Meeting Title: Brainforge Interview w- Amber Date: 2026-03-11 Meeting participants: Chukwuemeka (Anthony) Orji, Amber Lin
WEBVTT
1 00:05:21.650 ⇒ 00:05:23.230 Chukwuemeka (Anthony) Orji: Hello, good morning.
2 00:05:25.420 ⇒ 00:05:26.400 Amber Lin: Hi there!
3 00:05:26.830 ⇒ 00:05:28.210 Chukwuemeka (Anthony) Orji: Hi, can you hear me?
4 00:05:30.800 ⇒ 00:05:33.510 Chukwuemeka (Anthony) Orji: I’m trying to get my video on.
5 00:05:42.570 ⇒ 00:05:43.990 Chukwuemeka (Anthony) Orji: Hello, good morning.
6 00:05:43.990 ⇒ 00:05:47.560 Amber Lin: Hello, and good morning! Wait, what time zone are you based in?
7 00:05:47.830 ⇒ 00:05:51.720 Chukwuemeka (Anthony) Orji: Chicago, CST, Central Time Zone.
8 00:05:51.900 ⇒ 00:05:58.009 Amber Lin: Okay, gotcha, okay. It’s my morning, because I’m in PST. It’s almost your noon time.
9 00:05:58.290 ⇒ 00:06:00.969 Chukwuemeka (Anthony) Orji: Okay, okay, it’s 11 AM here.
10 00:06:01.140 ⇒ 00:06:02.250 Amber Lin: I see.
11 00:06:02.880 ⇒ 00:06:05.090 Amber Lin: Awesome.
12 00:06:05.290 ⇒ 00:06:19.580 Amber Lin: Well, this is the first interview with Brainforge, so how it’s gonna go is we’re gonna start off with a quick intro. I’m gonna have some questions for you, and I’ll make sure to leave space at the end for you to ask questions as well.
13 00:06:20.240 ⇒ 00:06:21.030 Chukwuemeka (Anthony) Orji: Okay.
14 00:06:21.260 ⇒ 00:06:25.369 Amber Lin: Yeah. Would you mind starting off with a quick intro yourself?
15 00:06:28.830 ⇒ 00:06:43.939 Chukwuemeka (Anthony) Orji: Good morning. Thank you for giving me the opportunity to interview for this position. My name is Chuku Ameka Anthony Orgy. I’m a chemical engineer, with a master’s in Business Analytics, quantitative.
16 00:06:44.050 ⇒ 00:06:48.060 Chukwuemeka (Anthony) Orji: Management and, decision making.
17 00:06:48.190 ⇒ 00:06:59.140 Chukwuemeka (Anthony) Orji: My background sits at the intersection of analytics, system architecture, and operational decision support, right? What I do is I focus on
18 00:06:59.190 ⇒ 00:07:11.939 Chukwuemeka (Anthony) Orji: Translating messy data and, messy business questions, from different business, solutions and creating a centralized source of truth.
19 00:07:11.970 ⇒ 00:07:24.450 Chukwuemeka (Anthony) Orji: where we could easily manipulate data and, create actionable insights. Over the past, years, 6 years experience of my career, I have worked
20 00:07:24.490 ⇒ 00:07:36.569 Chukwuemeka (Anthony) Orji: in highly data-analytical intensive positions, where I have worked directly with, CFOs, COOs, and, client, in client-facing
21 00:07:36.570 ⇒ 00:07:44.599 Chukwuemeka (Anthony) Orji: consulting environment. I have worked with, in cross-functional departments, from sales.
22 00:07:44.610 ⇒ 00:07:51.880 Chukwuemeka (Anthony) Orji: To operations, to, project delivery, to, finance as well.
23 00:07:51.880 ⇒ 00:08:04.620 Chukwuemeka (Anthony) Orji: What I do best is, looking through organizational business processes to understand the organizational business ecosystem, understand organizational business problem.
24 00:08:04.620 ⇒ 00:08:19.479 Chukwuemeka (Anthony) Orji: And then, use, data modeling to model those business objects so that I could understand the story that the company’s, data, infrastructure tells.
25 00:08:19.490 ⇒ 00:08:39.269 Chukwuemeka (Anthony) Orji: And then create analysis from that effective setup so that I can provide actionable insights, for… to meet organizations’ strategy objective. And this usually involves taking disparate data from different sources and business solutions.
26 00:08:39.270 ⇒ 00:08:48.879 Chukwuemeka (Anthony) Orji: like ARP, CMMS, CRM, and other, usable business solutions, and linking them together.
27 00:08:50.630 ⇒ 00:09:01.900 Amber Lin: Gotcha, okay. Thank you for that. I’ll… I’ll do a little quick intro about myself, and I have some questions I wanted to ask you. So, my name is Amber, I joined Brainbridge about a year ago.
28 00:09:02.060 ⇒ 00:09:04.669 Amber Lin: My background is mostly in
29 00:09:04.700 ⇒ 00:09:22.490 Amber Lin: consulting, and I did a little bit of project management when I joined, and right now, I’m on the strategy team, which means, do data analysis, do reporting, and we help consult clients of, hey, this is our… this is what we found, this is what we recommend you to do.
30 00:09:23.110 ⇒ 00:09:34.780 Amber Lin: And so, to kick it off, my first question, is not about, say, the work you have done, but more so why you are looking right now.
31 00:09:35.800 ⇒ 00:09:45.329 Chukwuemeka (Anthony) Orji: Okay, I am right now looking for, and actively looking for a position, because I was recently laid off in January 30th.
32 00:09:45.400 ⇒ 00:10:02.060 Chukwuemeka (Anthony) Orji: due to a company global restructuring, and members of my department and our positions were, discontinued. It wasn’t on, bad terms, or any performance-related issues.
33 00:10:02.060 ⇒ 00:10:19.519 Chukwuemeka (Anthony) Orji: As a matter of fact, I set up the entire data analytical infrastructure for the organization, and I understand perfectly well the reason for the global restructuring as somebody who is actively a part of the financial data. So,
34 00:10:19.520 ⇒ 00:10:29.030 Chukwuemeka (Anthony) Orji: I left infrastructure for the organization that would help the organization get to where they want to go to, so it was not about performance.
35 00:10:30.220 ⇒ 00:10:36.330 Amber Lin: Gotcha, I understand. It happens all the time, I just want to note that down.
36 00:10:38.860 ⇒ 00:10:45.050 Amber Lin: Gotcha. Okay, so my… my second question is…
37 00:10:46.060 ⇒ 00:10:51.870 Amber Lin: Have you… you mentioned you worked with clients before.
38 00:10:52.860 ⇒ 00:10:58.130 Amber Lin: Could you go in depth there a little bit more? I’m curious how you deal with
39 00:10:58.140 ⇒ 00:11:14.960 Amber Lin: shifting requirements from clients, because that’s… that’s essentially how consulting is different from internal work, is that the clients would one day say they want one thing, and then the other day they want something else. How have you dealt with that in the past?
40 00:11:15.490 ⇒ 00:11:23.340 Chukwuemeka (Anthony) Orji: Right, in the past, I’ve worked in consulting environments, like TMG Global. It’s a consulting IT infrastructure.
41 00:11:23.500 ⇒ 00:11:35.119 Chukwuemeka (Anthony) Orji: management service firm, as well as, Aramark CPS, government construct, facility management, contracting, role.
42 00:11:35.310 ⇒ 00:11:39.570 Chukwuemeka (Anthony) Orji: In those roles, it was more client-facing. For instance, in,
43 00:11:39.630 ⇒ 00:11:44.860 Chukwuemeka (Anthony) Orji: Aramark CPS, I was more of the internet control data analyst.
44 00:11:44.880 ⇒ 00:12:04.809 Chukwuemeka (Anthony) Orji: as well as the client-facing data analysts. We usually had, quality control checks, and I was the one that had to do the quality reporting, to the, the client-facing, stakeholders. And that involves creating, client-facing dashboards based on stakeholder.
45 00:12:04.810 ⇒ 00:12:15.500 Chukwuemeka (Anthony) Orji: requirements. And one of the approaches that I’ve always used, in, in that, in that, in these, situations is to keep an agile.
46 00:12:15.500 ⇒ 00:12:26.489 Chukwuemeka (Anthony) Orji: Mindset when approaching the situation, to understand that, for a real-time service delivery organization, there’s always gonna be changes.
47 00:12:26.500 ⇒ 00:12:37.209 Chukwuemeka (Anthony) Orji: And, the way I handle those changes is to, facilitate strong stakeholder collaboration with the clients,
48 00:12:37.210 ⇒ 00:12:47.630 Chukwuemeka (Anthony) Orji: and then clear any ambiguities in communication. I always like to approach, client engagement as an agile situation
49 00:12:47.630 ⇒ 00:12:58.160 Chukwuemeka (Anthony) Orji: Where we would create solutions in nitrative, measures based on the client decision and ever-changing, environment.
50 00:12:58.440 ⇒ 00:13:02.769 Amber Lin: Okay, do you have, like, an example of when that happened in the past?
51 00:13:02.930 ⇒ 00:13:16.519 Chukwuemeka (Anthony) Orji: Right, in the past, we’re working with CPAs at Mac, we worked with, JLL Covigo, which was the work order management tool, and, Tableau.
52 00:13:16.610 ⇒ 00:13:22.930 Chukwuemeka (Anthony) Orji: which was the visualization, phase of that.
53 00:13:22.940 ⇒ 00:13:36.270 Chukwuemeka (Anthony) Orji: And then we… I had to gather requirements from the stakeholders about the KPI that is required, and then, the visuals, the matching visuals, for the KPI.
54 00:13:36.270 ⇒ 00:13:49.479 Chukwuemeka (Anthony) Orji: And then these requirements kept changing on, you know, on a steady basis, and I realized that the reason why the requirement was changing was because the stakeholders had not clearly defined
55 00:13:49.480 ⇒ 00:13:56.889 Chukwuemeka (Anthony) Orji: what the KPI, would be with respect to the strategic, objective of the program.
56 00:13:56.890 ⇒ 00:14:12.899 Chukwuemeka (Anthony) Orji: So what I did was to create data definitions that, were mapped directly to the assumed KPIs that, you know, according to the business ecosystem, and then facilitated workshop with the clients, to
57 00:14:12.900 ⇒ 00:14:28.999 Chukwuemeka (Anthony) Orji: you know, to pinpoint these definitions and establish a list of KPIs that were well understood by the entire stakeholders, and that was how we could clear ambiguity and create the perfect dashboards according to the strategic objective.
58 00:14:30.240 ⇒ 00:14:33.509 Amber Lin: Gotcha. I really like that example, so thank you for that.
59 00:14:34.790 ⇒ 00:14:37.929 Amber Lin: So, my next question, then, is…
60 00:14:38.080 ⇒ 00:14:57.250 Amber Lin: your experience working with data teams, because I know you’ve worked in a lot of different functions. I want to hear about what the team composition was like, internally, and let’s… let’s start there. What was the team composition like? Who was on the teams that you worked on?
61 00:14:58.030 ⇒ 00:15:13.639 Chukwuemeka (Anthony) Orji: Okay, with my… in my role as a Senior Business Analyst with, Aramark CPS, I worked in a team of three. I led a business associate analyst, and we worked together as a department.
62 00:15:13.640 ⇒ 00:15:25.950 Chukwuemeka (Anthony) Orji: We, the business development director or manager assigned tasks, and priorities to me, while I did the same to the business, associate analysts. What I’ve…
63 00:15:25.950 ⇒ 00:15:33.650 Chukwuemeka (Anthony) Orji: Realized about being a leader is also being a servant to the team, where we work collectively.
64 00:15:33.650 ⇒ 00:15:46.560 Chukwuemeka (Anthony) Orji: and actively as members for the organization, strategic objective. So, my approach towards leadership is more of a servant leader approach, where I don’t just,
65 00:15:46.560 ⇒ 00:15:59.579 Chukwuemeka (Anthony) Orji: I just assign tasks to individuals, but take active parts in the execution of those tasks from the beginning, to the end. So what I did, my role involved
66 00:15:59.680 ⇒ 00:16:18.630 Chukwuemeka (Anthony) Orji: Prioritizing tasks for the business associate analyst, and as well, making sure those tasks were, met the, the timeline and deadlines, and the deliverables were 100% effective, and they met organizational strategic objective. As well as, making sure that there was,
67 00:16:18.630 ⇒ 00:16:34.090 Chukwuemeka (Anthony) Orji: Knowledge transfer, and not just one way, and two-way, that we’re creating an ecosystem where, even, the business associate analysts can innovate and bring ideas towards other teams’ strategic objectives.
68 00:16:35.460 ⇒ 00:16:47.940 Amber Lin: Gotcha, okay. Have you worked with… because I know you were working with, say, business analysts, have you worked with, say, data engineers, analytic engineers, or…
69 00:16:47.940 ⇒ 00:17:02.580 Amber Lin: say, in the modeling and ingestion side, what’s your experience there? Because I remember you mentioned you did some modeling as well. Was there a separate person that did that, or did your team have to do that? What was that like?
70 00:17:03.050 ⇒ 00:17:17.009 Chukwuemeka (Anthony) Orji: Right, at CPS, there was a modeling team, and there was a data engineering architectural team that created this, mapping, with the entire system, business object, and these business solutions we use.
71 00:17:17.010 ⇒ 00:17:29.840 Chukwuemeka (Anthony) Orji: However, it is common practice for me to always create an entity relationship diagram somewhere, where I can understand the business object on my own. So, in that role, I worked closely
72 00:17:29.840 ⇒ 00:17:39.559 Chukwuemeka (Anthony) Orji: With, the data architecture team to understand the business object mapping in the different business solutions, and how those objects maps, together.
73 00:17:39.560 ⇒ 00:17:57.739 Chukwuemeka (Anthony) Orji: involved, of a saying how the data outputs are, so that we can validate the relationship and, give feedbacks to the architecture team so that there could be corrections that could be made. I also worked with the architecture team to,
74 00:17:57.770 ⇒ 00:18:15.179 Chukwuemeka (Anthony) Orji: facilitate security and access permissions based on roles and programs that we had. But I have, I would say proficient architectural skills as well. In my role with TMG Global, I built the entire
75 00:18:15.180 ⇒ 00:18:20.700 Chukwuemeka (Anthony) Orji: analytical infrastructure from start to finish. We started with Excel.
76 00:18:20.920 ⇒ 00:18:35.670 Chukwuemeka (Anthony) Orji: you know, it was a disparate connection of business solutions, and I built the architecture from ground, from Excel down to the database, until we matured to Microsoft Fabric. So I have a great…
77 00:18:35.670 ⇒ 00:18:51.349 Chukwuemeka (Anthony) Orji: a proficient understanding of architecture, and in that position, I also did a whole lot of knowledge transfer to the business as an analyst that I work with, explaining how this architecture is and how it maps to the business objects and the business solutions.
78 00:18:51.350 ⇒ 00:19:01.459 Chukwuemeka (Anthony) Orji: Creating structures, standards, and data governance practices and documentations to be passed to the business analysts and the rest of the team.
79 00:19:01.460 ⇒ 00:19:19.670 Chukwuemeka (Anthony) Orji: And, making sure that adoption was easy by creating layers of, reporting layers, semantic models that were business-ready with dimensions and facts for the business, and then just creating the entire ecosystem, that was adaptable for the company.
80 00:19:20.530 ⇒ 00:19:29.569 Amber Lin: Gotcha. The first company you mentioned, the CPS, what’s… which one is? I’m trying to look on your linear to figure out which one it was.
81 00:19:29.570 ⇒ 00:19:31.529 Chukwuemeka (Anthony) Orji: That is our MAC facilities at CC.
82 00:19:31.530 ⇒ 00:19:33.490 Amber Lin: Oh, okay, okay, gosh.
83 00:19:33.490 ⇒ 00:19:34.040 Chukwuemeka (Anthony) Orji: Bye.
84 00:19:34.420 ⇒ 00:19:36.350 Amber Lin: So, I’ll save that.
85 00:19:36.770 ⇒ 00:19:41.599 Amber Lin: Awesome. So, that’s really helpful to know, because in how we work
86 00:19:41.660 ⇒ 00:19:47.510 Amber Lin: And feel free to ask questions later. So we have our…
87 00:19:47.510 ⇒ 00:20:05.620 Amber Lin: data teams, we have our strategy teams, and then we have our AI team. So, in… in our case, or in your case, you would be working on the strategy team, but then any modeling tasks or ingestion tasks, we have a data team to support that, so that we can focus on the things that we’re the best at. So.
88 00:20:05.620 ⇒ 00:20:14.459 Amber Lin: I think having that knowledge and experience to do the modeling and understand what it looks like in a database would be really, really helpful.
89 00:20:14.460 ⇒ 00:20:25.990 Amber Lin: But I think we will be able to get help from the modeling team if there’s something that’s very technical and, very early in the pipeline.
90 00:20:26.370 ⇒ 00:20:46.039 Chukwuemeka (Anthony) Orji: I do understand that, but, you know, it’s always been a practice of my own to always create the model on my own, even if there’s already a model. Somehow I always create the model. It is always… it’s like a flag to me, for me, like a validation flag.
91 00:20:46.040 ⇒ 00:20:55.119 Chukwuemeka (Anthony) Orji: So I can easily spot, when there is an error coming from the ingestion pipeline, where the error is coming from.
92 00:20:55.120 ⇒ 00:21:14.300 Chukwuemeka (Anthony) Orji: which of the KPIs or analysis is wrong, and I can easily trace it down. So, to me, it’s more like a validation phase, so that I can make sure that the output, you know, it’s always right to the input. And I believe if a whole lot of operational analysts takes this approach.
93 00:21:14.300 ⇒ 00:21:29.769 Chukwuemeka (Anthony) Orji: It would be easy to understand the business that they are modeling before they query any data, because understanding the story of how these objects map will give you a clear understanding of what you want to solve, you know, using the data.
94 00:21:31.090 ⇒ 00:21:36.909 Amber Lin: Gotcha. I like that approach. Awesome. So…
95 00:21:37.450 ⇒ 00:21:42.970 Amber Lin: My last question before I want to open up for you to ask me questions is.
96 00:21:43.710 ⇒ 00:22:01.729 Amber Lin: What… like, what motivates you working? Like, what do you want to do? Because I… I know, personally, for me, I started out my career thinking I wanted to do something, and then I moved around, and then, like, right now, I don’t really know what I want to keep doing in the future, but
97 00:22:01.740 ⇒ 00:22:07.429 Amber Lin: As someone that’s deeper in your career, would you… what do you think
98 00:22:07.630 ⇒ 00:22:15.470 Amber Lin: You want to get out of your next job, or want to get out in the future, like, is there a path that you want to go towards?
99 00:22:16.590 ⇒ 00:22:32.130 Chukwuemeka (Anthony) Orji: Right, and what I’m doing right now is exactly how I’ve mapped my career to be from the very beginning. I started as a chemical engineer with a concentration in process instrumentation and control.
100 00:22:32.180 ⇒ 00:22:48.369 Chukwuemeka (Anthony) Orji: And I moved towards analytics. And it was intentional, because I didn’t want to be limited to just the industrial phase of my career. I wanted to move into the business. And I had this mindset that an understanding of process instrumentation and control
101 00:22:48.370 ⇒ 00:22:54.149 Chukwuemeka (Anthony) Orji: Would easily transfer into the business world, because processes is about flows, mass balance.
102 00:22:54.150 ⇒ 00:22:59.779 Chukwuemeka (Anthony) Orji: and the entirety of parameter and metrics monitoring. And that’s the same way
103 00:23:00.080 ⇒ 00:23:13.560 Chukwuemeka (Anthony) Orji: business I modeled, the financial planning and analysis, the operational model, and every other way, and that’s exactly what I’m doing. What I want to do is the same way, having worked in the industrial field.
104 00:23:13.660 ⇒ 00:23:27.630 Chukwuemeka (Anthony) Orji: and being, like, the data heart of the field, seeing exactly how data moves through all functional area, and being able to create insights from that data is what I’m actually doing.
105 00:23:27.650 ⇒ 00:23:42.879 Chukwuemeka (Anthony) Orji: in the next 5 years, I see myself being more of a financial operations manager, being in this field that I’m at right now, but maybe I would have, had more experience because of more,
106 00:23:42.880 ⇒ 00:23:55.220 Chukwuemeka (Anthony) Orji: scenarios that I would have gone on, business problems that I would have solved, and the ability to lead people, you know, and create an environment that can easily solve any such, business problem.
107 00:23:57.480 ⇒ 00:24:05.239 Amber Lin: Gotcha, note it down. And, like, follow-up question of, say, at… at your work.
108 00:24:05.430 ⇒ 00:24:10.070 Amber Lin: Or at a new company. What would motivate you the best?
109 00:24:12.000 ⇒ 00:24:18.419 Chukwuemeka (Anthony) Orji: At a new company, what motivates me the best is when a company has clear objectives.
110 00:24:19.180 ⇒ 00:24:22.640 Chukwuemeka (Anthony) Orji: And clear ownership of… .
111 00:24:22.640 ⇒ 00:24:24.820 Amber Lin: Of roles and tasks.
112 00:24:24.990 ⇒ 00:24:31.990 Chukwuemeka (Anthony) Orji: Clear objectives at first helps you to understand the goals of the organization.
113 00:24:32.200 ⇒ 00:24:47.660 Chukwuemeka (Anthony) Orji: And it clears ambiguity of what the organization wants to go, and it helps you focus on what the target is. And then the proper assignment of those helps you understand who the best people
114 00:24:47.820 ⇒ 00:25:07.720 Chukwuemeka (Anthony) Orji: to collaborate with for different tasks, so you can understand who the right stakeholders are that you could, collaborate with. When those, structures are not in place, you find that processes and governance becomes very difficult.
115 00:25:07.820 ⇒ 00:25:22.380 Chukwuemeka (Anthony) Orji: Another thing is a company that encourages a structure of change management and innovation and idea. When there’s no change management, it is very difficult to foster innovation and idea.
116 00:25:22.380 ⇒ 00:25:31.639 Chukwuemeka (Anthony) Orji: And people sometimes do not bring out their opinions because it just comes to an end. In a company where my ideas and innovations
117 00:25:31.940 ⇒ 00:25:43.529 Chukwuemeka (Anthony) Orji: you know, not just, you know, taken down, and there’s change management for adoption of new practices and policies. I think, those are the environments that I do my best work.
118 00:25:46.300 ⇒ 00:25:54.709 Amber Lin: Gotcha. I heard that, and I think our company would be able to support that, but I won’t leave the room for you to ask questions so that I can tell you more about our company.
119 00:25:55.280 ⇒ 00:25:59.880 Chukwuemeka (Anthony) Orji: Okay, I do have some questions put down in the notes here.
120 00:26:00.070 ⇒ 00:26:01.290 Amber Lin: Okay, so…
121 00:26:05.490 ⇒ 00:26:07.110 Chukwuemeka (Anthony) Orji: Alright, so,
122 00:26:08.540 ⇒ 00:26:16.950 Chukwuemeka (Anthony) Orji: how… what level of AI, what is the intensity of AI does bring forth using their work?
123 00:26:17.250 ⇒ 00:26:35.319 Chukwuemeka (Anthony) Orji: Because I, you know, there’s this limitation of how much you can use AI in companies, and I do understand the ethic involved in AI and data communication and sensitivity of data, but I also like to hear that from your perspective.
124 00:26:36.200 ⇒ 00:26:52.640 Amber Lin: I see. I would say this is the biggest perk, we get from working at our company. I do think we’re at the top 5%, 1% of companies with our AI adoption and our AI development. So, to explain it further.
125 00:26:52.770 ⇒ 00:26:56.130 Amber Lin: our… I think our attitude with AI is…
126 00:26:56.460 ⇒ 00:27:01.250 Amber Lin: we’re gonna use it as much as possible. In…
127 00:27:01.280 ⇒ 00:27:11.439 Amber Lin: And we want to use it in an organizational fashion, because in a lot of companies, you see people, one person uses ChatGPT, the other person
128 00:27:11.440 ⇒ 00:27:21.699 Amber Lin: doesn’t use AI. Or you have another person that’s, maybe uses Cursor and then uses Claude somewhere, but it’s a disjointed process.
129 00:27:21.860 ⇒ 00:27:31.539 Amber Lin: What we have done, actually, is we’ve, first of all, ensured that we have good context for the entire organization.
130 00:27:31.540 ⇒ 00:27:37.010 Amber Lin: That means we’re up-to-date on the most recent developments, we’re connected with all of our systems.
131 00:27:37.010 ⇒ 00:27:53.589 Amber Lin: our meetings are transcribed, our Slack messages are integrated, our tickets are integrated, our meeting notes with clients are integrated, and also our code base, our past PRs, all of these are integrated in one place so that
132 00:27:53.690 ⇒ 00:28:02.039 Amber Lin: One, on managing processes. Our project management role has been slowly automated away,
133 00:28:02.160 ⇒ 00:28:12.909 Amber Lin: Because we’re able to generate tickets, we’re able to audit the state of the tickets, and we’re able to say, hey, update the time allocation for this week.
134 00:28:13.170 ⇒ 00:28:16.330 Amber Lin: So that’s on the operational side, and…
135 00:28:16.680 ⇒ 00:28:19.399 Amber Lin: on my side of how I use
136 00:28:19.550 ⇒ 00:28:22.789 Amber Lin: This for my development is…
137 00:28:23.270 ⇒ 00:28:30.710 Amber Lin: I’m… I haven’t… like, I would say 70-80% of my…
138 00:28:30.750 ⇒ 00:28:50.590 Amber Lin: Analysis involves AI. There’s some that I have to do directly in the codebase and do… look at the modeling that I have to do on my own to make sure I can validate data, but when it comes to queries, I work very closely directly in Cursor. I’m integrated with our database.
139 00:28:50.900 ⇒ 00:29:01.800 Amber Lin: in cursor, I’m able to run queries in cursor, structure my notebooks, output analysis and visualizations in there.
140 00:29:01.920 ⇒ 00:29:17.450 Amber Lin: I think another one of my teammates, they were recently able to generate dashboards after they’ve defined, like, the schemas, mapped the fields, work with stakeholders. They were directly able to generate a pretty okay-looking dashboard
141 00:29:17.710 ⇒ 00:29:37.669 Amber Lin: with AI, and then it just saved them, I would say the 20 hours a junior analysts would take to do the basic things, and immediately, someone like you was able to review it and say, hey, I want this little tweak here, I want this design there. So I think if that’s something you care about, you will be very excited.
142 00:29:37.670 ⇒ 00:29:38.900 Chukwuemeka (Anthony) Orji: Go ahead. Right.
143 00:29:38.900 ⇒ 00:29:40.360 Amber Lin: We’re developing.
144 00:29:40.640 ⇒ 00:29:59.690 Chukwuemeka (Anthony) Orji: Right, yeah, I think that’s great. I also use AI. I’ve been trying to use MCP with connections with Power BI of late, trying to see how that works. I rebranded my portfolio of recent, showing case studies, and I use, AI.
145 00:29:59.690 ⇒ 00:30:07.190 Chukwuemeka (Anthony) Orji: And, you know, it was, awesome what AI could do, and I do appreciate it. I use it to…
146 00:30:07.190 ⇒ 00:30:20.159 Chukwuemeka (Anthony) Orji: optimize my work, but the entire of the structure, the background, and the logic is all mine, and I always have to validate what AI brings out, because most of the time… not most of the time, some of the time.
147 00:30:20.160 ⇒ 00:30:24.210 Chukwuemeka (Anthony) Orji: it’s not always correct, so I appreciate that response.
148 00:30:24.210 ⇒ 00:30:25.090 Amber Lin: Of course.
149 00:30:25.320 ⇒ 00:30:31.700 Chukwuemeka (Anthony) Orji: I do have some other questions, like, the next question would be, what…
150 00:30:32.110 ⇒ 00:30:36.310 Chukwuemeka (Anthony) Orji: KPIs Buddhists will own or manage?
151 00:30:37.810 ⇒ 00:30:39.800 Amber Lin: Let me write that down.
152 00:30:43.220 ⇒ 00:30:44.979 Amber Lin: So, I think…
153 00:30:45.190 ⇒ 00:30:58.480 Amber Lin: I’m… I wouldn’t be able to answer this perfectly, because I’m not the person you would report to. But, based on my understanding of this… this role, you would be a…
154 00:30:58.880 ⇒ 00:31:00.729 Amber Lin: Senior Data Analyst.
155 00:31:00.770 ⇒ 00:31:20.459 Amber Lin: That you would oversee parts of our, one, for sure, oversee some client projects. You would own the relationships with the clients, and one of your KPIs would be, does this client renew? Are they happy? Do we expand our contracts with them?
156 00:31:20.700 ⇒ 00:31:25.390 Amber Lin: Right, so that’s overseeing the work on clients. The other part is…
157 00:31:25.960 ⇒ 00:31:37.990 Amber Lin: overseeing some teammates in the strategy team that will report to you. You would help set, hey, this is the direction I want this project to go,
158 00:31:38.170 ⇒ 00:31:48.949 Amber Lin: Here are some tasks you should do, and they will have work that you need to review, because you have, the experience that they do not have, and we need that.
159 00:31:48.950 ⇒ 00:32:03.779 Amber Lin: Before we send the work to clients. So that’s what I foresee for now. I think when… if we were to talk again later in the interview, I think one of our CEOs would be the best one to answer that question for you.
160 00:32:04.430 ⇒ 00:32:13.859 Chukwuemeka (Anthony) Orji: Okay, the next question I wanted to ask is, what would success look like in the first 90 days? I don’t know if you can answer that as well.
161 00:32:14.600 ⇒ 00:32:18.489 Amber Lin: I can give, like, a quick answer. Okay.
162 00:32:18.600 ⇒ 00:32:31.939 Amber Lin: So, I think the first 90 days, we usually immediately staff people on client projects. So, the first 90 days, I guess the first 30 days even, would be delivering some parts of successful work.
163 00:32:32.150 ⇒ 00:32:38.680 Amber Lin: Making sure you have established your relationship with the clients and with the teammates.
164 00:32:39.040 ⇒ 00:32:48.860 Amber Lin: to facilitate focus down the road. I think also the first 90… 30, 60 days also look like, increased level AI adoption.
165 00:32:49.000 ⇒ 00:32:52.930 Amber Lin: And I would say maybe in 90 days.
166 00:32:53.560 ⇒ 00:33:01.300 Amber Lin: It will look like, successes on certain projects, great client feedback, and also.
167 00:33:01.430 ⇒ 00:33:11.850 Amber Lin: I think that depends on persons of creating playbooks, brainstorming automation, suggesting changes to our organization. That’s the…
168 00:33:12.140 ⇒ 00:33:21.360 Amber Lin: That’s the success criteria I’ve seen that’s the same across people, but we do develop individual ones for each person.
169 00:33:21.900 ⇒ 00:33:40.550 Chukwuemeka (Anthony) Orji: And that takes me to the next question. I think we’ve touched this a little, but I just wanted to clarify what the current data analytical infrastructure looks like, and what the tools would be that I may be working with for this role.
170 00:33:41.950 ⇒ 00:33:49.550 Amber Lin: Gotcha. Because we all work on client projects, it really depends on what the client has been using.
171 00:33:49.550 ⇒ 00:33:52.459 Chukwuemeka (Anthony) Orji: And then what we’re able to suggest.
172 00:33:52.460 ⇒ 00:33:53.330 Amber Lin: So…
173 00:33:53.520 ⇒ 00:34:07.520 Amber Lin: In terms of the database, some people use BigQuery, some people use Snowflake, it’s really a range of things that they like to do. And in terms of the ingestion, modeling.
174 00:34:07.590 ⇒ 00:34:26.390 Amber Lin: I think tools we often use is dbt, and then we use Polytomic for some of the connectors, and so… and then, once it gets to the reporting layer, then it also depends on clients. Some people use Power BI, which is okay. Some people use Tableau, and recently.
175 00:34:26.699 ⇒ 00:34:44.099 Amber Lin: we’ve been trying to transition people over to one of our partners who’s very… a very AI-enabled reporting platform that lets the clients self-serve. So, it’s called Omni, and we’re… we’re moving a few clients there.
176 00:34:44.340 ⇒ 00:34:55.139 Amber Lin: Because they’re all very interested in, hey, I can have my employees ask the AI about our data. So, that’s the… on the reporting side, and…
177 00:34:55.580 ⇒ 00:35:01.819 Amber Lin: I would say how we do reporting, there will be a lot more AI involved.
178 00:35:01.940 ⇒ 00:35:07.780 Amber Lin: Than the average, say, data or consulting company.
179 00:35:08.360 ⇒ 00:35:20.409 Chukwuemeka (Anthony) Orji: Now, I think now it gives me a picture of, why the need to be data-savvy, not just from the tool perspective, but from data,
180 00:35:20.450 ⇒ 00:35:30.900 Chukwuemeka (Anthony) Orji: perspective. I think this role will emphasize a whole lot of… on data structure, data standardization, data governance, and
181 00:35:31.850 ⇒ 00:35:34.320 Chukwuemeka (Anthony) Orji: More about,
182 00:35:35.020 ⇒ 00:35:44.699 Chukwuemeka (Anthony) Orji: reporting all through business and cross-functional areas, I believe, and how, data can connect from different, disparate,
183 00:35:44.800 ⇒ 00:35:58.720 Chukwuemeka (Anthony) Orji: sources. So I think that answer, Clais, you know, gives me a vision of exactly what is expected from the rule. And I think the last question I wanted to ask is about company culture.
184 00:35:58.720 ⇒ 00:36:05.970 Chukwuemeka (Anthony) Orji: I don’t know if this is the right time to ask those questions, or maybe if I’m moving to the next, stage.
185 00:36:06.110 ⇒ 00:36:13.750 Chukwuemeka (Anthony) Orji: I would like to ask what the company culture at Brainforge is. I know Brainforge is a startup,
186 00:36:13.910 ⇒ 00:36:27.859 Chukwuemeka (Anthony) Orji: you know, I don’t want to say… would I say it’s a startup, but from my research, I think I see about 50 employees. The workforce is about 50 employees total. Correct me if I’m wrong.
187 00:36:29.300 ⇒ 00:36:39.620 Amber Lin: Yeah, I’ll give a quick question, because we’re about… we’re at time. I will say the culture is the reason that I stay.
188 00:36:40.270 ⇒ 00:36:46.770 Amber Lin: We are a small company. We are a startup, so we move fast, but
189 00:36:47.140 ⇒ 00:37:02.290 Amber Lin: I think all the people that we’ve selected are, like, I would call them good people or kind people. I think, ultimately, who we work with for half of our day is very important, and…
190 00:37:02.740 ⇒ 00:37:15.570 Amber Lin: I think we’re… people are very collaborative. If I ask for help, nobody really declines. If there’s something that we need on a project, people usually don’t
191 00:37:15.690 ⇒ 00:37:28.760 Amber Lin: push off responsibilities or divert things just to say, hey, this is not my job. I think everybody’s very willing to help, and they’re all very interesting people, so…
192 00:37:29.550 ⇒ 00:37:42.410 Amber Lin: I think the… if we were to proceed to the next round, I think that would be a great question to ask everybody, just to see what their experiences are, because this is just my personal experience at the company.
193 00:37:43.190 ⇒ 00:37:57.410 Chukwuemeka (Anthony) Orji: Okay, I’m very comfortable with startups. CMG was also a startup, and I’m very comfortable with, getting on the job and, you know, getting hands-on within the first one week, because I’m more of a builder.
194 00:37:57.500 ⇒ 00:38:05.560 Chukwuemeka (Anthony) Orji: Than, just a BI reporting specialist. My role has always been to create infrastructures.
195 00:38:05.700 ⇒ 00:38:14.749 Chukwuemeka (Anthony) Orji: But I’m as well very adaptable, and I can still, adapt to any, infrastructure that has been created and optimized it.
196 00:38:15.900 ⇒ 00:38:26.849 Amber Lin: Awesome. Alright, thank you so much for your time today. Our operations team will reach out for sure in the next one or two weeks, no matter what the decision will be.
197 00:38:27.080 ⇒ 00:38:33.659 Chukwuemeka (Anthony) Orji: Alright, thank you very much, Amber. It was great talking to you, and I really appreciate the time to interview for this role.
198 00:38:34.350 ⇒ 00:38:36.649 Amber Lin: I appreciate your time as well. Alright, have a good one.
199 00:38:36.650 ⇒ 00:38:38.380 Chukwuemeka (Anthony) Orji: Thank you, and you too. Bye-bye.