I feel like I’m pretty… like, I wouldn’t say I’m, like, the greatest, SQL of Python I’m learning. Like, I use it pretty regularly, just depending on the client, so it’s not like it’s something that we use daily with every client, but it’s like…
41 00:05:33.790 ⇒ 00:05:51.190 Dennis Oliver: I’ve worked with HCA, which is a healthcare client, and with them, I had to write in SQL all the time. Python, I generally use it from time to time. I will say I do vibe code from time to time, so it’s like, I have an understanding of how to write Python, but then I do also…
42 00:05:51.190 ⇒ 00:06:00.480 Dennis Oliver: use Vibe to help write, like, write Python code as well, so it’s like, I’m familiar with using the technology that we have there to kind of help speed up the…
43 00:06:00.480 ⇒ 00:06:05.159 Dennis Oliver: the efforts of coding and things there, so… I… I don’t know, I feel like I knew.
44 00:06:05.160 ⇒ 00:06:15.089 Awaish Kumar: Like, how familiar are… like, I understand vibe coding, which are completely okay, like, as long as you understand what’s going on, right?
45 00:06:15.510 ⇒ 00:06:18.000 Awaish Kumar: And with the query on what’s going on in the…
46 00:06:18.400 ⇒ 00:06:32.569 Awaish Kumar: if the AI writes the code that is not optimal, you should be able to spot it, right? That’s what we are looking for. So, like, what do you think, like, in terms of advanced concepts of SQL, like,
47 00:06:32.970 ⇒ 00:06:37.499 Awaish Kumar: Are you familiar with… like, how would you optimize the query, for example? What would you…
48 00:06:37.780 ⇒ 00:06:49.829 Awaish Kumar: For example, if there is a table, and I write a query, which takes, like, 5 minutes to execute, and there’s a lot of time for me, I would at least want it to be under a minute.
49 00:06:50.040 ⇒ 00:06:57.140 Awaish Kumar: Or under 30 seconds, so how would you optimize that? How… what…
50 00:06:57.280 ⇒ 00:07:05.300 Awaish Kumar: What aspects of that query, or database, or whatever you would consider, and then what exactly you would do to optimize it.
51 00:07:06.010 ⇒ 00:07:14.809 Dennis Oliver: I mean, I want to make sure I answer this in the right way, because I, like, once again, I don’t really write SQL all the time, but I am familiar with it at the same time.
52 00:07:14.970 ⇒ 00:07:31.919 Dennis Oliver: I mean, a lot of it does matter on how much data you’re pulling into that query, so as far as the date range, if you’re trying to, I guess, run through to take a test to make sure that the query works initially, I mean, you definitely have to take into account how much data are you pulling into that query at the same time, so it’s like, I wouldn’t want to run a…
53 00:07:31.920 ⇒ 00:07:35.400 Dennis Oliver: 10-minute long career and, like, waste the runs for that.
54 00:07:35.400 ⇒ 00:07:48.199 Dennis Oliver: But I mean, on top of that, you… I guess you have to be cognizant of the joins that you’re trying to join and the data that you’re pulling in. I… I guess I don’t want… I’m not sure the exact answer for this one.
55 00:07:48.270 ⇒ 00:07:49.420 Dennis Oliver: I’ve…
56 00:07:49.640 ⇒ 00:08:02.160 Dennis Oliver: use SQL to, I guess, like, I don’t know, like, if I’m pulling URLs, trying to make sure that I’m sourcing the right URLs to kind of sort through that, or pulling date ranges, or, like, names and things like that, but I don’t know if I…
57 00:08:02.530 ⇒ 00:08:03.520 Dennis Oliver: Yeah, exactly.
58 00:08:03.520 ⇒ 00:08:06.409 Awaish Kumar: I get it, like, you use SQL to…
59 00:08:06.520 ⇒ 00:08:08.949 Awaish Kumar: Carry your database, get some data.
60 00:08:08.950 ⇒ 00:08:09.280 Dennis Oliver: Hmm.
61 00:08:09.280 ⇒ 00:08:25.619 Awaish Kumar: I understand, completely understand that, right? But it’s, like, very common in our job, right? I… when I’m dealing with a client which has, like, billions of rows, and… or millions of rows, where I have to process their data, like, every day.
62 00:08:25.720 ⇒ 00:08:29.350 Awaish Kumar: So, what should I do, right? If it takes,
63 00:08:29.590 ⇒ 00:08:48.390 Awaish Kumar: if my client wants the updated data every hour, and my… each single query is running, like, for 5-10 minutes, I won’t be able to process everything in an hour to start a new refresh. So, what should I be doing, right? What should we be considering? Like, this is the same simple…
64 00:08:48.610 ⇒ 00:09:00.799 Awaish Kumar: conversation we have with the company, within the colleagues, all the time, and and, like, obviously, we optimize that. Yeah, moving on, like, I…
65 00:09:00.910 ⇒ 00:09:08.519 Awaish Kumar: I just want to know, like, if you can talk me through, like, any one of your projects where, from end to end, where you have worked, like.
66 00:09:08.640 ⇒ 00:09:10.100 Awaish Kumar: end-to-end…
67 00:09:10.260 ⇒ 00:09:22.130 Awaish Kumar: from the… starting from the requirements and delivering a solution, and also, and what is, like, I just want a detailed, walkthrough of that, exact steps, what you…
68 00:09:22.320 ⇒ 00:09:25.110 Awaish Kumar: Implemented, or what you have done, yeah.
69 00:09:25.670 ⇒ 00:09:43.330 Dennis Oliver: Yeah, no, no problem. I mean, I guess, like, for us, it’s more so the analytics project, so with those, generally, we’ll start with the client. Let’s say if we’re doing a brand new implementation, I would say, like, an Adobe implementation, first thing that we would do is we’ll have a meeting with them to see exactly what they’re trying to track.
70 00:09:43.410 ⇒ 00:10:01.560 Dennis Oliver: ask really the whys of why we’re… what data they’re trying to gather, so we’re not trying to track every single button on the website if they’re trying to do that. It’s like, you want to have your specific one, so kind of ask those general questions to figure out exactly the whys of what we are doing. Once we find that, we kind of
71 00:10:01.570 ⇒ 00:10:19.939 Dennis Oliver: create a SDR, so just a solution design reference document, where we take in the events that we’re tracking, we grab the EVARs, or if you’re doing Google tags, we’re going through Google and grabbing the tags, making sure that we have all of the variables that we’re trying to map those to, once we have those created.
72 00:10:19.940 ⇒ 00:10:28.450 Dennis Oliver: we generally go through and create, using GitHub, most… for the most part, we’ll create, each one of those events in there with the…
73 00:10:28.450 ⇒ 00:10:45.759 Dennis Oliver: the data… I guess, trying to… the data events that we’re trying to capture, they will pass over to their developers, because we don’t do the development, like, developing their data layer on our side, so we’ll pass it over to their developers to be able to grab that data from the website. Once that is done, we go through the process of QAing those flows.
74 00:10:45.760 ⇒ 00:10:47.030 Awaish Kumar: How do you capture that?
75 00:10:47.460 ⇒ 00:10:49.279 Dennis Oliver: Capture which one, the data?
76 00:10:49.620 ⇒ 00:10:53.920 Awaish Kumar: the data. I want to know exactly the tools, technologies you use.
77 00:10:54.220 ⇒ 00:10:54.730 Dennis Oliver: Oh, boy.
78 00:10:54.730 ⇒ 00:10:57.529 Awaish Kumar: Obviously, on the website, some engineer will…
79 00:10:57.700 ⇒ 00:11:07.349 Awaish Kumar: send their data, but it will send to somewhere from where you will read, move it around, transform, whatever. So I just wondered exactly what exactly you do.
80 00:11:07.870 ⇒ 00:11:27.699 Dennis Oliver: I mean, well, they send the data through… like, we set it up to… for the data to be captured through Adobe, so it’s… I mean, like, if we’re doing Adobe, for instance, so it’ll be captured through Adobe, so you’re sending that to the Adobe data layer using the environment code that will pass them through Adobe. So you pass them that, that kind of links up the data to the events for what we’re doing, and they’ll send it through the data layer
81 00:11:27.700 ⇒ 00:11:29.180 Dennis Oliver: directly to Adobe.
82 00:11:29.180 ⇒ 00:11:32.970 Dennis Oliver: From Adobe, we capture the data. You have Adobe,
83 00:11:32.970 ⇒ 00:11:52.789 Dennis Oliver: Adobe Workspace, where we kind of take in the data there, and you can kind of look at it through the workspace to create charts, create charts, filter the data out, send it over to… I guess you can run SQL on the charts there, but you can also send it out to a database or a data warehouse. In some cases, not so much for the Adobe clients, but I have with some of the Google clients who send it through
84 00:11:52.790 ⇒ 00:11:59.030 Dennis Oliver: SQL, we’ll send it to the Google warehouse, where they’ll run it through SQL there, too.
85 00:11:59.030 ⇒ 00:12:17.990 Dennis Oliver: kind of do that part of it. I won’t say that’s not really so much the part that we do once it gets to the warehouse, so I would say, I don’t really interact with it on that end so much once it gets inside of the data warehouse, but generally before the data warehouse, we make sure that the data is clean, that they’re receiving, the things that we can kind of edit there beforehand, as far as making sure
86 00:12:17.990 ⇒ 00:12:28.549 Dennis Oliver: through the QA process, what we’re doing is seeing if the events are firing off, if the data that we’re expecting to be collected there is being collected, so if it’s button name, the time of the button is clicked,
87 00:12:28.730 ⇒ 00:12:37.939 Dennis Oliver: Well, I don’t know, the button name, the time the button is clicked, the page name, the URLs that are associated with it, or if it’s the exact events that we’re looking to be tracked, we’re grabbing all that data.
88 00:12:38.210 ⇒ 00:12:45.429 Awaish Kumar: Okay, so where do you, like, see yourself working, like, you work before the warehouse, And,
89 00:12:45.540 ⇒ 00:12:49.820 Awaish Kumar: And the data collection, is that part of your… Job?
90 00:12:50.060 ⇒ 00:13:01.670 Dennis Oliver: Yeah, so everything before the warehouse with the data collection, so right before it gets to that warehouse area, we kind of… we do all of the Adobe analytics, the Google Analytics, like, the analytics part of it.
91 00:13:01.670 ⇒ 00:13:12.699 Dennis Oliver: we get the data pretty much to where it’s clean, and then we give it to the client, and then kind of from there, the client will work through the warehouse. As far as I… from what it’s seeming to me is with the SQL, I guess.
92 00:13:12.700 ⇒ 00:13:25.170 Dennis Oliver: it depends on which data they’re doing. I don’t know if y’all are using more so in-house data with what you’re doing through BrainForge, or is it more so, like, you’re doing the analytics implementations for the clients, in a sense, or is it different?
93 00:13:25.760 ⇒ 00:13:29.479 Awaish Kumar: Yeah, we have a lot of work streams here. Like, normally the…
94 00:13:29.780 ⇒ 00:13:37.089 Awaish Kumar: the thing I’m talking about is more of a, like, we ingest data to the warehouse from different sources.
95 00:13:37.270 ⇒ 00:13:41.319 Awaish Kumar: When it’s in warehouse, we do modeling on top of it, like, we do…
96 00:13:41.690 ⇒ 00:13:44.439 Awaish Kumar: Like, there’s when we do analytics engineering.
97 00:13:44.580 ⇒ 00:13:48.500 Awaish Kumar: And we do that for reporting, right? So we basically create models.
98 00:13:48.610 ⇒ 00:13:52.240 Awaish Kumar: Create different tables, like this, alright.
99 00:13:52.520 ⇒ 00:13:53.770 Awaish Kumar: the…
100 00:13:54.240 ⇒ 00:14:02.570 Awaish Kumar: Medellin architecture, you implement that, come up with some golden dataset that will be used by some BI tools and, end users.
101 00:14:02.700 ⇒ 00:14:06.010 Awaish Kumar: Yeah, there are obviously different,
102 00:14:06.500 ⇒ 00:14:14.420 Awaish Kumar: people in the company that, like, do… that do, like, product analytics, for example, that resembles maybe with what you’re doing, like,
103 00:14:14.570 ⇒ 00:14:18.120 Awaish Kumar: Some of my team members, like, they implement, they ask.
104 00:14:18.600 ⇒ 00:14:37.959 Awaish Kumar: engineers to implement some events that they capture in a mix, for example, in a mix panel of amplitude, where they basically create all these charts and things for the client. Yeah. I think you do kind of similar things with, like, your data being… going to the… maybe directly to GFO, or…
105 00:14:38.180 ⇒ 00:14:43.220 Awaish Kumar: Right. Somewhere else, and where you basically… Help client create charts, analyze.
106 00:14:43.430 ⇒ 00:14:47.370 Awaish Kumar: And give answers, like, okay, what buttons were clicked, whatever.
107 00:14:47.650 ⇒ 00:14:48.840 Awaish Kumar: Yeah. That goes forward.
108 00:14:49.220 ⇒ 00:14:59.589 Dennis Oliver: Yeah, I was gonna say, you kind of said it… kind of did it directly there, like, with GA4, we would gather where… what is being clicked, what is being accepted in that way, and looking at the events that are firing off.
109 00:14:59.610 ⇒ 00:15:10.079 Dennis Oliver: to make sure that they go hand-in-hand with what the client is looking to track. And then, if you see, like, I guess, for instance, if the client is expected to track form completions, but they’re seeing that
110 00:15:10.080 ⇒ 00:15:24.319 Dennis Oliver: I guess last month they had so many foreign completions, but this next month they had an extreme drop-off, and that’s kind of where we’re coming in. Hey, we see… we have this alert fire off in GA4, where we’re trying to see why is there such a big drop-off in data? And it’s like, alright, well, this is where we come in and say, well, let’s look at.
111 00:15:24.320 ⇒ 00:15:28.119 Awaish Kumar: But my point is that you do all that in GA4 itself, right?
112 00:15:28.360 ⇒ 00:15:29.050 Dennis Oliver: Right.
113 00:15:29.300 ⇒ 00:15:29.880 Awaish Kumar: Yeah.
114 00:15:30.310 ⇒ 00:15:34.040 Awaish Kumar: Okay, yeah, I understand, so I… yeah, and what…
115 00:15:34.540 ⇒ 00:15:40.669 Awaish Kumar: that’s, like, that is what I want to know. Like, there are different… in analytics engineering also, there are a lot of…
116 00:15:41.010 ⇒ 00:15:50.610 Awaish Kumar: different paths you can take, right? One is, like, you can… GoData goes to warehouse, you kind of do modeling, work with BI tools, and…
117 00:15:50.660 ⇒ 00:16:03.349 Awaish Kumar: try to create models in… or tables in the warehouse, and then move it to, like, Power BI, Tableau, and do that kind of sort. That sort of thing. The other part is, like, also product analytics, where
118 00:16:03.520 ⇒ 00:16:05.939 Awaish Kumar: To basically get the data really clean.
119 00:16:06.220 ⇒ 00:16:10.069 Awaish Kumar: GFO, or some… something like that, mixed panel, and basically…
120 00:16:10.240 ⇒ 00:16:19.439 Awaish Kumar: analyze it there, basically, and try to maintain that. So, yeah. Okay, I think I have understood,
121 00:16:19.860 ⇒ 00:16:28.430 Awaish Kumar: And, Yeah, apart from that, I just have, one question on…
122 00:16:28.780 ⇒ 00:16:34.480 Awaish Kumar: Basically, how do you… like, for example, if you come up with some findings, Huh.
123 00:16:34.750 ⇒ 00:16:36.849 Awaish Kumar: on GF4 or somewhere.
124 00:16:37.150 ⇒ 00:16:44.490 Awaish Kumar: And there’s one… Non-technical stakeholder, maybe your director or someone who…
125 00:16:44.600 ⇒ 00:16:47.409 Awaish Kumar: Who is not familiar with how data works, or…
126 00:16:47.740 ⇒ 00:16:56.309 Awaish Kumar: So, and if it disagrees with your, findings, how would you convince him, or how would you bake your…
127 00:16:56.750 ⇒ 00:16:57.780 Awaish Kumar: Findings.
128 00:16:58.550 ⇒ 00:17:02.189 Dennis Oliver: I mean, generally with funders and things like that, I guess, like, I’ll…
129 00:17:02.200 ⇒ 00:17:12.300 Dennis Oliver: I like to create slideshows. One of the easier ways of kind of formulating that is going from the charts and things that we have in GA4 and stuff like that can be kind of difficult.
130 00:17:12.300 ⇒ 00:17:28.629 Dennis Oliver: I think one of the best tools that I have is having the software engineering background that I’ve had before with these things. I’m able to kind of explain it a little bit differently. So, I would say, like, my total background would be before doing software engineering and things like that. I went to school for psychology.
131 00:17:28.630 ⇒ 00:17:46.170 Dennis Oliver: also did sales for a little while, so I have a lot of experience kind of talking with clients when it comes to things in that matter. So, one of the things… like I said, kind of really generally is, like, I like to create slideshows, so I might grab the slideshow and say, hey, this is what we’re looking at, or this is the data that’s coming in that is wrong, or…
132 00:17:46.170 ⇒ 00:17:54.939 Dennis Oliver: this is… well, I… let me… I’m trying to think of a specific event. Let’s say, yeah, I’m thinking of it this way, with one client, it was a healthcare client.
133 00:17:54.940 ⇒ 00:18:07.429 Dennis Oliver: we was trying to track a majority of the clicks on their website. Well, they was trying to track, like, pretty much everywhere where certain clicks was happening, and they were using just, like, specific… they were not using specific,
134 00:18:07.540 ⇒ 00:18:13.020 Dennis Oliver: like, specific, I don’t know, I’m going blank on the word, but specific selectors.
135 00:18:13.020 ⇒ 00:18:30.820 Dennis Oliver: on their website for different clicks that they were trying to track, and it was getting an overabundance of numbers of clicks that was showing in. And they kind of was looking at that as being a good thing, because they thought it was a lot of traffic occurring on the website, so it was something that the stakeholder didn’t really want to hear, that it’s not performing that well, you’re just not being very specific in your clicks.
136 00:18:30.960 ⇒ 00:18:53.859 Dennis Oliver: So from that, I was kind of created a slideshow. I showed, hey, this button has this same selector, showed where all the buttons had the same selectors that they weren’t expecting to see, showed where those clicks was occurring, and I was just letting them know how you want to be intentional about how you have your website set up, so you want to have specific clicks that track this in a specific event that might monitor this. You can track all of the clicks if you want to.
137 00:18:53.860 ⇒ 00:18:59.470 Dennis Oliver: I told them I don’t recommend that, because if you have, like… what decisions are you making off of this data?
138 00:18:59.470 ⇒ 00:19:23.560 Dennis Oliver: But if we’re tracking, you know, clicks to this form, or clicks to this CTA, and we have those set up to where it’s kind of, intentional, then you can make informed decisions based off of that data. So it was something that they didn’t want to hear at first, but at the end of it, they learned a lot about how their website worked, and how the new CTAs and things that it was adding to the website are actually tracked and can, like, have a meaningful difference for what they’re trying to do.
139 00:19:24.300 ⇒ 00:19:31.440 Awaish Kumar: Okay, and then I think I just want to ask, like, where do you see yourself,
140 00:19:31.930 ⇒ 00:19:35.049 Awaish Kumar: To be in the next role, like, doing the same.
141 00:19:35.160 ⇒ 00:19:36.750 Awaish Kumar: Okay, welcome.
142 00:19:37.090 ⇒ 00:19:43.810 Awaish Kumar: working in GFO, product, like, or other product analytics tools, or you would like to…
143 00:19:43.970 ⇒ 00:19:52.369 Awaish Kumar: Move into more, like, analytics engineering, with using dbt, or working with data, which is in warehouse, building…
144 00:19:52.490 ⇒ 00:19:57.889 Awaish Kumar: kind of a… Like, summary tables, and all those tables on top of them.
145 00:19:58.440 ⇒ 00:20:00.350 Awaish Kumar: So where do you see yourself grow?
146 00:20:01.030 ⇒ 00:20:15.139 Dennis Oliver: I mean, I feel like so far, once again, kind of like what you brought up there, I’ve gotten a lot of experience working with the first side, the front side of the data, as far as Google Analytics and things like that. I’m not against the warehousing and stuff like that, so, like.
147 00:20:15.220 ⇒ 00:20:16.300 Dennis Oliver: I’ve…
148 00:20:16.370 ⇒ 00:20:31.170 Dennis Oliver: begin creating a website on my own as well. Like, this is a side thing that I work on, and with some of that, I’ve done query… like, I have query charts set up to be able to track, like, certain clicks and what data are being feeded… fed in based off of a form that’s created on the website.
149 00:20:31.170 ⇒ 00:20:43.370 Dennis Oliver: So it’s like, a lot of those things, I’m still… like, I’m new at, but it’s interesting. I think the thing to me with a lot of this, with technology, is that it’s kind of new and refreshing. I feel like we’re talking to computers in a language that
150 00:20:43.370 ⇒ 00:21:00.699 Dennis Oliver: only, like, I don’t know, 1% of the people on the planet know. So it’s like, learning how to, I guess, formulate… no, my bad… learning how to formulate the data on the back end, after it’s… like, formulate the data on the back end once it’s inside of the warehouse to make informed decisions is pretty interesting to me as well.
151 00:21:00.700 ⇒ 00:21:03.500 Dennis Oliver: I guess, like, I’m kind of enjoying the…
152 00:21:03.630 ⇒ 00:21:11.510 Dennis Oliver: the journey of learning all of this. I went from learning general, like, this coding in general in JavaScript, Python a little bit.
153 00:21:11.510 ⇒ 00:21:23.770 Dennis Oliver: C-sharp to learning how, like, from creating something, how does the analytics kind of play a role in all of it? And I think, like, I feel like I have a pretty good hold on that, but I would, I guess.
154 00:21:23.910 ⇒ 00:21:40.350 Dennis Oliver: the question I would have for you guys, or have for you in this one, is, like, I’m open to learning how to data warehouse and do things on that side of it, but would it be more so, like, a specialization in, like, a particular tool, or is it more, like, I guess, like, you’re working on…
155 00:21:40.350 ⇒ 00:21:45.519 Awaish Kumar: Analytic engineering is, basically analytics engineering, right? We… Right.
156 00:21:45.880 ⇒ 00:21:49.159 Awaish Kumar: we are not… I’m not talking about any tools right now, right?
157 00:21:49.770 ⇒ 00:22:06.080 Awaish Kumar: more like the concepts of the analytics engineering, what you do in that field, what kind of tools you might have to learn. So we are not against someone, like… Obviously, someone is expert enough to work on one thing, he can also work on
158 00:22:06.260 ⇒ 00:22:23.509 Awaish Kumar: similar tool, right? Only thing I want to know, like, and I’m… I just asked this question because we already have these two different work streams in our company, so there are people who do product analytics, right, only. And they are… they have their… like, that’s their expertise.
159 00:22:23.620 ⇒ 00:22:25.549 Awaish Kumar: Right? We don’t want them to, like…
160 00:22:25.710 ⇒ 00:22:42.519 Awaish Kumar: do whatever, like, we are doing, because they are just good at what they are doing, and we have the clients that demand that kind of work. So, and we also have the people who do analytics engineering, and, like, in warehousing, and we have clients that also require these services.
161 00:22:42.860 ⇒ 00:22:47.609 Awaish Kumar: It’s just a matter of, like, where you want to see yourself, in this…
162 00:22:48.010 ⇒ 00:23:03.629 Dennis Oliver: Oh, well, yeah, I would say in that term, like, while I’m open to learning, like, data warehousing, so far I have been, I guess, out… I would be interested in, like, really learning and becoming, I guess you would say, specialized in, like, Adobe.
163 00:23:03.630 ⇒ 00:23:15.330 Dennis Oliver: And with that being, like, I don’t mind picking up Tableau and stuff, but I also enjoy, like, the… like I was saying, kind of the architecture phase of it. It’s just kind of a step above where I’m at right now with the engineering phase, where…
164 00:23:15.330 ⇒ 00:23:29.649 Dennis Oliver: it’s like, we’re doing the work, but I guess, like, the look down and just setting up the modeling of the data that’s going to be ingested by the warehouse has been pretty, I guess, interesting to me. As a kid, I wanted to be an architect, like, just like a regular building architect.
165 00:23:29.650 ⇒ 00:23:45.469 Dennis Oliver: But I felt like it was too much math, so it’s kind of cool to be able to go to, you know, like, do the work that I’m doing now, and then still have the availability to become an architect, too, kind of in a different realm, like, creating the data pipelines that so many people ingest. So, I would say, personally.
166 00:23:45.470 ⇒ 00:23:52.400 Dennis Oliver: I would like the architect role a little bit better, just because I’ve done it, like, I’ve been doing it for a while, and I feel like it would be a progression…
167 00:23:52.400 ⇒ 00:23:55.830 Dennis Oliver: It would be a step above where I’m at now.
168 00:23:55.870 ⇒ 00:24:03.600 Dennis Oliver: Versus, I think, like, with the warehousing side of it, while it seems cool, and I work with coworkers who have the experience in Tableau and things like that.
169 00:24:03.790 ⇒ 00:24:20.340 Dennis Oliver: I feel like I’ll be starting back over again, kind of at ground zero, with, you know, I wouldn’t say ground zero, because I understand it more so now, but I think that would be the only thing, is that for, like, growth-wise, I would like to move up to architect, which would be the next level for where I’m at now as an engineer, so…
170 00:24:20.570 ⇒ 00:24:21.210 Dennis Oliver: Yep.
171 00:24:21.650 ⇒ 00:24:22.510 Awaish Kumar: Okay.
172 00:24:22.650 ⇒ 00:24:26.900 Awaish Kumar: Yeah, thank you for your time, Mike. That’s it for me.
173 00:24:27.060 ⇒ 00:24:29.769 Awaish Kumar: And if you have any questions, yeah, you may ask.
174 00:24:30.140 ⇒ 00:24:41.989 Dennis Oliver: Okay. I was trying to think if I had a good one. You kind of gave me a couple of good ones there. well, you answered some of my questions within the workflow kind of question as far as what you guys do.
175 00:24:42.000 ⇒ 00:24:50.890 Dennis Oliver: I don’t know, I guess, with the roles that you have, I know I was told before that this is more like a 1099 type of role.
176 00:24:50.960 ⇒ 00:24:59.229 Dennis Oliver: that you were doing, and I guess currently I’m, like, a W-2 employee, so I was wondering, like, with the 1099s, is that something that would be, like.
177 00:24:59.290 ⇒ 00:25:11.640 Dennis Oliver: a contract to hire, or is it just, like, is the company just running off of 1099s now? And if there’s, like, is there, like, a time associated with the 1099? Is it, like, you have a contract for a year, or is it, like…
178 00:25:11.740 ⇒ 00:25:13.499 Dennis Oliver: Depending on what clients you have.
179 00:25:14.400 ⇒ 00:25:19.959 Awaish Kumar: Like, it depends on… I am not the best person to answer these kind of things, that may be the HR.
180 00:25:19.960 ⇒ 00:25:20.610 Dennis Oliver: Totally.
181 00:25:20.940 ⇒ 00:25:35.989 Awaish Kumar: But, yeah, our company hires full-time employees in the US only, and for the outsiders, it’s more the contract roles. And for the US also, it can be contract or full-time. It depends.
182 00:25:36.180 ⇒ 00:25:43.640 Awaish Kumar: It depends, like, initially, normally we start with the contract, right, doing, like, maybe part-time joining, and…
183 00:25:44.000 ⇒ 00:25:55.270 Awaish Kumar: like, it will be paid, obviously, for one, two weeks, so you just see if it works out, and if it works, then, like, the company can, like, hire you as a full-time employee, if you’re in the US.
184 00:25:55.350 ⇒ 00:26:08.520 Awaish Kumar: And if you are not in U.S, then it’s obviously a contract role. It could be full-time contract role, but it’s still a contract, right? Because VINFORGE is not… it’s the only entity in the US, it’s not…
185 00:26:08.780 ⇒ 00:26:10.309 Awaish Kumar: In other countries.
186 00:26:10.730 ⇒ 00:26:24.829 Dennis Oliver: Gotcha. Yeah, I guess that’s the kind of the only thing that I look at and I kind of play around with. It seems, from what I’ve seen, it seems like you guys work on some pretty cool stuff, and I see a lot of, I guess, interconnectivity with the work that I do now.
187 00:26:24.880 ⇒ 00:26:39.269 Dennis Oliver: with it, just kind of a different logo, but then I guess my worry is I’m a W-2 employee now, versus, like, 1099, so I’m not sure about the different effects of that and, like, how it affects a lot of different stuff there, so it’s kind of, you know, interesting.
188 00:26:39.270 ⇒ 00:26:43.699 Awaish Kumar: You can, like, clarify that with the recruiter or someone, like, yeah.
189 00:26:44.090 ⇒ 00:26:44.770 Dennis Oliver: Gotcha.
190 00:26:45.100 ⇒ 00:26:45.420 Awaish Kumar: Okay.
191 00:26:45.420 ⇒ 00:26:45.980 Dennis Oliver: Whoa.
192 00:26:46.280 ⇒ 00:26:50.989 Dennis Oliver: Yeah, I was gonna say, I can’t really think too much more. I really appreciate your time today, so I appreciate you.
193 00:26:51.250 ⇒ 00:26:55.620 Awaish Kumar: Yeah, no worries, thank you for your time, and… See you.
194 00:26:56.050 ⇒ 00:26:56.879 Dennis Oliver: See you later.