Meeting Title: Brainforge Data Engineering Interview Date: 2026-03-11 Meeting participants: Chadd McNicholas, Awaish Kumar
WEBVTT
1 00:01:48.760 ⇒ 00:01:49.540 Awaish Kumar: I…
2 00:01:50.230 ⇒ 00:01:51.430 Chadd McNicholas: Hey, I wish.
3 00:01:52.350 ⇒ 00:01:53.890 Awaish Kumar: Yes, how are you doing?
4 00:01:54.260 ⇒ 00:01:56.520 Chadd McNicholas: I’m doing good. Did I pronounce that right?
5 00:01:56.760 ⇒ 00:01:58.980 Awaish Kumar: Yes. How can I pronounce your name?
6 00:01:59.170 ⇒ 00:01:59.910 Chadd McNicholas: Chad.
7 00:02:00.480 ⇒ 00:02:01.400 Awaish Kumar: Chad, okay.
8 00:02:01.400 ⇒ 00:02:01.730 Chadd McNicholas: Yeah.
9 00:02:01.730 ⇒ 00:02:03.479 Awaish Kumar: And where are you located?
10 00:02:03.480 ⇒ 00:02:05.229 Chadd McNicholas: I am in Austin, Texas.
11 00:02:05.940 ⇒ 00:02:06.880 Awaish Kumar: Okay.
12 00:02:07.440 ⇒ 00:02:13.490 Awaish Kumar: Yeah, so my name is Avesh, I’m kind of, leading data engineering at Brainforge.
13 00:02:13.720 ⇒ 00:02:16.069 Awaish Kumar: And, Baron Forge is a…
14 00:02:16.530 ⇒ 00:02:25.059 Awaish Kumar: kind of providing data and AI consultancy services to mid- to large enterprises. Most of our clients are in the US, and
15 00:02:26.190 ⇒ 00:02:30.550 Awaish Kumar: Yeah, our employees are, like, basically…
16 00:02:31.240 ⇒ 00:02:36.849 Awaish Kumar: from across the world. So, we have people from US, Europe, Asia.
17 00:02:37.970 ⇒ 00:02:40.609 Awaish Kumar: Almost all over, and we work…
18 00:02:41.060 ⇒ 00:02:56.549 Awaish Kumar: I know our time zone’s really flexible, but yeah, we need to overlap, with our team members for some hours, like, at least 4 hours, so we can, communicate regarding updates and all of that. So, yeah, that’s basically it.
19 00:02:56.670 ⇒ 00:03:00.750 Awaish Kumar: For my side, yeah, let’s get started with your introduction.
20 00:03:01.420 ⇒ 00:03:07.379 Chadd McNicholas: Yeah, so I’m John from Nicholas, you just want me to, like, go over my career history, or just what I’m…
21 00:03:07.770 ⇒ 00:03:08.700 Chadd McNicholas: Mike.
22 00:03:09.530 ⇒ 00:03:11.949 Chadd McNicholas: What’s relevant, or what would you like to hear?
23 00:03:11.950 ⇒ 00:03:13.289 Awaish Kumar: Whatever you like.
24 00:03:13.500 ⇒ 00:03:15.740 Chadd McNicholas: So,
25 00:03:16.070 ⇒ 00:03:29.820 Chadd McNicholas: I think you saw my video already, kind of, talking about a lot of my career history, but basically, it boils down to, I… I believe that all,
26 00:03:30.010 ⇒ 00:03:41.639 Chadd McNicholas: All decisions really need to be data-driven, and if you can’t… you can’t improve it unless you can measure it, and so by providing means of measurement, you can then improve almost anything.
27 00:03:41.750 ⇒ 00:03:51.120 Chadd McNicholas: And so that’s something that I just follow very… that’s, like, a mantra I follow very closely, so… and apply that to my work and personal endeavors.
28 00:03:51.890 ⇒ 00:03:59.079 Chadd McNicholas: About this role specifically, I really enjoyed my time at Tableau. I was a solutions architect.
29 00:03:59.080 ⇒ 00:04:24.059 Chadd McNicholas: Post-sales, paid consulting, building out entire deployments for, like, I don’t know, over 70 clients. This was specifically in the BI space, Tableau, obviously, where I would, work with the C-levels, or VPs, and try to understand their vision of the deployment, and then I worked with the engineering and tech side, the BI side, and then even some of the business verticals to ensure that
30 00:04:24.060 ⇒ 00:04:29.750 Chadd McNicholas: were executing properly upon that vision and putting out the platform they really wanted.
31 00:04:30.110 ⇒ 00:04:32.720 Chadd McNicholas: Otherwise, I…
32 00:04:33.690 ⇒ 00:04:49.379 Chadd McNicholas: Let’s see, what else? I’ve done, so at Indeed, I kind of took that same, like, consulting, like, solutions architecture, approach, treated, like, my users as customers, and,
33 00:04:49.590 ⇒ 00:04:59.459 Chadd McNicholas: kind of executed on my vision of making Indeed more data-driven by democratizing analytics for the entire company, as opposed to having, like, a centralized BI team.
34 00:04:59.460 ⇒ 00:05:11.760 Chadd McNicholas: We had one, just because some teams just simply didn’t have the, you know, the resources for it, but for ones that did, I enabled them to use the platform, integrate their data, and do analytics per best practices.
35 00:05:11.840 ⇒ 00:05:13.199 Chadd McNicholas: For that,
36 00:05:14.070 ⇒ 00:05:24.960 Chadd McNicholas: Yeah, that’s kind of my story. Outside of work, I’m heavy into fitness and nutrition, health optimization, also kind of data-driven there, too.
37 00:05:26.180 ⇒ 00:05:32.389 Awaish Kumar: And, like, what… Why you, like, looking for a new role right now?
38 00:05:32.840 ⇒ 00:05:39.440 Chadd McNicholas: I was laid off in, last year, and, that’s why I’m looking for a new role.
39 00:05:40.780 ⇒ 00:05:46.069 Awaish Kumar: Okay, can we talk about your… any… one of your latest projects?
40 00:05:46.420 ⇒ 00:05:47.330 Awaish Kumar: And, like.
41 00:05:47.330 ⇒ 00:05:48.210 Chadd McNicholas: Yeah.
42 00:05:48.460 ⇒ 00:05:49.860 Awaish Kumar: Oh, my…
43 00:05:49.900 ⇒ 00:06:03.240 Chadd McNicholas: My latest project was probably the largest single project that I worked on, was the migration from Tableau Server to Tableau Cloud at Indeed.
44 00:06:03.510 ⇒ 00:06:10.599 Chadd McNicholas: So, Salesforce, closed a deal with Indeed to,
45 00:06:11.050 ⇒ 00:06:35.140 Chadd McNicholas: basically, they got them a sweetheart deal to switch to cloud over server, and since I and my team were managed the platform deployment COE and so on, it was on me to ensure that we had a smooth migration. So, I started with understanding the risks and challenges with going from one platform to the other, because it is not seamless.
46 00:06:35.320 ⇒ 00:06:47.310 Chadd McNicholas: There are a lot of limitations when you go to a cloud from self-hosted. And then, since I only had a team of four people reporting to me, there was no way the four of us were going to migrate
47 00:06:47.310 ⇒ 00:06:58.960 Chadd McNicholas: over 12,000 users and almost 9,000 published assets, each which required some level of handholding for every, every item. So, I interviewed vendors.
48 00:06:58.960 ⇒ 00:07:04.090 Chadd McNicholas: To be the boots on the ground for the migration. Brought them on board,
49 00:07:04.090 ⇒ 00:07:28.900 Chadd McNicholas: worked with the business verticals on, their, concerns and limitations. For example, sales and finance, they could not be impacted, or their analytics could not be impacted for, you know, end of month, end of quarter, end of year. So I had to take those into consideration when I was building my cohorts and sprints for the migrations. Also, understanding the various connection types and how
50 00:07:29.500 ⇒ 00:07:33.659 Chadd McNicholas: What would be required to migrate them if,
51 00:07:33.660 ⇒ 00:07:57.650 Chadd McNicholas: Some of them, like, were simply not compatible, and we have to find a different method of, pipelining those data, while others were just, publish and re-enter your password. So, putting together playbooks for all the different scenarios, working with the users and, and support channels, mostly in Slack, and some email, but Slack was definitely better, and so on.
52 00:07:57.700 ⇒ 00:08:00.260 Chadd McNicholas: All the way to,
53 00:08:00.670 ⇒ 00:08:08.980 Chadd McNicholas: Yeah, just getting everyone migrated, all the orgs migrated, in about 10 months with minimal impact to our users.
54 00:08:10.070 ⇒ 00:08:11.190 Awaish Kumar: And, yeah.
55 00:08:11.720 ⇒ 00:08:15.520 Awaish Kumar: Like, and what are you looking for in a new role?
56 00:08:16.050 ⇒ 00:08:33.789 Chadd McNicholas: I’m extremely hands-on, I like solving problems, I like tinkering, I, am analytics-focused, so, like, my work at General Motors was very prototypical, and I really enjoyed doing that work where I was, kind of pathfinding,
57 00:08:33.789 ⇒ 00:08:40.039 Chadd McNicholas: new ways to, work with the data that they had that they had no, structure for.
58 00:08:40.090 ⇒ 00:08:41.959 Chadd McNicholas: At the time,
59 00:08:42.090 ⇒ 00:09:04.539 Chadd McNicholas: And I just really enjoy solving problems, doing analytics, and just innovating. And so that’s really what I want to do. I was a manager at my last role. I’m not necessarily looking to be a manager now. I enjoyed it, it was very fulfilling, but I also really enjoy being hands-on with analytics and data and problem solving.
60 00:09:06.070 ⇒ 00:09:14.700 Awaish Kumar: Okay, yeah, we are hiring for, like, analytics engineers, data engineers, Data analysts, so, yeah.
61 00:09:14.700 ⇒ 00:09:15.050 Chadd McNicholas: Yeah.
62 00:09:15.940 ⇒ 00:09:22.500 Awaish Kumar: All the parts, one can take, in a data world.
63 00:09:22.750 ⇒ 00:09:28.090 Awaish Kumar: Apart from that, like, I have some, like…
64 00:09:28.360 ⇒ 00:09:30.950 Awaish Kumar: Do you… do you want to talk about any…
65 00:09:31.100 ⇒ 00:09:35.740 Awaish Kumar: Like, complex project in terms of analytics.
66 00:09:36.140 ⇒ 00:09:39.009 Awaish Kumar: Like, really modeling and all of that.
67 00:09:39.520 ⇒ 00:09:44.540 Awaish Kumar: Complexity in terms of… Data modeling, warehouse,
68 00:09:44.850 ⇒ 00:09:47.189 Awaish Kumar: Architecting the warehouse and things like that.
69 00:09:47.610 ⇒ 00:10:04.379 Chadd McNicholas: Okay, so, full disclosure, I have not architected a data warehouse from scratch, like a full infrastructure. I’ve worked closely with teams that did and supported them. However, what I did do, like, for example, at General Motors was,
70 00:10:04.480 ⇒ 00:10:12.130 Chadd McNicholas: they had multi-trillion record datasets in Hadoop, and… Excuse me,
71 00:10:12.670 ⇒ 00:10:32.479 Chadd McNicholas: And the engineers were downloading single files off HDFS and trying to analyze them, and they brought me in to, figure out a holistic, solution. And so I experimented with various data pipelines, eventually, going with Hive to, do some, building, building,
72 00:10:33.130 ⇒ 00:10:46.940 Chadd McNicholas: a reporting layer, an aggregated reporting layer of… so reducing 400 trillion… no, 4 trillion records to about 300 million, to gain the insights they needed. At the time, we didn’t have, a good place to.
73 00:10:47.000 ⇒ 00:11:04.960 Chadd McNicholas: a semantic layer to do it, so I kind of… I just built the table in Hadoop, and then ingested… incrementally ingested it into Tableau as columnar extracts as an interim solution while we were exploring things like, Dreamio and Druid and Greenplum and other,
74 00:11:04.960 ⇒ 00:11:08.329 Chadd McNicholas: Options, but, .
75 00:11:09.900 ⇒ 00:11:12.860 Awaish Kumar: what exactly did you do? Like, I want to, like…
76 00:11:13.220 ⇒ 00:11:16.419 Awaish Kumar: the data, I understand,
77 00:11:17.140 ⇒ 00:11:27.649 Awaish Kumar: the story behind the project, I want to get more technical into it. Like, what tools you use, or what, for example, you wrote SQL, whatever.
78 00:11:27.650 ⇒ 00:11:47.589 Chadd McNicholas: Okay, yeah, yeah, specifically for this project, it was almost entirely SQL. To be fair, this was Hive SQL, and I particularly… we were a couple versions behind, so things like, Distinct were big no-no’s, because it would, move the, the whole MapReduce to a single node.
79 00:11:47.590 ⇒ 00:12:01.729 Chadd McNicholas: And when you’re dealing with huge data sets, they would literally run for years to finish. So you had to really do a lot of performance optimization of your SQL to make sure it would run
80 00:12:01.730 ⇒ 00:12:09.440 Chadd McNicholas: and make sure it plays nicely with all the other users who are using the same cluster for their analytics.
81 00:12:09.900 ⇒ 00:12:28.909 Chadd McNicholas: So, yeah, I built out the ETL for that, and then, and then I used Tableau on top of that. For a couple other projects, I also used KNIME. I’m not sure if you’re familiar with that, K-N-I-M-E, which was powerful. And then, at Indeed, I used Python for some things, too.
82 00:12:30.390 ⇒ 00:12:31.640 Awaish Kumar: Okay, so…
83 00:12:31.860 ⇒ 00:12:41.370 Awaish Kumar: when you, you know, you used, like, ETL, Hive for ETL, and so were you moving things, the data to somewhere else, or…
84 00:12:41.480 ⇒ 00:12:45.730 Awaish Kumar: Was it in the same… Thank God.
85 00:12:45.730 ⇒ 00:12:52.560 Chadd McNicholas: Yeah, so it was… it was still all staying in HDFS, because I had nowhere else to put it. There was no,
86 00:12:52.850 ⇒ 00:13:10.430 Chadd McNicholas: We didn’t have… there was no Snowflake or anything at the time, so it was just working HDFS, and then connecting Tableau directly, via a Hadoop connector, to then ingest the reporting, the… basically the data store, and copy it over and incrementally load it.
87 00:13:11.240 ⇒ 00:13:16.690 Awaish Kumar: So, did you did any kind of, like, modeling, like, star schemas, or…
88 00:13:17.040 ⇒ 00:13:27.960 Chadd McNicholas: I did at Modernize, we were a click shop, and, MySQL, and, yeah, I inherited,
89 00:13:28.320 ⇒ 00:13:39.090 Chadd McNicholas: And, like, the most, the most snowflake of schemas you could imagine. And, like, there were,
90 00:13:39.530 ⇒ 00:13:44.939 Chadd McNicholas: bridge tables and such that were completely unnecessary, and so on. It was just, like, it was as…
91 00:13:45.040 ⇒ 00:14:08.389 Chadd McNicholas: Anyway, so I did… I did some optimization of those models to… to make it cleaner and easier to run. Also, I’ve conceptually, have done models, like, for example, inside Tableau, you can build your semantic layer in there using relations and so on, for it. And I, I mean, just also academically, I’ve done many exercises in data modeling.
92 00:14:09.170 ⇒ 00:14:11.350 Awaish Kumar: Okay, have you used dbt?
93 00:14:11.650 ⇒ 00:14:14.059 Chadd McNicholas: I took the class on it.
94 00:14:14.150 ⇒ 00:14:35.750 Chadd McNicholas: I mean, conceptually, it’s… it’s… what I love… so, have I used it at work? No. But, the concept is very straightforward. It’s parameterization of your SQL, and getting into CICD for that integration, and then having your, really making it easier to do your bronze, silver, gold medallion layers, and so on.
95 00:14:35.750 ⇒ 00:14:42.739 Chadd McNicholas: Yeah, the concepts are very straightforward, but I haven’t formally done that in my work with dbt, no.
96 00:14:42.740 ⇒ 00:14:46.240 Chadd McNicholas: But it’s… it’s pretty easy.
97 00:14:46.380 ⇒ 00:14:47.190 Awaish Kumar: Yeah, yeah.
98 00:14:47.310 ⇒ 00:14:50.949 Awaish Kumar: It is easy, so, like, if you have rote skull queries, then…
99 00:14:51.070 ⇒ 00:14:58.120 Awaish Kumar: It’s kind of… essentially, it’s the same thing, right? You’re writing queries for… Just,
100 00:14:58.290 ⇒ 00:15:03.969 Awaish Kumar: Just a way to parametrize it and version control it.
101 00:15:04.570 ⇒ 00:15:07.199 Awaish Kumar: And, yeah, make it more modular.
102 00:15:07.500 ⇒ 00:15:24.150 Chadd McNicholas: Yeah, obviously it’s gonna have its own idiosyncrasies, that would take some learning, like, what’s the… what’s the syntax, you know, in the, you know, in the files and so on, but and also it’s gonna have some shortcuts, that are good, particularly for, like, testing and QA, that…
103 00:15:24.310 ⇒ 00:15:25.460 Chadd McNicholas: Save time.
104 00:15:26.140 ⇒ 00:15:32.320 Awaish Kumar: So how would you, rate yourself, from out of 10, like, in SQL, Python.
105 00:15:34.640 ⇒ 00:15:52.009 Chadd McNicholas: Sql, I put myself near expert. Might be a little rusty right now, but since it’s been a couple months since I’ve done it, but yeah, I definitely expert, window functions, CTEs, you name it, self-joins, Cartesian joins, you name… whatever.
106 00:15:52.210 ⇒ 00:15:54.510 Chadd McNicholas: Python,
107 00:15:54.800 ⇒ 00:16:11.969 Chadd McNicholas: I… so I use Python for scripting to get the job done, working with APIs, data integration, and so on. I definitely have to have Google open when I use it. And now, actually, like, with the advent of, with agent… with agenda coding, like.
108 00:16:11.970 ⇒ 00:16:33.170 Chadd McNicholas: using applications like Cursor, it’s, like, dirt easy. You just have to know, make sure it’s actually doing what you want and not sneaking anything in. But I’ve written plenty of scripts and done plenty of prototyping in Python, but I don’t use it every day, so I have to keep, like, a reference open when I use it, because I do sometimes forget, like, syntax when I’m doing it.
109 00:16:34.040 ⇒ 00:16:34.850 Awaish Kumar: Okay.
110 00:16:34.990 ⇒ 00:16:39.210 Awaish Kumar: But do you understand how it works as a backend?
111 00:16:39.740 ⇒ 00:16:42.899 Awaish Kumar: like, how, for example, the concept of the Python…
112 00:16:43.160 ⇒ 00:16:49.450 Awaish Kumar: what are the list comprehensions? What are the… how the memory gets managed in Python?
113 00:16:50.380 ⇒ 00:16:52.090 Chadd McNicholas: Yeah, yeah,
114 00:16:52.470 ⇒ 00:16:57.179 Chadd McNicholas: Yeah, I’ve done plenty of coding over the years, they don’t really… it’s not that much different.
115 00:16:58.230 ⇒ 00:17:02.970 Awaish Kumar: Okay, so, like.
116 00:17:03.330 ⇒ 00:17:08.050 Chadd McNicholas: like, working with lists and stuff in Pythons, it starts at 0 and all that.
117 00:17:09.530 ⇒ 00:17:13.470 Awaish Kumar: Like, how would you… Like,
118 00:17:13.670 ⇒ 00:17:17.479 Awaish Kumar: how the memory management in Python will work?
119 00:17:18.410 ⇒ 00:17:24.169 Chadd McNicholas: No, I haven’t gone into, like, that level of… Like, how much,
120 00:17:25.260 ⇒ 00:17:32.180 Chadd McNicholas: Are you talking about, like, just, like, the stacks and such? I mean, like, we’re getting, like, assembly language level, discussion here, or…
121 00:17:33.230 ⇒ 00:17:41.320 Awaish Kumar: And, like, it’s just a… Simple concept, like, how, like, if you create, variables and different scopes.
122 00:17:41.320 ⇒ 00:17:49.329 Chadd McNicholas: Oh, yeah, so yeah, if something’s created in a function, and you assign the same variable outside the function, which gets priority, and so on, yeah.
123 00:17:50.920 ⇒ 00:17:51.670 Awaish Kumar: Okay.
124 00:17:51.900 ⇒ 00:17:56.910 Awaish Kumar: So, apart from that,
125 00:17:57.800 ⇒ 00:18:03.819 Awaish Kumar: like, are you familiar with the latest warehouses, like Snowflake, BigQuery?
126 00:18:04.520 ⇒ 00:18:17.419 Chadd McNicholas: Yeah, so I’ve worked with mostly Snowflake, some BigQuery, but most of my work with BigQuery, was migrating from BigQuery to Snowflake, because we wanted to get fully off GCP when I was at Indeed.
127 00:18:18.080 ⇒ 00:18:35.760 Chadd McNicholas: But yeah, I owned a small schema for metadata management for my platforms there, but I also worked a lot with the BI teams, with their data in Snowflake, to ensure they were properly querying and ingesting data into the BI platform.
128 00:18:38.350 ⇒ 00:18:43.870 Awaish Kumar: Okay, and what do you think the difference is between BigQuery and Snowflake?
129 00:18:44.310 ⇒ 00:18:50.300 Chadd McNicholas: The big difference between BigQuery and Snowflake, BigQuery is more expensive.
130 00:18:51.460 ⇒ 00:18:57.559 Chadd McNicholas: That’s… that’s about all I know. That’s my… I don’t… I haven’t had much hands-on with a BigQuery other than migrating people off it.
131 00:18:58.340 ⇒ 00:19:01.559 Awaish Kumar: Okay, like, why do you think it’s expensive?
132 00:19:02.350 ⇒ 00:19:06.470 Chadd McNicholas: the licensing, that’s about all I know.
133 00:19:06.640 ⇒ 00:19:09.049 Chadd McNicholas: I don’t know their licensing model.
134 00:19:10.430 ⇒ 00:19:11.130 Awaish Kumar: Okay.
135 00:19:11.130 ⇒ 00:19:12.260 Chadd McNicholas: Yeah.
136 00:19:12.760 ⇒ 00:19:16.460 Awaish Kumar: Okay, so, like, and how we can optimize the cost in Snowflake?
137 00:19:16.920 ⇒ 00:19:20.299 Chadd McNicholas: Ensuring you’re using the right size,
138 00:19:20.600 ⇒ 00:19:25.400 Chadd McNicholas: engines, I don’t remember the term, versus,
139 00:19:25.620 ⇒ 00:19:29.220 Chadd McNicholas: Also, making sure you have proper caching configured.
140 00:19:33.320 ⇒ 00:19:40.730 Chadd McNicholas: Yeah, because it’s compute-based, not so much memory-based. Yeah, query optimization.
141 00:19:41.420 ⇒ 00:19:42.599 Chadd McNicholas: Things like that.
142 00:19:46.000 ⇒ 00:19:46.770 Awaish Kumar: Okay.
143 00:19:46.930 ⇒ 00:19:54.699 Awaish Kumar: com… But, okay, I… I think, yeah, I’m good with the… all the technical questions.
144 00:19:54.700 ⇒ 00:20:03.799 Chadd McNicholas: I mean… Yeah, I mean, if… yeah, if my level of aptitude is not what you’re looking for, I mean, we… we can end this early. I mean, I’m…
145 00:20:05.660 ⇒ 00:20:08.740 Awaish Kumar: You know, I’m… I mean, I just want to move on to next questions.
146 00:20:08.740 ⇒ 00:20:09.400 Chadd McNicholas: Okay.
147 00:20:09.400 ⇒ 00:20:12.590 Awaish Kumar: Questions are more, like, regarding communication.
148 00:20:12.590 ⇒ 00:20:13.530 Chadd McNicholas: Okay.
149 00:20:14.230 ⇒ 00:20:18.749 Awaish Kumar: So, how would you communicate with the stakeholders if you think,
150 00:20:18.920 ⇒ 00:20:23.750 Awaish Kumar: The fine… if they don’t agree with the findings, or…
151 00:20:24.440 ⇒ 00:20:33.380 Awaish Kumar: with your findings, or the analysis you’ve done, if they don’t agree with that, how would you back your,
152 00:20:34.000 ⇒ 00:20:36.680 Awaish Kumar: Your findings, or how would you convince them?
153 00:20:38.840 ⇒ 00:20:53.520 Chadd McNicholas: So, you have to meet them where they’re at, so you have to understand why they disagree with you, and ask them to help substantiate where they’re coming from, provide… just provide some background. The last thing you want to do is tell them they’re wrong.
154 00:20:53.520 ⇒ 00:21:11.369 Chadd McNicholas: I learned… I didn’t learn that directly in consulting, but that comes very quick, is that you’re… and also, you don’t really play the customers always right, either. You have to meet them where they are. There’s a book called The Challenger Sale. I don’t know if you’ve read that, and it really talks about how to…
155 00:21:11.370 ⇒ 00:21:16.309 Chadd McNicholas: Work with, really to close a sale, but you can apply it to almost anything.
156 00:21:16.720 ⇒ 00:21:18.860 Chadd McNicholas: Excuse me. And…
157 00:21:18.920 ⇒ 00:21:37.509 Chadd McNicholas: what the three major, like, precepts are to teach, tailor, and then take control. And so, when someone is challenging you, you have to reframe and understand where they are coming from, and then provide evidence in their language so that they can see that this is going to benefit them with your solution.
158 00:21:38.350 ⇒ 00:21:47.820 Awaish Kumar: I understand getting, like, meeting them where they are, but what would you do, what steps, exactly steps you would take to… to reach to that point?
159 00:21:48.660 ⇒ 00:21:57.109 Chadd McNicholas: Yeah, as I was saying, I would, I would ask them to underst… I would ask them why
160 00:21:57.170 ⇒ 00:22:09.180 Chadd McNicholas: they… what, you know, what data they’re seeing to make them disagree with that. And then I would help them understand, where there may be something incorrect in the analysis.
161 00:22:09.750 ⇒ 00:22:13.420 Chadd McNicholas: And guide them on our approach.
162 00:22:13.670 ⇒ 00:22:15.860 Chadd McNicholas: On how we got to our numbers.
163 00:22:18.040 ⇒ 00:22:23.449 Awaish Kumar: Yeah, you can also keep in mind that not… they are not necessarily the technical guys.
164 00:22:23.450 ⇒ 00:22:24.480 Chadd McNicholas: And…
165 00:22:25.260 ⇒ 00:22:31.100 Awaish Kumar: You just have to… Simplify your things to talk, like, yeah, I understand.
166 00:22:31.100 ⇒ 00:22:41.230 Chadd McNicholas: Yeah, that’s where the tailor part of the Challenger sale is, is that you talk to the CIO versus… differently than you would with, like, the VP of Sales.
167 00:22:41.460 ⇒ 00:22:45.959 Chadd McNicholas: when you’re communicating, you have to communicate it, you know, in their language.
168 00:22:46.520 ⇒ 00:22:52.379 Awaish Kumar: Yeah, okay. Yeah, obviously, I wanted to more, like, understand if you’re…
169 00:22:52.790 ⇒ 00:22:57.850 Awaish Kumar: If you share the… the di… if you create some kind of diagrams, or any…
170 00:22:59.530 ⇒ 00:23:00.680 Chadd McNicholas: Oh, my God.
171 00:23:01.310 ⇒ 00:23:02.650 Awaish Kumar: Oh, I agree. Oh, I see.
172 00:23:02.650 ⇒ 00:23:10.630 Chadd McNicholas: depending on the level of them, and their aptitude, I generally would avoid,
173 00:23:10.630 ⇒ 00:23:26.869 Chadd McNicholas: eye charts, as I call them, where you’re just… you just flood them with data and information. I learned that the hard way back at Intel, when I was the younger analyst, and I just threw all these, stats and stuff at them, and they’re like, we don’t care, what’s the answer? So…
174 00:23:27.040 ⇒ 00:23:37.799 Chadd McNicholas: I kind of… I’d start at, you know, basically, a North Star metric and saying, hey, this is what we’re trying to achieve, this is where we’re at, and
175 00:23:38.220 ⇒ 00:23:52.450 Chadd McNicholas: And this is how we’re going to get there. Try to keep it as plain English as possible, and then if they want to dive into the details, that’s when we can do so, if they want to bring in resources, or if they want to do it themselves to, to, go through those data together.
176 00:23:55.360 ⇒ 00:24:05.109 Awaish Kumar: Okay, and, okay, yeah, apart from that, like, for example, if there is a,
177 00:24:05.370 ⇒ 00:24:07.189 Awaish Kumar: Conflict in your team.
178 00:24:07.420 ⇒ 00:24:11.379 Awaish Kumar: Regarding a technical solution, how would you handle that?
179 00:24:11.650 ⇒ 00:24:15.320 Chadd McNicholas: A conflict, so… within Brainforge…
180 00:24:15.570 ⇒ 00:24:16.899 Awaish Kumar: Within the team, yeah.
181 00:24:16.900 ⇒ 00:24:20.330 Chadd McNicholas: Okay, there, so we have,
182 00:24:20.980 ⇒ 00:24:30.090 Chadd McNicholas: Hypothetically, two proposed solutions for a customer, Sow or something, right?
183 00:24:30.350 ⇒ 00:24:34.100 Chadd McNicholas: And we need to choose between the two, or do we have, like, a conflict conflict?
184 00:24:34.100 ⇒ 00:24:37.490 Awaish Kumar: No, no, like, conflict means we gotta…
185 00:24:37.840 ⇒ 00:24:41.920 Awaish Kumar: Like, we got approved from the client, we got the project, we are just working on that.
186 00:24:41.920 ⇒ 00:24:42.380 Chadd McNicholas: Yeah.
187 00:24:42.390 ⇒ 00:24:44.700 Awaish Kumar: And maybe, like, we all are…
188 00:24:45.010 ⇒ 00:24:50.459 Awaish Kumar: like, very good engineers, right? I come up with my solution, and you come up with your solution.
189 00:24:51.560 ⇒ 00:24:55.820 Awaish Kumar: now there is a disagreement between us, right? I want…
190 00:24:56.110 ⇒ 00:25:08.790 Awaish Kumar: to implement it my way, and you have your own, like, solution for that. And then, how would you… since you are leading that, the project, how would you resolve that?
191 00:25:09.110 ⇒ 00:25:19.619 Chadd McNicholas: Yeah, okay, gotcha. So, first of all, I wouldn’t… I would do my best to ensure that never happens to begin with. Make sure that we’re kind of acting as one team, and…
192 00:25:19.620 ⇒ 00:25:37.790 Chadd McNicholas: working collaboratively. But should this scenario happen, I would apply, like, some… like a product management, project management approach, where we have two competing solutions, and now we have to look at the impact, the confidence, and the ease
193 00:25:38.520 ⇒ 00:25:47.060 Chadd McNicholas: Assign a score to each of those, and determine which one has a higher value and probability of being a better solution.
194 00:25:48.000 ⇒ 00:26:02.099 Chadd McNicholas: And that would be an objective approach and transparent approach to achieving that, and this is something we would all do together, as opposed to, they give me their cases and I just work off to the side and decide. This would be a collaborative and transparent,
195 00:26:02.100 ⇒ 00:26:09.949 Chadd McNicholas: solution, but I would take the one-team approach, too, because there’s no reason people should be Building separate…
196 00:26:09.950 ⇒ 00:26:23.079 Awaish Kumar: This is a one-team approach. From what I mean by coming up with a solution doesn’t mean that… it means that two people, different people, are working on the same thing, it means that maybe you are assigned to come up with a
197 00:26:23.430 ⇒ 00:26:30.359 Awaish Kumar: the solution for this project, and I have it… and you come up with a roadmap or a plan.
198 00:26:30.360 ⇒ 00:26:30.720 Chadd McNicholas: Yeah.
199 00:26:30.720 ⇒ 00:26:33.860 Awaish Kumar: And you have to present it to the larger team.
200 00:26:34.300 ⇒ 00:26:42.060 Chadd McNicholas: Okay, yeah, oh, competing proposals, that’s good. Okay, I thought they were… they already built them out, but yeah, no, this… this is even more relevant, then.
201 00:26:42.200 ⇒ 00:26:51.619 Awaish Kumar: Okay? What you’re proposing is not good enough, or we can do this instead of this. So obviously, if you have done your work.
202 00:26:51.790 ⇒ 00:27:05.479 Awaish Kumar: and, somebody is proposing from his experiences, there could be disagreements. Then I’m asking, for you, like, how would you resolve that? Instead of making it a conflict, how would you resolve, to become, like.
203 00:27:05.730 ⇒ 00:27:06.280 Awaish Kumar: Yeah.
204 00:27:06.280 ⇒ 00:27:10.409 Chadd McNicholas: I would apply the ICE approach, absolutely.
205 00:27:11.310 ⇒ 00:27:17.160 Chadd McNicholas: Objectively and transparently score each solution based on their impact, confidence, and ease.
206 00:27:21.060 ⇒ 00:27:25.830 Awaish Kumar: Okay, yeah, that does make sense. This is what… We shouldn’t be doing, right?
207 00:27:25.830 ⇒ 00:27:34.330 Chadd McNicholas: Yeah, yeah, that’s what I’ve done, like, with when you have, like, for example,
208 00:27:34.580 ⇒ 00:27:40.439 Chadd McNicholas: I don’t have a hard example in my head right now, but, like, for example, let’s just say marketing, wants to get,
209 00:27:40.510 ⇒ 00:27:57.819 Chadd McNicholas: a certain integration done, and sales wants to get a new security model created, and they both need them yesterday. How do you prioritize them? And then you apply that, score them, and you’re saying, okay, this is how we’re going to put on the Kanban board, and
210 00:27:57.900 ⇒ 00:27:59.930 Chadd McNicholas: And, and approach it that way.
211 00:28:01.630 ⇒ 00:28:07.120 Awaish Kumar: Okay, I think we are almost on time, we just have left 3 minutes.
212 00:28:07.390 ⇒ 00:28:11.639 Awaish Kumar: I will leave this time for you, if you want to ask any questions.
213 00:28:11.640 ⇒ 00:28:15.940 Chadd McNicholas: Yeah, yeah, I do, let’s see.
214 00:28:16.390 ⇒ 00:28:34.490 Chadd McNicholas: So, I couldn’t find too much about the company. Y’all are pretty small, clearly lean, operating, AI-forward consulting firm. What’s the, like, the size and structure of the team? Obviously, you’re a global team, but, like, how big is the…
215 00:28:34.550 ⇒ 00:28:36.440 Chadd McNicholas: For this role, yeah.
216 00:28:36.440 ⇒ 00:28:41.709 Awaish Kumar: Yeah, total… the… In total, the brain forge is, like, almost 332.
217 00:28:41.820 ⇒ 00:28:44.369 Awaish Kumar: Maybe between 30 to 40 people.
218 00:28:44.370 ⇒ 00:28:45.300 Chadd McNicholas: Okay.
219 00:28:46.140 ⇒ 00:28:53.079 Awaish Kumar: And then we have… Inside of that, we have a AI team, data team, strategy team.
220 00:28:53.570 ⇒ 00:29:02.759 Awaish Kumar: So we have, kind of, like, 10 to 15 developers, like, between… all these different…
221 00:29:04.380 ⇒ 00:29:06.550 Awaish Kumar: Categories of work… of work streams.
222 00:29:06.830 ⇒ 00:29:07.640 Chadd McNicholas: Okay.
223 00:29:08.340 ⇒ 00:29:11.769 Chadd McNicholas: Okay, cool, let’s see,
224 00:29:11.950 ⇒ 00:29:25.000 Chadd McNicholas: One thing that was never… you might not even know the answer is that, like, comp wasn’t really discussed in the job description. It was only asked in the application. And also, it says it’s not if it’s… it might be contract, or it might be FTE.
225 00:29:26.640 ⇒ 00:29:32.130 Awaish Kumar: Yeah, it depends. I think contract… it mentioned contract because it depends on where you are.
226 00:29:32.790 ⇒ 00:29:35.630 Awaish Kumar: Right? Obviously, I’m in UAE right now.
227 00:29:35.790 ⇒ 00:29:39.890 Awaish Kumar: And, Redford’s gonna hire me as a full-time employee.
228 00:29:39.890 ⇒ 00:29:40.500 Chadd McNicholas: Oh.
229 00:29:40.910 ⇒ 00:29:43.330 Awaish Kumar: in UAE, right, they will contract with me.
230 00:29:43.570 ⇒ 00:29:47.519 Awaish Kumar: Yeah, so it depends on the locations, if they bring.
231 00:29:47.520 ⇒ 00:29:48.499 Chadd McNicholas: That makes sense.
232 00:29:48.620 ⇒ 00:29:49.530 Awaish Kumar: retirement.
233 00:29:49.660 ⇒ 00:29:54.160 Awaish Kumar: consultant, and Or a contractor, and
234 00:29:54.490 ⇒ 00:30:07.230 Awaish Kumar: apart from that, for comp, like, if maybe they wanted your answer on what you are looking for, and yeah, and I think the answer to that will be in last stages.
235 00:30:07.530 ⇒ 00:30:12.489 Awaish Kumar: when you are, like, maybe at the final stages, they are going to… when they travel.
236 00:30:12.790 ⇒ 00:30:15.819 Awaish Kumar: Talking about right now, we don’t know, no.
237 00:30:16.230 ⇒ 00:30:21.730 Chadd McNicholas: Okay, who’s the… who’s the hiring manager? Who would I be reporting up to?
238 00:30:22.910 ⇒ 00:30:36.480 Awaish Kumar: It’s kind of pretty flat, hierarchy, and depends on, like, as I mentioned, I’m kind of leading a data engineering team at Brainforge, but you might be reporting to
239 00:30:37.010 ⇒ 00:30:46.140 Awaish Kumar: our CEO, directly, like, it is completely different than the normal companies wear.
240 00:30:46.470 ⇒ 00:30:49.840 Awaish Kumar: You are reporting to the same person who is also
241 00:30:50.750 ⇒ 00:30:56.540 Awaish Kumar: Kind of team lead for you, and compensation and everything is divided here.
242 00:30:56.970 ⇒ 00:31:00.449 Awaish Kumar: But at the Brain Forge, it’s like, I might be…
243 00:31:00.560 ⇒ 00:31:04.860 Awaish Kumar: There for delivery of the work.
244 00:31:05.000 ⇒ 00:31:18.819 Awaish Kumar: ensuring best practices and things like that, but the, yeah, compensation-related things are administrative things, you might be talking with, HR or directly with the UTAM CEO.
245 00:31:18.970 ⇒ 00:31:23.300 Chadd McNicholas: Okay, yeah, yeah, let’s see, I… okay, we’re right on the time,
246 00:31:23.550 ⇒ 00:31:25.280 Chadd McNicholas: Can I ask two more questions?
247 00:31:25.490 ⇒ 00:31:26.090 Awaish Kumar: Yeah, yeah, Sean.
248 00:31:26.090 ⇒ 00:31:30.299 Chadd McNicholas: So you’ve been there since June. What do you love about, working at Brainforce?
249 00:31:31.770 ⇒ 00:31:35.270 Awaish Kumar: Yeah, actually, I’ve been here since January,
250 00:31:35.440 ⇒ 00:31:37.750 Awaish Kumar: Before, I just worked as a…
251 00:31:38.430 ⇒ 00:31:47.370 Awaish Kumar: I was a contract… I’m a contractor all… from the beginning of the January till now, but kind of, I was working part-time before that.
252 00:31:47.520 ⇒ 00:32:02.270 Awaish Kumar: And then I moved, switched to become, to work full-time here. And I like is that, I have been… my entire career, I have been working at startups, or at growth stage companies, and this… this is also one of the startups where I enjoy it.
253 00:32:02.790 ⇒ 00:32:08.989 Awaish Kumar: Like, there’s a lot of flexibility, accountability, And,
254 00:32:11.770 ⇒ 00:32:17.970 Awaish Kumar: in terms of… and, like, the opportunity, right? It’s in startup, you can work,
255 00:32:18.390 ⇒ 00:32:28.200 Awaish Kumar: You can have an impact in making the decisions on… on different tech stakes, and different… getting some clients, and on the client work.
256 00:32:29.370 ⇒ 00:32:46.509 Awaish Kumar: you have a lot of flexibility also in terms of choosing the toolings, recommending the toolings and things like that, but it comes with accountability also, right? So that you are responsible for what you are proposing, and…
257 00:32:47.320 ⇒ 00:32:55.740 Awaish Kumar: Yeah, so I enjoy, like, it’s a fast-paced environment, I enjoy being there. You learn a lot in that environment, and
258 00:32:56.630 ⇒ 00:32:59.410 Awaish Kumar: And, yeah, there’s a lot of opportunities to grow it.
259 00:33:00.050 ⇒ 00:33:07.000 Chadd McNicholas: And these gigs are pretty short, right? Like, it looks like not… they’re not, like, multi-year contracts, they’re…
260 00:33:07.300 ⇒ 00:33:08.400 Chadd McNicholas: A few months.
261 00:33:09.890 ⇒ 00:33:18.670 Awaish Kumar: These are… depends on… it’s client, like, it varies from client to client, right? We have…
262 00:33:19.880 ⇒ 00:33:27.960 Awaish Kumar: Like, it can last a few months, and then it can… or at the same client can then extend your contract to become a year-long
263 00:33:28.440 ⇒ 00:33:35.490 Awaish Kumar: inclined, or… It can end there, but obviously we…
264 00:33:35.750 ⇒ 00:33:43.119 Awaish Kumar: We have a lot of clients right now which are, like, year-long, or on a continuous basis with no end dates.
265 00:33:43.600 ⇒ 00:33:58.479 Chadd McNicholas: Okay, gotcha. Okay, cool. Okay, my last question, and I’m sorry, I know it’s late there. So, just based on my background and experience in our conversation, what do you think would be my biggest challenge for this role?
266 00:34:00.760 ⇒ 00:34:10.020 Awaish Kumar: Yeah, I… I think, here, from our conversation, I think you have been working mostly
267 00:34:10.130 ⇒ 00:34:19.739 Awaish Kumar: 8 product companies, or things like that. Here, you will be working
268 00:34:20.929 ⇒ 00:34:25.479 Awaish Kumar: Simultaneously, simultaneously, maybe for 2-3 clients.
269 00:34:25.699 ⇒ 00:34:33.390 Awaish Kumar: at the same… Same time, so you have to manage your time between multiple clients, contact switch.
270 00:34:33.770 ⇒ 00:34:37.710 Awaish Kumar: And deliver the… the value.
271 00:34:38.090 ⇒ 00:34:40.819 Awaish Kumar: Also, so that’s… that could be a challenge.
272 00:34:41.000 ⇒ 00:34:47.400 Awaish Kumar: It depends on your personality, how you… how can you handle the context switching, and how can you handle…
273 00:34:47.670 ⇒ 00:34:55.230 Awaish Kumar: Like, make sure delivery of work is… is at par, while you are…
274 00:34:55.489 ⇒ 00:34:59.359 Awaish Kumar: Like, moving around different clients and delivering for all of them.
275 00:34:59.810 ⇒ 00:35:06.969 Chadd McNicholas: Okay, yeah, just fair, like, yeah, it’s kind of… it’s analogous, like, with just with my work at, like,
276 00:35:07.140 ⇒ 00:35:09.659 Chadd McNicholas: Literally over my past 3…
277 00:35:10.050 ⇒ 00:35:21.489 Chadd McNicholas: roles. Like, at Indeed, I had to support, like, the migration while also supporting, like, privacy and regulation compliance, at the same time with their own deadlines.
278 00:35:21.490 ⇒ 00:35:35.550 Chadd McNicholas: General Motors, I was normally working on, at least two projects simultaneously. And then going back to Modernize, I was supporting priorities for… I was the entire BI department for the company. The company was only 25 people, but they still had their own verticals.
279 00:35:35.550 ⇒ 00:35:42.510 Chadd McNicholas: And I had to support each of them in their priorities, in getting, you know, simultaneous work done.
280 00:35:42.650 ⇒ 00:35:53.739 Chadd McNicholas: Okay, cool. No, I appreciate, I appreciate the candid response. That’s… thank you. Okay, I know it’s late. I’ll let you go.
281 00:35:53.740 ⇒ 00:35:58.799 Awaish Kumar: Yeah, no worries, it’s been great talking to you, and thank you for your time.
282 00:35:59.080 ⇒ 00:36:01.990 Awaish Kumar: And, once I submitted my feedback.
283 00:36:02.440 ⇒ 00:36:05.550 Awaish Kumar: The recruiters will, like Kayla or…
284 00:36:05.750 ⇒ 00:36:08.729 Awaish Kumar: We’ll get back to you, like, as soon as possible.
285 00:36:09.060 ⇒ 00:36:14.280 Chadd McNicholas: Okay, thanks. Yeah, I had a great time. Yeah, have a good night. I wish.
286 00:36:15.080 ⇒ 00:36:16.050 Awaish Kumar: Yeah, we do. Bye.
287 00:36:16.050 ⇒ 00:36:16.610 Chadd McNicholas: Anyway.