Meeting Title: Brainforge Interview w- Amber Date: 2026-03-06 Meeting participants: Garrett, Amber Lin
WEBVTT
1 00:03:33.110 ⇒ 00:03:35.820 Amber Lin: Hi there. Hi there! Nice to meet you.
2 00:03:35.820 ⇒ 00:03:39.510 Garrett: Yeah, nice to meet you as well. Thanks for taking the time today, how you doing?
3 00:03:39.510 ⇒ 00:03:50.029 Amber Lin: Of course, I’m doing well. I saw your Loom video, and so I got to know a little bit about you. So…
4 00:03:50.030 ⇒ 00:04:03.880 Amber Lin: this… I think for this initial interview, we’re gonna do a little bit of quick introductions. Sure. And then, I have some questions for you, and then we’ll leave space for you to also ask me questions about our company.
5 00:04:04.090 ⇒ 00:04:05.760 Garrett: Sure, sounds great.
6 00:04:06.640 ⇒ 00:04:09.659 Amber Lin: Awesome. You wanna go ahead and give a quick intro?
7 00:04:09.790 ⇒ 00:04:24.350 Garrett: Yeah, absolutely. So yeah, thanks so much, again, you know, for taking the time. So, essentially, I’ve been, like, a technical, program manager, around 10 years now, I would say.
8 00:04:24.380 ⇒ 00:04:30.060 Garrett: Most recently, I just, finished a contract role at Disney.
9 00:04:30.460 ⇒ 00:04:36.030 Garrett: And there, I was working, on a data lake, initiative.
10 00:04:36.460 ⇒ 00:04:45.250 Garrett: Involving, a lot of different, internal teams, primarily under, ad sales, organization.
11 00:04:45.760 ⇒ 00:05:04.529 Garrett: And, essentially we were delivering a new data warehouse that lived within Databricks, and then connecting that to different upstream and downstream systems, so that was kind of the goal, of the project.
12 00:05:04.530 ⇒ 00:05:10.939 Garrett: Prior to that, I also worked at other companies. I worked at Apple, where I provided
13 00:05:11.280 ⇒ 00:05:18.339 Garrett: Kind of custom executive dashboards, utilizing Jira data and building those in Tableau.
14 00:05:18.590 ⇒ 00:05:28.799 Garrett: And then, you know, rapidly prototyping, views, building, SQL queries using, like, cloud and different AI tools.
15 00:05:30.900 ⇒ 00:05:46.389 Garrett: And then, when I was at, Meta, before Apple, I was kind of more on the business operations side, and, they’re, supporting one senior director and one VP out of, Reality Labs.
16 00:05:46.550 ⇒ 00:05:51.510 Garrett: So think, like, you know, smart glasses and VR, you know, kind of product lines.
17 00:05:51.700 ⇒ 00:06:06.379 Garrett: And then just maintaining a lot of the project-level data, like milestone timelines and, financials, like OPEX, CAPEX, and headcount distribution across projects, out of Google Sheets.
18 00:06:06.490 ⇒ 00:06:19.959 Garrett: And then migrating that data to Smartsheet, like, another tool, while producing, like, monthly and quarterly reporting and things like that. So I’ve done a lot of, kind of, the operations side.
19 00:06:20.110 ⇒ 00:06:22.529 Garrett: And reporting and analytics.
20 00:06:22.940 ⇒ 00:06:33.850 Garrett: And then prior to that, a lot of my career was at, DirecTV, almost 8 years, where I kind of saw the transformation from the satellite business to the DirecTV stream site.
21 00:06:34.050 ⇒ 00:06:51.470 Garrett: And with that, being a part of the PMO, standing up, Jira and Jira line for the whole enterprise, you know, all the different Scrum teams and agile release trains, and, you know, instituting, like, PI, planning processes and…
22 00:06:51.610 ⇒ 00:07:02.129 Garrett: you know, executive dashboards, reporting, things like that. And then my role there kind of transitioned to more data analytics as well, so building dashboards in Tableau, and then…
23 00:07:02.270 ⇒ 00:07:11.660 Garrett: Working cross-functionally with different organizations, you know, on product and streaming operations, finance, things like that. So yeah, that’s kind of the intro.
24 00:07:11.840 ⇒ 00:07:12.370 Garrett: For me.
25 00:07:12.370 ⇒ 00:07:19.610 Amber Lin: Gotcha, okay. Thank you for that. Quick something about me. I’ve been at Brainforge for about a year now.
26 00:07:19.610 ⇒ 00:07:22.610 Garrett: I mostly worked in the…
27 00:07:22.610 ⇒ 00:07:37.700 Amber Lin: consulting and analytics space before I joined Brainforge, and when I joined, I also started, to stand up our PMO, so working on the project management side. Our current project management system has changed a little bit, and
28 00:07:37.700 ⇒ 00:07:55.419 Amber Lin: I’ll dive into that deeper, when you ask questions about us, but currently, I’m working more on the strategy analytics team, which is… which is why, we’re talking right now, because I believe you apply for the digital product analyst role, right?
29 00:07:55.420 ⇒ 00:08:10.879 Garrett: Yeah, absolutely, yeah. And I, I was kind of, like, had a question about that, too, like, I wasn’t really sure what, like, role to choose. Like, I do have, like, more of a TPM background, but I do have that analyst, you know, kind of background as well, you know?
30 00:08:10.930 ⇒ 00:08:16.850 Garrett: Working a lot with data and, you know, SQL and, you know, building dashboards and things like that.
31 00:08:17.070 ⇒ 00:08:17.420 Amber Lin: I see.
32 00:08:18.310 ⇒ 00:08:22.620 Amber Lin: I think there’s a… there’s a lot of flexibility within the company.
33 00:08:22.620 ⇒ 00:08:23.130 Garrett: Oh, great, Chris.
34 00:08:23.130 ⇒ 00:08:37.470 Amber Lin: because we provide services in data, strategy, and AI. They’re our main branches. So, I was also curious about that. I was looking at your LinkedIn, looking at your video. Sure. What is the area that you want to work in?
35 00:08:38.549 ⇒ 00:08:45.259 Amber Lin: Let me know about that, or what interests you, or what you’ve had experience in.
36 00:08:45.670 ⇒ 00:08:54.830 Garrett: Absolutely. Okay. Yeah, I think, I guess, like, what kind of gets me most excited is, like, just building kind of insights, you know, like,
37 00:08:54.980 ⇒ 00:09:03.200 Garrett: like, building KPIs, you know, working to kind of, like, define requirements, that kind of end-to-end process of…
38 00:09:03.510 ⇒ 00:09:08.520 Garrett: Like, building, an architecture, you know, standing up data warehouses.
39 00:09:08.850 ⇒ 00:09:13.859 Garrett: Gathering data that would be transformed, right, into insights.
40 00:09:14.420 ⇒ 00:09:24.470 Garrett: So, you know, I’ve gotten involved into, like, some of the engineering aspects of that. Like, you know, whether it be, like, building SQL scripts, you know, or…
41 00:09:24.770 ⇒ 00:09:30.059 Garrett: Even some Python as well, like, in terms of ETL, like, moving data, you know, from
42 00:09:30.180 ⇒ 00:09:32.080 Garrett: Various systems and things.
43 00:09:33.570 ⇒ 00:09:42.059 Garrett: So I think that there’s kind of some flexibility into where I could kind of get involved, or, you know, building dashboards, you know, and things like that as well, so…
44 00:09:42.420 ⇒ 00:09:43.989 Garrett: Cool, sounds good.
45 00:09:43.990 ⇒ 00:09:48.090 Amber Lin: So, I guess my follow-up question is.
46 00:09:48.090 ⇒ 00:09:52.370 Garrett: How much of your experience here is more on the…
47 00:09:52.370 ⇒ 00:10:09.420 Amber Lin: management, portfolio management, overseeing project side, or how much of it is, you doing the work, or reviewing other people’s work? So I just want to see, like, where you land, so I can ask the team, hey, what is a better fit?
48 00:10:09.720 ⇒ 00:10:17.630 Garrett: Absolutely. Yeah, I would say, like, maybe a 50-50, or, like, you know, something kind of in that line.
49 00:10:17.980 ⇒ 00:10:32.120 Garrett: Because I’ve had… I’ve had roles where it’s more like, you know, I mentioned kind of like at Meta, where it’s more like business operations, where I kind of owned the reporting and, you know, I delivered… that was just more kind of like Google Sheets and Google Slides, you know, kind of presentations and things like that.
50 00:10:32.120 ⇒ 00:10:38.829 Garrett: But I’ve also kind of, like, built, you know, executive dashboards, like, in Tableau, like, end-to-end.
51 00:10:39.030 ⇒ 00:10:58.339 Garrett: So, like, you know, standing up data, in PostgreSQL, you know, for example, and then, like, tapping into the Jira API, right, and then gathering, key, like, objects, right, and turning them into, like, a schema, right, where we can kind of join tables together, and then…
52 00:10:58.480 ⇒ 00:11:04.240 Garrett: Extract, you know, key insights, kind of based on requirements and things like that.
53 00:11:04.940 ⇒ 00:11:09.760 Garrett: So… Cool, okay.
54 00:11:09.760 ⇒ 00:11:12.079 Amber Lin: I see, so I, I think…
55 00:11:12.190 ⇒ 00:11:15.419 Amber Lin: Reflecting on what we have in the company.
56 00:11:15.420 ⇒ 00:11:16.180 Garrett: Yeah, exactly.
57 00:11:16.180 ⇒ 00:11:20.680 Amber Lin: we have a lot of… we… I think it seems like you would fit in both…
58 00:11:20.750 ⇒ 00:11:27.829 Amber Lin: kind of the intercession for data to strategy, which I think is great, because usually our projects start up
59 00:11:27.890 ⇒ 00:11:46.139 Amber Lin: start out more in the data side, and once we have that established, we have connected the things, then we try to find opportunities to expand into more strategy work, where it is… it starts off with, okay, let’s build them reporting, let’s see if we can find insights, let’s see if we can
60 00:11:46.140 ⇒ 00:11:52.539 Amber Lin: do that, expansion and make the clients feel like, okay, we can provide more value.
61 00:11:52.570 ⇒ 00:11:53.200 Amber Lin: So…
62 00:11:53.200 ⇒ 00:11:54.040 Garrett: Exactly.
63 00:11:54.040 ⇒ 00:11:57.000 Amber Lin: Yeah, and so my follow-up question there is.
64 00:11:57.050 ⇒ 00:12:15.790 Amber Lin: How much external communication or communicating with stakeholders have you done? Because I… looking at your experience, it seems like a lot of, internal projects, like, have you worked with clients directly, or how has it been, on that aspect?
65 00:12:16.030 ⇒ 00:12:25.639 Garrett: Absolutely. I guess, yeah, I guess kind of more internally, I guess you could say, like, enterprise level, so I guess to give me an example was when I was at,
66 00:12:26.180 ⇒ 00:12:31.749 Garrett: DirecTV, I worked closely with the product management team once DirecTV Stream had launched.
67 00:12:32.010 ⇒ 00:12:48.319 Garrett: And they had, you know, very important kind of business-level KPIs they were keeping track of, you know, like subscriber growth and ensuring that the development of new features was tying to the business KPIs, right, of the company, and just improving
68 00:12:48.460 ⇒ 00:12:51.220 Garrett: You know, reducing churn, you know, things like that as well.
69 00:12:52.030 ⇒ 00:12:58.479 Garrett: And so, a lot of the insights, you know, that we produced as, like, a data analytics team.
70 00:12:59.070 ⇒ 00:13:14.069 Garrett: Essentially, so I’ll give you an example. One of the feature sets that was added on the DirecTV Stream app was called, like, a sports experience package. So think, you know, the ability to, like, be able to add your favorite teams, you know, within the app, or see…
71 00:13:14.070 ⇒ 00:13:21.220 Garrett: live stats and things like that. So what we did is we, tracked that data through New Relic.
72 00:13:21.220 ⇒ 00:13:24.799 Garrett: You know, which is kind of like a observability tool.
73 00:13:25.400 ⇒ 00:13:31.000 Garrett: And then we, in the back end, we warehouse that data in Snowflake.
74 00:13:31.730 ⇒ 00:13:38.990 Garrett: So then we were able to kind of track, clicks and things like that, right, within the app, across the different clients.
75 00:13:39.220 ⇒ 00:13:44.379 Garrett: To see, the level of usage, right, of those new features.
76 00:13:44.540 ⇒ 00:13:49.669 Garrett: Because we wanted to correlate that to the development effort, right, of the teams.
77 00:13:50.140 ⇒ 00:13:58.250 Garrett: So that’s just kind of an example of some KPIs that, you know, were requirements from the product management team.
78 00:13:58.490 ⇒ 00:14:05.499 Garrett: And that, you know, it’s important to have that partnership to kind of help drive, you know, the business forward after the launch, so…
79 00:14:05.950 ⇒ 00:14:12.899 Amber Lin: Gotcha, okay. It made me also curious about, okay, so you say you worked on a lot of.
80 00:14:13.490 ⇒ 00:14:19.619 Amber Lin: internal enterprise clients, what was the timeline like of your projects? Like, usually how long does it take?
81 00:14:20.290 ⇒ 00:14:38.649 Garrett: Yeah, so that was, like, that project was before, like, kind of earlier days of, like, Tableau and, like, pre-AI and all that, so, like, like, the complete tracking of, like, the sports experience data, and, we were also doing it kind of in parallel with the development team, right, as they were releasing features to kind of…
82 00:14:38.750 ⇒ 00:14:54.640 Garrett: Ensure we were getting them, you know, quick enough feedback, right, as new features were launched, but overall, like, just for that project, it was probably, like, 6 months of, you know, iterative, like, dashboard building and, gathering, you know, new data attributes and things like that.
83 00:14:55.120 ⇒ 00:15:00.389 Amber Lin: What about your other projects? Is it also around, like, the 6-month range?
84 00:15:01.890 ⇒ 00:15:12.100 Garrett: like, more recently, you mean, like, at Disney? So that one, the one at Disney was, like, more of a short-term, like, consulting arrangement, so kind of similar.
85 00:15:12.290 ⇒ 00:15:22.870 Garrett: I was hired on as a PM, working with, like, a system architect, as well as a couple data engineers. And for that, we were asked to come in and
86 00:15:23.070 ⇒ 00:15:27.209 Garrett: Essentially, build the requirements in the system design.
87 00:15:27.650 ⇒ 00:15:37.389 Garrett: And essentially do an assessment of the current state system architecture, and then how we would implement the data warehouse, you know, being Databricks.
88 00:15:37.750 ⇒ 00:15:48.990 Garrett: And then connect that to the upstream and downstream systems, you know, being Snowflake, we had BigQuery, and then we also had Google Cloud Platform.
89 00:15:50.390 ⇒ 00:15:57.860 Garrett: And so, the initial, like, requirements, documentation, and kind of planning for the project was just, you know, a couple of months.
90 00:15:58.000 ⇒ 00:16:08.739 Garrett: And then after that, you know, a couple months later, we’ve… we’re able to kind of stand up the data warehouse, then hand it off to the internal team. So that was just really, like, a 4-month kind of project.
91 00:16:09.520 ⇒ 00:16:10.540 Garrett: For Disney?
92 00:16:11.050 ⇒ 00:16:13.419 Garrett: But that’s kind of, like, I guess,
93 00:16:14.150 ⇒ 00:16:20.129 Garrett: an example of, like, a consulting, you know, kind of project that I did, where it wasn’t really, like, internally. It was, like, me working
94 00:16:20.480 ⇒ 00:16:25.320 Garrett: You know, for a vendor, like, kind of supporting Disney, you know, internal teams, so…
95 00:16:26.180 ⇒ 00:16:38.070 Amber Lin: Gotcha, okay. Let me see… yeah, and I had another question on… because I know you’ve been working with these really, really big companies, big names, really.
96 00:16:38.070 ⇒ 00:16:38.610 Garrett: Yeah.
97 00:16:38.620 ⇒ 00:16:57.370 Amber Lin: But we’re quite small, and we’re mostly a consulting company, and sometimes we have very urgent requirements from clients, and the timeline gets very short, because the clients need to talk to their boss, which needs a response tomorrow. So I was wondering if you have
98 00:16:57.440 ⇒ 00:16:59.899 Amber Lin: had experiences of…
99 00:17:00.220 ⇒ 00:17:10.799 Amber Lin: having to turn around something really quick, the client’s not happy with what we just gave them, and they want a response tomorrow. Like, do you have experiences dealing with that?
100 00:17:11.140 ⇒ 00:17:28.880 Garrett: Yeah, absolutely. So, I guess, another kind of example, when I was working on contract at Apple, like, asked to come in and build, you know, executive dashboards, I was able to… so they didn’t have, like, any kind of Tableau repository stood up, they didn’t have any, you know, data warehouse, they didn’t have…
101 00:17:28.980 ⇒ 00:17:39.730 Garrett: kind of anything to support, like, data analytics, you know, for Jira. It was just kind of all, like, out-of-box JIRA. You could go in and look, like, by project and things like that, so there was no…
102 00:17:39.950 ⇒ 00:17:44.980 Garrett: Kind of portfolio-level view across media and entertainment, so that was kind of the…
103 00:17:45.160 ⇒ 00:18:02.299 Garrett: the goal of my role, so to speak. So essentially what I did is to kind of get up to speed quickly as I worked, with internal teams that I identified within Apple that were, like, already data analytics teams. And with them, I was able to, you know, stand up a data warehouse, and then
104 00:18:02.300 ⇒ 00:18:06.720 Garrett: You’ll get a Tableau repository set up and all those kinds of things, and…
105 00:18:06.840 ⇒ 00:18:19.570 Garrett: With that, and within combination of using, AI tools, I was able to already start producing dashboards and get, you know, feedback within, I would say, like, 2 weeks, 2 to 3 weeks.
106 00:18:19.810 ⇒ 00:18:29.860 Garrett: And that was, pretty detailed dashboard views, like, you know, views that had, like, over several hundred lines of SQL code.
107 00:18:30.170 ⇒ 00:18:39.549 Garrett: So, you know, to be able to get to that state in just, like, you know, a couple weeks, you know, before AI tools and things like that, it would have taken me, like, a couple months, probably.
108 00:18:39.710 ⇒ 00:18:40.500 Garrett: So…
109 00:18:40.970 ⇒ 00:18:46.550 Amber Lin: Gotcha, okay, so when you talk about AI tools you use, can you go a bit, deeper on that?
110 00:18:47.000 ⇒ 00:19:06.230 Garrett: Yeah, sure, so, so think, like, so Claude, you know, like, just take that as an example. So, like, if I have an AI project that I’m designing, so just, like, the Jira kind of API one, first I would design kind of, like, the schema based on the data within the tool. So, like, Jira is very…
111 00:19:06.440 ⇒ 00:19:22.970 Garrett: like, hierarchy-based, you know, you have, like, your epics, you have your features, your stories. So first I was just thinking in terms of, like, the design of the data, like, the data lineage, you know, how, would the data link together, and things like that, and actually, like, running
112 00:19:22.970 ⇒ 00:19:27.309 Garrett: sample queries within the UI itself, right? To have…
113 00:19:27.370 ⇒ 00:19:45.330 Garrett: like, a set of requirements based on the data to ensure, like, things like I’m capturing all stories, you know, across, like, the media and entertainment projects, right? Because there’s, like, 25 of them, so there’s quite a bit of them. So just doing that kind of, like, upfront analysis, in the UI.
114 00:19:45.440 ⇒ 00:19:59.579 Garrett: And then, you know, designing how, like, the schema would look in the backend, right, within the data warehouse. So having, like, you know, stories table and things like that. And then, I would create kind of, like, a design.
115 00:19:59.580 ⇒ 00:20:12.740 Garrett: or have… or feed this to AI to have AI create, like, a schema design for myself. And then once I have that schema design, then AI can understand the thinking in terms of, like, joins.
116 00:20:12.870 ⇒ 00:20:15.909 Garrett: Of, like, tables that I would use in the data warehouse.
117 00:20:16.100 ⇒ 00:20:31.769 Garrett: And then from there, I can prompt AI to build, SQL queries based on requirements for views. So, like, if I have a requirement for review for a capacity planning model, like, what attributes do I need in my dataset to support that model? So I would need, like.
118 00:20:31.880 ⇒ 00:20:40.010 Garrett: all the stories across all the Jira projects, I would need some quantitative measurement, like a story point, you know, identifier.
119 00:20:40.240 ⇒ 00:20:58.329 Garrett: I would need to know, like, what team, is that story supporting? You know, all those kind of attributes created in one, wide data set. And then once I have that data set from, like, a query, you know, that AI, provides to me, I can kind of iterate on it
120 00:20:58.500 ⇒ 00:21:04.330 Garrett: And, get it to the point where it provides all the attributes to build the views, you know, in Tableau.
121 00:21:04.690 ⇒ 00:21:05.980 Garrett: That they’re requesting.
122 00:21:06.420 ⇒ 00:21:22.200 Garrett: And then think about, you know, how do I want to… how often do I want to update that data? What kind of KPIs do I want to show on the dashboard? So I think, you know, using, like, Claude and things like that can make that a lot faster, so…
123 00:21:22.200 ⇒ 00:21:30.169 Amber Lin: Awesome. Yeah, I can… I can tell you a bit more about how we use AI internally later. I think my last question.
124 00:21:30.880 ⇒ 00:21:36.810 Amber Lin: is why… why Brainforce, among all these companies, like, why do you look at us?
125 00:21:37.490 ⇒ 00:21:45.299 Garrett: Yeah, absolutely. One of the really exciting things is, I think a lot of the work that you guys are doing, you know, in terms of the data analytics and AI space,
126 00:21:45.490 ⇒ 00:21:49.579 Garrett: Has, like, really strong parallels with my background, and
127 00:21:49.770 ⇒ 00:22:01.700 Garrett: you know, I’ve definitely… I’ve been in this kind of data analytics space for a long time now, and I think that, you know, it seems like a great company where I could kind of grow my career and, you know, work on exciting projects and things like that, so…
128 00:22:02.030 ⇒ 00:22:18.740 Garrett: Yeah, seems like, kind of a great opportunity, good fit. Oh, also, one thing I’m not sure if you’re for… or I did… was referred by my, like, a former recruiter, but she’s also kind of a friend, so it’s… we get to, like, you know, work together as well, so… Oh, cool.
129 00:22:18.740 ⇒ 00:22:21.489 Amber Lin: Cool. Oh, you worked with Kayla before?
130 00:22:21.490 ⇒ 00:22:25.510 Garrett: Yeah, she helped me actually get that job at Disney, the contract that I was on.
131 00:22:25.510 ⇒ 00:22:26.470 Amber Lin: Oh, awesome!
132 00:22:26.470 ⇒ 00:22:30.449 Garrett: Yeah, so, yeah, we have a rapport there, so…
133 00:22:30.570 ⇒ 00:22:34.089 Amber Lin: Awesome. Okay. Any questions I can help answer?
134 00:22:34.370 ⇒ 00:22:45.359 Garrett: Yeah, absolutely. So I have, just, like, a couple questions I wrote down here. So, like, in terms of the, kind of, like, the overall, development process, you know, things like that.
135 00:22:45.450 ⇒ 00:23:00.300 Garrett: How would I… how would I come in and kind of help, improve that process, like, in terms of the first, like, 6 months, you know, into the role? Is there, like, a certain part of that that I would own, like, in terms of my role, or is it kind of, like,
136 00:23:01.010 ⇒ 00:23:03.510 Garrett: a collaborative, you know, thing.
137 00:23:04.610 ⇒ 00:23:05.040 Garrett: That’s one other than the.
138 00:23:05.040 ⇒ 00:23:21.120 Amber Lin: So, let me… let me break it down into a few parts. I think first, we have 30, 60, 90-day plans for our new hires. Okay, great. So, I think as part of that, we will set together, okay, these are the plans, these are our goals we want to achieve. Oh, okay.
139 00:23:21.120 ⇒ 00:23:21.610 Garrett: Great.
140 00:23:21.610 ⇒ 00:23:34.090 Amber Lin: Then, secondly, most of our hires start off with a short trial period, and then you get placed on… so from day one, you get placed on to a client project.
141 00:23:34.090 ⇒ 00:23:44.839 Amber Lin: Okay. And depending how that goes, you might change the role you’re interested in, or we think, we both think we’re a fit, then we’ll continue to a full-time position.
142 00:23:44.840 ⇒ 00:23:45.330 Garrett: Okay.
143 00:23:45.330 ⇒ 00:23:55.119 Amber Lin: And so, depending on what client projects you get staffed on, it sounds like, from your experience, it could be a data project, could be a, more strategy, data analysis.
144 00:23:55.440 ⇒ 00:24:08.050 Amber Lin: reporting projects, so depending on that, it would shape, like, what type of responsibilities you hold. Okay, got it. But I… I was very interested in… in the AI workflow that you’re…
145 00:24:08.310 ⇒ 00:24:09.830 Amber Lin: building, and…
146 00:24:09.830 ⇒ 00:24:10.190 Garrett: Yeah.
147 00:24:10.190 ⇒ 00:24:16.309 Amber Lin: I think one of our initiatives right now, we are building a lot of, the…
148 00:24:16.710 ⇒ 00:24:32.860 Amber Lin: So, you know, the new AI skills, commands, etc, for all of the workflows internally. Not only for, say, project management, we have a lot of, say, use this… use this command to audit all the tickets, use this command to summarize everything that happened in Slack.
149 00:24:32.860 ⇒ 00:24:38.060 Amber Lin: So we’re building operational tools, but right now, we’re… Missing?
150 00:24:38.060 ⇒ 00:24:52.630 Amber Lin: the specific knowledge in the domain, such as how do you do this specific analysis for this type of client, with these set of KPIs? How do you build an automated process that can go through that?
151 00:24:52.730 ⇒ 00:25:01.100 Amber Lin: That’s what I’m starting to look into, that’s what I’m… we’re working with AI team internally to see what we can build, but I think…
152 00:25:01.210 ⇒ 00:25:11.639 Amber Lin: since you have described your experience in that, I think it would be really cool if you were to lead that type of automation and workflow development.
153 00:25:11.640 ⇒ 00:25:15.489 Garrett: for the work streams that you’re interested in, so maybe for…
154 00:25:15.490 ⇒ 00:25:29.210 Amber Lin: for setting up data, for setting up things for analysis. I think that type of specific experience of doing it without AI is what we’re looking for right now.
155 00:25:29.390 ⇒ 00:25:31.000 Garrett: Great. So I hope that answers…
156 00:25:31.000 ⇒ 00:25:31.640 Amber Lin: question.
157 00:25:31.640 ⇒ 00:25:39.230 Garrett: Yeah, no, absolutely, and yeah, good to know that my experience supports that, so… And then,
158 00:25:39.450 ⇒ 00:25:46.020 Garrett: One of the next questions I had, you mentioned, you know, it’s important to deliver results in weeks, you know, essentially.
159 00:25:46.240 ⇒ 00:25:57.139 Garrett: So how… how does the current, like, program delivery infrastructure, kind of support that, and how does that role fit into that? Like, would I be responsible for
160 00:25:57.330 ⇒ 00:26:02.969 Garrett: You know, just ensuring, kind of, the… those timelines for… for those specific projects and things like that?
161 00:26:03.720 ⇒ 00:26:27.010 Amber Lin: Gotcha. Okay, so our clients start off pretty small, on smaller engagements, and then we expand them to longer 3-month, 6-month contracts, and we generally sync with clients every week, and usually there’s the bigger deliverable on the end of the timeline, then each week there’s a small deliverable, we update them on, hey, this is what we’re doing.
162 00:26:27.170 ⇒ 00:26:30.599 Garrett: Okay. And I think at your seniority, when you come in.
163 00:26:30.600 ⇒ 00:26:43.089 Amber Lin: You would be owning a deliverable, or you might be owning the communication with clients. So, clients would tell you, hey, these are the responsibilities, or things we want to achieve.
164 00:26:43.090 ⇒ 00:26:54.079 Amber Lin: And you can communicate with internal teams to say, hey, can we do this on time? Can we do this this way? Or do I have to communicate to clients what compromises we have to make?
165 00:26:54.190 ⇒ 00:26:55.800 Garrett: Okay. So… Great.
166 00:26:55.800 ⇒ 00:27:01.860 Amber Lin: Usually, usually the clients do expect things to be,
167 00:27:02.050 ⇒ 00:27:11.820 Amber Lin: to be fast, or have a certain period of SLA, because we’re working with external clients, and usually they don’t have that much…
168 00:27:12.180 ⇒ 00:27:13.460 Amber Lin: time.
169 00:27:13.620 ⇒ 00:27:16.939 Garrett: Are you guys doing Agile, like, sprint planning and stuff like that right now?
170 00:27:16.940 ⇒ 00:27:26.950 Amber Lin: Yeah, I think we were… we used to be more robust on it when we had our PMO, so right now, we kind of run each project.
171 00:27:27.720 ⇒ 00:27:33.439 Amber Lin: I guess we’re more agile than before, because we have our, on our projects.
172 00:27:33.610 ⇒ 00:27:51.030 Amber Lin: We have the project team members take on roles such as the engagement planner, which is more similar to a PM role. We have, the… one of them take on communications with the clients, so owning the relationship, owning future renewals.
173 00:27:51.030 ⇒ 00:28:07.640 Amber Lin: And we have also, someone who’s very senior in that specific domain, such as data or AI, to be staffed across different client projects to say, hey, this is how you can architecture this, this is how you can solve this more complex problem.
174 00:28:07.710 ⇒ 00:28:08.420 Amber Lin: Okay.
175 00:28:08.420 ⇒ 00:28:08.980 Garrett: Got it.
176 00:28:08.980 ⇒ 00:28:13.909 Amber Lin: Yeah, so I think if you were to come in, you probably would fit very well into…
177 00:28:14.030 ⇒ 00:28:20.780 Amber Lin: planning the engagement, making sure the project goes forward, but I think with your experience, like, working
178 00:28:21.200 ⇒ 00:28:29.240 Amber Lin: as an external person into the client, so you’ve essentially worked as a consultant, I think it would also be fitting for you to.
179 00:28:29.350 ⇒ 00:28:32.869 Garrett: Manage the client relationships, manage…
180 00:28:32.870 ⇒ 00:28:39.450 Amber Lin: Get a renewal, get an expansion. So that’s kind of, where I see your fit.
181 00:28:39.980 ⇒ 00:28:49.410 Garrett: Yeah, that sounds great. Awesome. Yeah, I think that’s kind of the main… main questions that I had to hit on. Awesome!
182 00:28:49.410 ⇒ 00:29:09.049 Amber Lin: We’re right on time, so the next steps is I’ll send my notes to the operations, to the internal recruiting team, and the operations team will get back to you no matter what the results are, I think within 2 weeks or so. Okay. And if we were to advance, I believe there’s 2 more rounds of interview.
183 00:29:09.050 ⇒ 00:29:09.560 Garrett: Yes.
184 00:29:09.560 ⇒ 00:29:10.870 Amber Lin: I think that’s Yeah.
185 00:29:10.870 ⇒ 00:29:12.210 Garrett: Kayla said in the email? Yep.
186 00:29:12.210 ⇒ 00:29:13.440 Amber Lin: Awesome, okay.
187 00:29:13.440 ⇒ 00:29:14.650 Garrett: Awesome. Yeah.
188 00:29:14.650 ⇒ 00:29:17.380 Amber Lin: Thank you for taking the time to talk, it’s been great.
189 00:29:17.550 ⇒ 00:29:19.450 Garrett: Alright, thanks so much, Jammer, it’s great meeting you.
190 00:29:19.450 ⇒ 00:29:20.250 Amber Lin: Yeah.
191 00:29:20.250 ⇒ 00:29:21.700 Garrett: Alright, have a great rest of your day.
192 00:29:21.700 ⇒ 00:29:22.030 Amber Lin: I…
193 00:29:22.030 ⇒ 00:29:23.070 Garrett: Bye-bye.