Meeting Title: Brainforge x Garrett Second Interview Date: 2026-03-10 Meeting participants: Garrett, Greg Stoutenburg
WEBVTT
1 00:02:22.510 ⇒ 00:02:23.210 Greg Stoutenburg: Hey, Garrett!
2 00:02:23.210 ⇒ 00:02:24.960 Garrett: Hey, great. Hey, dude.
3 00:02:24.960 ⇒ 00:02:26.580 Greg Stoutenburg: Hey, doing alright, how are you today?
4 00:02:26.580 ⇒ 00:02:28.990 Garrett: Good, how you doing? Nice to meet with you.
5 00:02:29.160 ⇒ 00:02:32.920 Greg Stoutenburg: Yeah, nice to meet you too. Where are you located?
6 00:02:33.130 ⇒ 00:02:36.669 Garrett: I’m in a Redondo Beach area in LA. How about you?
7 00:02:36.980 ⇒ 00:02:40.789 Greg Stoutenburg: Oh, cool. I’ve only heard of Redondo Beach, thanks to the Beach Boys.
8 00:02:40.790 ⇒ 00:02:42.180 Garrett: Oh, okay.
9 00:02:42.180 ⇒ 00:02:44.969 Greg Stoutenburg: Isn’t that, what song is that?
10 00:02:45.820 ⇒ 00:02:51.040 Garrett: I can’t remember, that’s… It’s one of the originals, isn’t it, I think? From their…
11 00:02:51.470 ⇒ 00:02:53.569 Greg Stoutenburg: We’re down to a beach, LA.
12 00:02:53.570 ⇒ 00:02:58.700 Garrett: Yeah, exactly. Yeah, right near, kind of LAX, you know, in the South Bay.
13 00:02:59.370 ⇒ 00:03:02.270 Garrett: Yeah. Where are you at, right now?
14 00:03:02.810 ⇒ 00:03:08.310 Greg Stoutenburg: Yeah, it is in Surfin USA, I had to check. Cool. I’m in York, Pennsylvania.
15 00:03:08.310 ⇒ 00:03:09.290 Garrett: Oh, okay. East Coast.
16 00:03:09.290 ⇒ 00:03:19.149 Greg Stoutenburg: Near Baltimore and Harrisburg, yeah. Cool. Originally from Michigan, but, you know, I ended up here because, you know, one moves around.
17 00:03:19.150 ⇒ 00:03:24.280 Garrett: Yeah, exactly. Well, the weather right now in Michigan isn’t… isn’t too good either, right?
18 00:03:24.280 ⇒ 00:03:28.509 Greg Stoutenburg: Yeah, I was actually just there this last weekend, and it was just rainy and foggy the whole time.
19 00:03:28.510 ⇒ 00:03:30.010 Garrett: Yeah. I bet.
20 00:03:30.280 ⇒ 00:03:35.109 Greg Stoutenburg: So, it’s actually nicer in Pennsylvania, and I’ll be headed to Florida tomorrow, so…
21 00:03:35.110 ⇒ 00:03:36.320 Garrett: Yeah, that’d be nice.
22 00:03:36.320 ⇒ 00:03:38.760 Greg Stoutenburg: Continue… continue wishing me luck on the weather front.
23 00:03:42.310 ⇒ 00:03:49.660 Greg Stoutenburg: Yeah, alright, well, yeah, I mean, maybe we can just start by just, you know, I’m… I believe this is your second interview, right? Absolutely.
24 00:03:49.660 ⇒ 00:03:53.360 Garrett: Exactly, yeah, I met with, yeah, Anna in the first round there.
25 00:03:53.680 ⇒ 00:03:57.019 Greg Stoutenburg: Okay, cool, yeah, what interested you in Brainforge?
26 00:03:57.530 ⇒ 00:04:09.970 Garrett: Well, actually, it’s funny, my, my friend who was a recruiter, my last role, she, kind of, hit me up about the company, and, she was actually just hired here recently, so.
27 00:04:09.970 ⇒ 00:04:10.290 Greg Stoutenburg: Oh.
28 00:04:10.290 ⇒ 00:04:17.399 Garrett: I just saw, it was cool when I was just researching the company, I just saw a lot of synergies, you know, with my kind of analytics background, and…
29 00:04:17.399 ⇒ 00:04:34.629 Garrett: You know, it’s really interesting that you guys are kind of like a, you know, consultancy, you know, standing up, analytics and, you know, data infrastructure projects, you know, for different companies, and definitely really aligns with my background, you know, just coming from, Disney. I worked on a big, like, data lake initiative there.
30 00:04:34.880 ⇒ 00:04:35.260 Greg Stoutenburg: Okay.
31 00:04:35.260 ⇒ 00:04:41.839 Garrett: So, thank, you know, kind of setting up a new data warehouse for the ad sales team there.
32 00:04:42.380 ⇒ 00:04:42.710 Greg Stoutenburg: Okay.
33 00:04:42.710 ⇒ 00:05:02.069 Garrett: So this was in, in Databricks, and then, within the kind of data lake, ecosystem that they had, we were, setting up, you know, connectivity to other, like, upstream and downstream systems as well. So think, like, you know, Snowflake, BigQuery, you know, Google Cloud.
34 00:05:02.470 ⇒ 00:05:14.819 Garrett: And this was all to kind of support, like, faster, time to insight for the ad sales team, so, they had the specific, you know, targeted segmentation data that they needed access to, and then.
35 00:05:14.820 ⇒ 00:05:23.499 Garrett: this kind of helped them provide, you know, targeted ads, you know, to customers across all the different, like, apps, you know, like ESPN and…
36 00:05:23.500 ⇒ 00:05:26.309 Garrett: Disney+, you know, Hulu, things like that, so…
37 00:05:26.310 ⇒ 00:05:37.979 Greg Stoutenburg: Cool, nice. Were they… when you were working with the team on that, like, what was… what was your role in the team? Like, were you talking with clients about what they wanted to see and what they wanted to do, or…
38 00:05:37.980 ⇒ 00:05:45.569 Garrett: Exactly, yeah, so, like, interfacing, with different stakeholders, keeping track of, like, the overall plan, so, like, you know, roadmap planning.
39 00:05:45.570 ⇒ 00:06:03.060 Garrett: Keeping track of, like, granular task tracking, so we had, like, GitHub repositories and Kanban boards and things like that, so we were able to, like, keep, you know, PR merges, you know, with the tasks to kind of keep everything, you know, organized, like, when setting up the data warehouse, for example.
40 00:06:03.060 ⇒ 00:06:07.510 Garrett: But just kind of, like, you know, facilitating, the program, ensuring
41 00:06:07.630 ⇒ 00:06:23.639 Garrett: You know, things were, you know, run… running smoothly, gathering all the kind of, like, initial requirements, right, from the teams, and working closely with, the system architect as well, right, for creating that kind of assessment of the current state.
42 00:06:23.640 ⇒ 00:06:33.799 Garrett: of the system architecture, and then how would the, you know, the future state look like with, you know, implementation of Databricks and, you know, connecting to the other systems and things like that, so…
43 00:06:34.600 ⇒ 00:06:39.770 Greg Stoutenburg: Cool. What, what part would you say was your favorite part of the project?
44 00:06:40.180 ⇒ 00:06:51.810 Garrett: I’d say my favorite part was probably, just getting more familiar with some of the technologies, like, for one, for Databricks, you know, I hadn’t used, that much in the past, so it was cool to…
45 00:06:51.820 ⇒ 00:07:06.439 Garrett: You know, get more kind of acquainted with that. What I did use is I used, more AI tools to kind of facilitate some of the, like, weekly reporting that I created. So as close as I used, Claude, which created a lot of, like.
46 00:07:06.630 ⇒ 00:07:09.930 Garrett: It was, like, an on-the-fly, React Native.
47 00:07:10.130 ⇒ 00:07:16.780 Garrett: code, that would just, like, visualize, a PowerPoint, like, roadmap slide.
48 00:07:16.780 ⇒ 00:07:17.100 Greg Stoutenburg: Sorry.
49 00:07:17.100 ⇒ 00:07:31.869 Garrett: Oh yeah, no worries. Keep going. Yeah, yeah. And this was really cool, because you could, like, show different work streams and, like, visualize, like, time-based roadmap progress, and then just, like, update milestone statuses and things like that on the fly.
50 00:07:32.230 ⇒ 00:07:40.260 Garrett: So I would kind of use that, for, like, weekly reporting, and then send that out to, like, the executive team, and they just kind of appreciated, you know, that format, and…
51 00:07:40.370 ⇒ 00:07:45.810 Garrett: You know, just being able to kind of, use AI tools more, like in the kind of day-to-day.
52 00:07:45.940 ⇒ 00:07:54.750 Garrett: Yeah. That was kind of, you know, a benefit from my side, and just exploring, other technologies, you know, I haven’t really worked with in past roles, so…
53 00:07:54.750 ⇒ 00:07:57.089 Greg Stoutenburg: Yeah, cool. Do you feel like it sped you up?
54 00:07:57.090 ⇒ 00:08:07.569 Garrett: Yeah, absolutely. I felt like, using AI in general, even before that, I was at, Apple on a contract, and there I was building a lot of…
55 00:08:07.600 ⇒ 00:08:25.420 Garrett: kind of, like, custom dashboards for executives and things like that, and they wanted more, kind of, custom views into the Jira data. So think, like, pulling, like, from the Jira API all the scattered Jira projects into, like, one cohesive, like, data set.
56 00:08:25.420 ⇒ 00:08:32.400 Garrett: And then calling it, like, Media and Entertainment Teams. Yeah. And then from that, you know, they wanted,
57 00:08:32.400 ⇒ 00:08:46.879 Garrett: So I created, like, custom SQL code that would give me wide data sets to build things like capacity planning models to see, like, you know, based on any given team, like, you know, what is their given capacity and, you know, how much capacity could they take on?
58 00:08:46.880 ⇒ 00:08:49.570 Garrett: Based on their team members, like, on any given sprint.
59 00:08:49.710 ⇒ 00:09:03.240 Garrett: And then, like, what projects, you know, do their resources correlate to, right, from the overall, like, big picture roadmap, which I also created, which was, like, another review, to kind of just tie the capacity and, like, roadmap planning, together.
60 00:09:03.240 ⇒ 00:09:19.549 Garrett: So I built all these views in Tableau, which was, like, Apple has a big Tableau presence. And then I just worked internally with data teams to spin up a, like, PostgreSQL database where I could store the Jira data, and then
61 00:09:19.580 ⇒ 00:09:36.869 Garrett: built, like, a schema, like, so each table was, like, stories, or, like, features, or, like, epics, and then I created, like, a join key that would allow me to join them all together based on the sample queries that I did in the Jira UI, you know, just JQLs and things like that.
62 00:09:37.070 ⇒ 00:09:44.190 Garrett: But then that way, I had, like, a cohesive, you know, data model, right, that I could kind of slice and dice, things like that.
63 00:09:44.840 ⇒ 00:09:56.339 Greg Stoutenburg: Yeah, okay, nice, cool. That’s, I mean, that’s a lot. Did the, I mean, did the team… did the team like those resources? Like, were they really able to, you know, use them on the day-to-day, use them for planning and things?
64 00:09:56.340 ⇒ 00:10:14.679 Garrett: Yeah, so this was really more supporting, like, she… I think she was, like, a director and product strategy, so this was, like, specifically for, you know, media and entertainment, and then, you know, for her to be able to see it, like, that bigger, portfolio level to see, you know, how many teams she had, you know, what they were working on.
65 00:10:14.680 ⇒ 00:10:20.079 Garrett: Opportunities, you know, to move teams around onto different projects, you know, things like that.
66 00:10:20.290 ⇒ 00:10:37.120 Greg Stoutenburg: Yeah. Yeah. Cool. Nice. I mean, so it sounds like you’re someone who can, like, you know, hear that some leader, hear that some stakeholder has a problem, and kind of, like, go, alright, let’s take these problems and, like, work out, work toward a solution. Exactly.
67 00:10:37.120 ⇒ 00:10:37.810 Garrett: Yeah.
68 00:10:37.810 ⇒ 00:10:53.609 Greg Stoutenburg: Like, broadly speaking. Yeah. Yeah. Is that, I mean, is that sort of, like, your area of emphasis? Like, you know, if you… if you didn’t… if you didn’t have to work for money, you did have to work in data. Yeah. Is that… is that the sort of thing you do… you would do, or, like, how would.
69 00:10:53.610 ⇒ 00:11:08.969 Garrett: Yeah, I do like the process of just, like, the analysis, like, building data models, and then, like, the dashboard visualization piece of it, too, like, being creative and, you know, experimenting with design and views and things like that, so… Yeah. Yeah.
70 00:11:08.970 ⇒ 00:11:09.500 Greg Stoutenburg: Yeah.
71 00:11:10.060 ⇒ 00:11:12.019 Greg Stoutenburg: Yeah, okay.
72 00:11:12.020 ⇒ 00:11:27.869 Garrett: And so I feel like I’m kind of more, like, a technical PM, because I’m, like, kind of bridging those two skill sets, you know, together. It’s, like, kind of the business, but then also, like, the data, analytical, you know, as well. Yeah. And then also when I was at DirecTV, that’s… I started it out kind of as, like, a…
73 00:11:28.090 ⇒ 00:11:30.510 Garrett: a PM and the PMO, so, like, think, like.
74 00:11:30.510 ⇒ 00:11:30.990 Greg Stoutenburg: -
75 00:11:30.990 ⇒ 00:11:49.460 Garrett: implementing Jira and JiraLine for, like, a large-scale transformation. So, at that time, DirecTV was satellite. They were, you know, standing up their DirecTV stream business, and so they had, like, kind of, like, an agile transformation, you know? So they had, like, 60 scrum teams at that time.
76 00:11:49.460 ⇒ 00:11:59.440 Garrett: And I had to kind of just go in there, you know, set up JIRA, you know, how the requirements hierarchy look like, you know, create training plans, all those, you know, kind of things. But then after that, once the…
77 00:11:59.550 ⇒ 00:12:06.320 Garrett: app launched, I was more kind of on the data analytics side, so I think, like, you know, partnering with, like, product management.
78 00:12:06.370 ⇒ 00:12:18.539 Garrett: Assessing, new feature implementations to see, like, how does that correlate with, you know, subscriber growth or, you know, reducing churn and ensuring, like, development dollars are…
79 00:12:18.540 ⇒ 00:12:28.530 Garrett: kind of in the correct place. So, for that, we were leveraging other, like, analytics platforms, like New Relic, which you might know, which is like a, you know, observability tool.
80 00:12:28.650 ⇒ 00:12:41.719 Garrett: And so, from there, we were able to see, like, okay, if we implemented a sports experience, you know, feature package, if, like, users go into the app and they add, you know, favorite teams, are they…
81 00:12:41.820 ⇒ 00:12:56.169 Garrett: you know, add favorite leagues, you know, how many clicks, you know, does that kind of sum up to within the app, you know, based on the total user base, to kind of see, like, you know, are they actually using those features, right? Like, from a usage perspective, so…
82 00:12:56.420 ⇒ 00:12:57.060 Greg Stoutenburg: Yeah.
83 00:12:57.140 ⇒ 00:12:58.480 Garrett: Yeah, I mean…
84 00:12:58.480 ⇒ 00:13:02.400 Greg Stoutenburg: Well, when someone was using those features, did it correlate to anything?
85 00:13:02.630 ⇒ 00:13:09.539 Garrett: Yeah, so we were able to see also, not only just kind of, like, at a per-user basis, but also, like, per client, like.
86 00:13:09.540 ⇒ 00:13:29.429 Garrett: you know, because the development was kind of siloed at that time as well, so you had, like, Fire TV, you know, versus iOS, etc. Now it’s all kind of unified over time. But at that time, it was really important to kind of see, you know, by client, you know, what was, you know, kind of driving the stickiness, you know, with the app. Yeah.
87 00:13:30.000 ⇒ 00:13:39.700 Greg Stoutenburg: Yeah, yeah, yeah. Yeah. Okay, cool. Well, we’re… I mean, I have a feeling we could just… we could just stay in the high level for, all day.
88 00:13:39.700 ⇒ 00:13:40.310 Garrett: Totally.
89 00:13:40.310 ⇒ 00:13:58.650 Greg Stoutenburg: I’ll look at some of my, I’ll look at some of the, sort of, the pre-written questions for this stage. So this is interview number two, this is sort of the more, little bit of the more technical one, but you’ve already just, you know, covered so much… a lot of your experience, so I’ll just, like, ask some of these specific questions. Sure. Tell me about an insight that changed product direction.
90 00:13:59.230 ⇒ 00:14:19.150 Garrett: Yeah, so I’ll kind of dig in more to, like, the sports experience one. So at that time, you know, the sports experience features, like, I think we built, like, 6 or 7 various Tableau dashboard views, and each one of those views, was a specific metric, right? Like.
91 00:14:19.150 ⇒ 00:14:33.999 Garrett: One view was about leagues, right? One view was about adding and favoriting teams. So we kind of tried to… to break it down at that level as well, you know, slice and dicing the different sports data, right? If you will.
92 00:14:34.000 ⇒ 00:14:41.449 Garrett: And that way, you know, we’re able to kind of give more insights into that buy-client, you know, kind of framing.
93 00:14:41.450 ⇒ 00:14:53.380 Garrett: Which I think was a big request from the, like, the product management team had their initial requirements, and usually whenever they needed a dashboard, it was always, you know, by client, you know, iOS versus Fire TV, etc.
94 00:14:53.510 ⇒ 00:15:12.099 Garrett: And that way, it was, that way it was more targeted, though, as well, right? Because, you know, product management being such a big organization, they had, you know, different ownership of, you know, specific areas, like, you know, they had specific client managers who owned, you know, Fire TV, or who owned, you know, iOS, and so…
95 00:15:12.130 ⇒ 00:15:22.149 Garrett: We had to ensure that the insights we were providing were very targeted in that sense, because then we knew, like, you know, which scrum teams were going to be involved in
96 00:15:22.190 ⇒ 00:15:27.460 Garrett: you know, new back, feature backlog work, right? Feature backlog planning.
97 00:15:27.630 ⇒ 00:15:42.909 Garrett: And so that was kind of the, the importance, right, of the insights that we provided, is that, you know, they were targeted, and that we were able to kind of, directly, you know, correlate them to, like, future planning, right, in terms of development and things like that.
98 00:15:42.910 ⇒ 00:15:43.240 Greg Stoutenburg: Yeah.
99 00:15:43.240 ⇒ 00:15:43.610 Garrett: Awesome.
100 00:15:43.610 ⇒ 00:16:00.970 Greg Stoutenburg: Yeah, yeah. Yeah, no, that’s good. Okay, so then you get those… so then you get those insights, and you know they have to assign to certain teams, because the question I’m trying to… I’m trying to ask is, once you’ve got the insights, and it’s time to feed that back into, you know, the product planning, cycle, what…
101 00:16:00.970 ⇒ 00:16:04.240 Garrett: What has happened then when you’ve fed those insights back?
102 00:16:04.370 ⇒ 00:16:13.350 Garrett: Right, so that usually turns into, like, new features, that turns into, potentially bug fixes, right, as well? Like, you know, you might…
103 00:16:13.350 ⇒ 00:16:26.710 Garrett: see things as we’re testing in the app. So one of the interesting things about this project, as well as having access to test accounts, we were able to kind of build dashboards, you know, as we were in parallel using the app, as well.
104 00:16:26.900 ⇒ 00:16:41.310 Garrett: So with that, not only, you know, did this come with, like, new features and new user stories, you know, kind of new development tickets within the product backlog, but it was also, like, bug tickets, right? Like, fixing, you know, actual defects. So it’s kind of, like, fixing…
105 00:16:41.390 ⇒ 00:16:53.770 Garrett: like, two birds, you know, with one stone, or killing two birds with one stone. And so it was really kind of the overall, you know, product prioritization that it fed into. It wasn’t just really, like, you know, new development or something like that, so…
106 00:16:53.770 ⇒ 00:16:56.530 Greg Stoutenburg: Yeah, okay, cool, yeah, fair enough.
107 00:16:57.110 ⇒ 00:17:02.130 Greg Stoutenburg: Here’s one. When should you not ship a data-backed recommendation?
108 00:17:03.300 ⇒ 00:17:12.280 Garrett: When should you not ship date back recommendation? I would say without getting, kind of, formal, sign-off, right, if you will, so…
109 00:17:12.500 ⇒ 00:17:28.630 Garrett: You know, when you’re… when you’re shipping a, you know, recommendation, you just want to ensure that you have, like, final, you know, sign-off and approval, right? Does it meet, you know, kind of that success criteria? Does it meet all the, the requirements, right? That definition of done, as well.
110 00:17:29.010 ⇒ 00:17:40.800 Garrett: So I think that it’s… I mean, it doesn’t have to be, like, you know, a long, exhaustive, you know, kind of process, but just ensuring that, like, everyone is in alignment, you know, before it gets shipped out, kind of before that, like, you know, go-live.
111 00:17:41.040 ⇒ 00:17:50.519 Greg Stoutenburg: Yeah, yeah. Yeah, now, if you’re gonna talk to, I mean, certainly this has been my experience, if you’re gonna talk to executives, they like things, clear, opinionated, pretty simple, right? Right.
112 00:17:50.940 ⇒ 00:18:00.839 Greg Stoutenburg: So say, you know, say you’re doing your, your, your analytical work, and you have to, you know, you have to make some recommendations, and you’ve, you’ve got, like, 8.
113 00:18:01.030 ⇒ 00:18:04.080 Greg Stoutenburg: But 8’s too many. They’re not comfortable.
114 00:18:04.740 ⇒ 00:18:05.360 Greg Stoutenburg: So…
115 00:18:05.360 ⇒ 00:18:05.790 Garrett: Yeah.
116 00:18:05.790 ⇒ 00:18:10.670 Greg Stoutenburg: How do you prioritize what you’re going to put in front of leadership and advocate for?
117 00:18:10.860 ⇒ 00:18:22.509 Garrett: Yeah, absolutely. So yeah, I agree, I think 8 is too many. I think, you know, getting 8 out of 5, and then maybe even, like, the top 3 is probably even better, because you want to go into detail, right, a little bit on that top 3.
118 00:18:22.670 ⇒ 00:18:36.849 Garrett: And I think just, you know, framing it in a way that summarizes, what each, you know, what each recommendation is about, and then just kind of, just that quick kind of bolded, you know, pros and cons, right?
119 00:18:36.910 ⇒ 00:18:56.709 Garrett: So that way it’s… you’re not kind of over… overwhelming the executive with information. I think that’s really important to… to keep in mind as well, is you want it quick, you want it, you know, concise and precise is, like, a term that I’ve heard before that I like. Yep, yep. And then just kind of, you know, straight to the point. So I think, you know, as you mentioned, just kind of dwindling it down, and then…
120 00:18:56.990 ⇒ 00:19:07.029 Garrett: Ensuring that, you have a lot more information in your back pocket that you can speak to, as opposed to what you’re presenting, I think is a way of looking at it. That’s good.
121 00:19:07.030 ⇒ 00:19:12.070 Greg Stoutenburg: Yeah, that’s good. That’s definitely… that’s the perspective of someone who’s been on some meetings.
122 00:19:12.070 ⇒ 00:19:12.740 Garrett: Yeah.
123 00:19:12.740 ⇒ 00:19:15.319 Greg Stoutenburg: Better not… if your entire brain is on the slide, you’re in trouble.
124 00:19:15.320 ⇒ 00:19:17.060 Garrett: Absolutely.
125 00:19:17.760 ⇒ 00:19:23.070 Garrett: happened to me before, early on in my career, I’m not saying it hasn’t happened, but I’ve gotten that feedback early on, too, correct.
126 00:19:23.070 ⇒ 00:19:29.229 Greg Stoutenburg: No, that’s good. I had a friend who, I went to graduate school for philosophy, and I had a…
127 00:19:29.230 ⇒ 00:19:29.770 Garrett: Okay, yeah.
128 00:19:29.770 ⇒ 00:19:35.049 Greg Stoutenburg: friend who had an experience where he was writing a paper on something not really in his area, and so that.
129 00:19:35.560 ⇒ 00:19:46.780 Greg Stoutenburg: he went to the conference, and he presented this paper, and then it’s the Q&A period, it’s like… it’s like, don’t ask me a question. Like, if I knew it, I would have said it in the last 20 minutes, like I said everything.
130 00:19:46.940 ⇒ 00:19:47.830 Garrett: Oh, man.
131 00:19:47.850 ⇒ 00:19:49.860 Greg Stoutenburg: That’s how he learned.
132 00:19:49.860 ⇒ 00:19:53.200 Garrett: We all need those lessons, right, sometimes. Yeah.
133 00:19:53.200 ⇒ 00:19:54.130 Greg Stoutenburg: Yeah, exactly.
134 00:19:54.620 ⇒ 00:20:05.869 Greg Stoutenburg: Yeah, well, I had a similar experience, and that’s what taught me… before cloud storage was a thing, I had an experience like that, where I learned, don’t rely on someone else’s computer when your entire presentation is a PowerPoint.
135 00:20:06.110 ⇒ 00:20:14.390 Garrett: True. Yeah. Group presentations where it’s just, like, one person using the computer, like, oh, I hope everything goes well, you know?
136 00:20:14.390 ⇒ 00:20:27.389 Greg Stoutenburg: Right. Oh, the computer’s not working? Well, I don’t know what to do for the… Exactly, right? Exactly. Yeah, very good. Okay, I think, you know, obviously, like, you have a lot of analytics shops.
137 00:20:27.390 ⇒ 00:20:28.220 Garrett: Yeah.
138 00:20:28.220 ⇒ 00:20:44.470 Greg Stoutenburg: about that, really? I guess, I guess in the last 10 minutes, what I’d like to talk about is just, like, tell me, like, like, paint a picture for me, like, give me more insight into your experience doing things like leading the project. Like, I want to hear about the project leader more than I want to hear about the analyst.
139 00:20:44.470 ⇒ 00:21:02.950 Greg Stoutenburg: Obviously, you’re an analyst, you know, so include it, but, like, you know, I’m less interested in, like, the slicing and dicing, and more in, like, the, here’s what the problem was, here’s what we knew, how we figured out what to do, here’s the approach we took, here’s the pieces of friction we ran into, you know, and then here’s how everyone, stakeholders made a billion dollars, like, you know.
140 00:21:02.950 ⇒ 00:21:03.300 Garrett: Absolutely.
141 00:21:03.470 ⇒ 00:21:06.040 Greg Stoutenburg: That… that, yeah, walk me through that kind of thing.
142 00:21:06.040 ⇒ 00:21:06.930 Garrett: Yeah, absolutely.
143 00:21:06.980 ⇒ 00:21:19.100 Garrett: So I guess I’ll just kind of start with Disney, because that’s just kind of the project that I recently wrapped up, but it was pretty much just me, and one other contractor who was, like, a system architect working on that one.
144 00:21:19.100 ⇒ 00:21:30.789 Garrett: And we’re just partnering with some FTs internally. They were on the data engineering and data science team. But essentially, you know, as soon as we came in there, we were kind of fed a bunch of, you know.
145 00:21:30.790 ⇒ 00:21:54.409 Garrett: documentation, you know, of the current state, and, you know, doing, doing the assessment, right? So that’s just me partnering with the architects, so we, you know, met together, we met with, the other FTs in a series of meetings. And together, essentially, we stood up our own, documentation of what, you know, the future state would look like. So that was the huge, requirements documentation.
146 00:21:54.410 ⇒ 00:22:17.549 Garrett: In the beginning, it was difficult, though. There was definitely a lot of back and forth and, you know, revisions, there was, you know, scope changes and things like that. But after a while, we did get it to the point where it was, like, you know, locked down, if you will. You know, which is obviously where we wanted it to get to. And then with that, we were also building a, a diagram
147 00:22:17.550 ⇒ 00:22:28.099 Garrett: system diagram and Miro, that would capture, the requirements documentation as well as the changes to the current state as well.
148 00:22:28.320 ⇒ 00:22:46.989 Garrett: So it was up to me to essentially ensure that we had, you know, all the requirements, documented. And then the system architect was more ensuring we had, you know, all the technical, you know, specs documented as well. So he and I were kind of, you know, partnering in tandem for a while there.
149 00:22:47.150 ⇒ 00:23:01.539 Garrett: And then, as soon as, probably a month or so on the project, we had two data engineers, onboarded as well. And then from there, I had my own kind of roadmap, you know, that I mentioned to you, that I had as a PowerPoint slide.
150 00:23:01.550 ⇒ 00:23:10.230 Garrett: But what I did after they joined the team is I… I broke that PowerPoint slide down into, like, a GitHub task checking sheet.
151 00:23:10.280 ⇒ 00:23:18.530 Garrett: So I just created, like, a bunch of, of GitHub tasks based on the requirements documentation that we had created initially.
152 00:23:18.640 ⇒ 00:23:38.179 Garrett: That way we had, like, a full backlog of work, tied to the requirements. So things like, you know, building the data, data warehouse in Databricks, for the audience store, they were calling it, which was broken down into, like, we had, like, a bronze layer, we had a silver layer, within that data house to kind of
153 00:23:38.580 ⇒ 00:23:49.479 Garrett: differentiate the different data pipelines that we had set up, as well as the data quality, right, to check when we promoted data from bronze layer to silver layer.
154 00:23:51.340 ⇒ 00:24:10.400 Garrett: And then, also the other various tasks where we would actually establish the connectivity to the other systems, like, in Snowflake or, BigQuery. So we actually had to, like, work with other teams internally, in Disney to, like, set up that… those connectivities, which was, like, a whole other series of, like, your ticket
155 00:24:10.400 ⇒ 00:24:21.409 Garrett: tracking. So I was keeping track of, like, the GitHub, tasks, like, internally within our team, but then also keeping track of, like, Jira tickets and things like that from other teams that we were kind of dependent on.
156 00:24:21.470 ⇒ 00:24:28.069 Garrett: So I fed, like, those dependent work items into, like, the overall big picture roadmap that I was managing.
157 00:24:28.390 ⇒ 00:24:44.140 Garrett: And so, like, once they joined the team, it was really from, like, a day-to-day management perspective there. It was, like, having daily stand-up meetings. It was, going over the Kanban board with the team, you know, going over, like, any blockers, anyone had.
158 00:24:44.350 ⇒ 00:24:49.480 Garrett: Like, personally, or just, like, with the, you know, the work in general.
159 00:24:49.860 ⇒ 00:25:00.870 Garrett: And then, working, meeting with other, stakeholders, like, other internal teams, so I also built the standard, operating procedures, documentation of how.
160 00:25:01.120 ⇒ 00:25:05.849 Garrett: Those teams would interact, like, with the new data warehouse, and how they would
161 00:25:05.950 ⇒ 00:25:10.039 Garrett: Write to the specific tables that we were creating for their teams.
162 00:25:10.320 ⇒ 00:25:11.800 Garrett: Within Databricks.
163 00:25:12.060 ⇒ 00:25:19.050 Garrett: So that way we had, just a good, understanding for them when they got onboarded, you know, to the system.
164 00:25:20.880 ⇒ 00:25:28.100 Garrett: So, yeah, that was kind of a lot of the detail work that I did, you know, kind of, like, shepherd the project along as well.
165 00:25:28.100 ⇒ 00:25:33.969 Greg Stoutenburg: Yeah, yeah. Yeah, maybe, so on my clock, I’ve got 6 minutes left.
166 00:25:33.970 ⇒ 00:25:34.570 Garrett: doors.
167 00:25:34.570 ⇒ 00:25:41.720 Greg Stoutenburg: Can you take two and tell me, like, okay, so you can manage projects as well, in varying sizes and scopes.
168 00:25:41.720 ⇒ 00:25:59.459 Greg Stoutenburg: What’s your experience working with, like, executive leadership? So, say you’re client-facing, and, you know, as a consultant, you’ve been client-facing, you’re sort of always client-facing, but, you know, you’re client-facing, and you’re, you know, it’s a weekly check-in, or it’s a monthly check-in, and you want to talk about the work that’s been done, but you’re also interested in getting new business.
169 00:25:59.460 ⇒ 00:26:10.629 Greg Stoutenburg: What’s, sort of, what’s the approach that you take toward looking for those new opportunities that you might be able to use to expand scope? And, hey, buddy! Hey!
170 00:26:11.830 ⇒ 00:26:13.389 Greg Stoutenburg: I don’t know if you could hear that at all.
171 00:26:13.390 ⇒ 00:26:14.260 Garrett: How are you?
172 00:26:14.260 ⇒ 00:26:19.000 Greg Stoutenburg: Because, like, he’ll have these, like, allergy fits where you scratching and, like, make noise.
173 00:26:19.000 ⇒ 00:26:34.340 Greg Stoutenburg: You know, looking for those opportunities to expand those scopes, and just, like, managing executive, especially executive, like, stakeholders generally, you know? Yeah. Especially if, you know, especially if they’re saying no, or that, you know, they don’t agree with your vision. Yeah, absolutely. What can you speak to there?
174 00:26:34.570 ⇒ 00:26:42.800 Garrett: Yeah, so, I guess I’ll talk a couple different ones. So, at Apple, so I had mentioned I was helping, like, the Director of Product Strategy for Media and Entertainment.
175 00:26:42.990 ⇒ 00:26:51.470 Garrett: I was also helping, like, engineering, managers as well, so they were using a retool platform, which was, like.
176 00:26:51.590 ⇒ 00:27:00.370 Garrett: It had, like, a bunch of, you know, applications within it. This was, kind of like an internal, application, you know, management system for, for,
177 00:27:00.540 ⇒ 00:27:03.240 Garrett: For Apple TV Plus and Apple Music.
178 00:27:03.990 ⇒ 00:27:09.410 Garrett: And essentially, they had, requirements to build,
179 00:27:09.480 ⇒ 00:27:21.489 Garrett: analysis or… or dashboards, based on those applications, and so they wanted to monitor… I remember, like, query… different query runs, like, these are just SQL queries and…
180 00:27:21.490 ⇒ 00:27:38.820 Garrett: and run kind of, like, the runtime of those queries, the usage, like, how long did it take to run them, things like that. So this was actually really useful insights to the developers of those teams, you know, within the different applications, so that way they could kind of double-click into
181 00:27:38.820 ⇒ 00:27:54.139 Garrett: the performance of their queries and, you know, just have more targeted help on how to fix them, and things like that. So that’s just, like, one example of, kind of a project that I just got involved in. Didn’t really ask to work on it, just kind of…
182 00:27:54.240 ⇒ 00:28:04.309 Garrett: you know, I was talking to, is that just another kind of coworker that, that I was working with, and he just said this would be a great, you know, kind of opportunity, and it would help a lot of the developers out, so, yeah.
183 00:28:04.310 ⇒ 00:28:04.940 Greg Stoutenburg: Yeah, cool.
184 00:28:04.940 ⇒ 00:28:14.289 Garrett: That was one example. Sorry, just one other one for the executives, yeah. When I was at Meta, I was, supporting one senior director and one VP,
185 00:28:14.420 ⇒ 00:28:17.440 Garrett: pretty regularly on a monthly basis, I would say.
186 00:28:17.550 ⇒ 00:28:31.419 Garrett: And this was producing, like, monthly business operations reporting for their organizations. And they wanted to see, within the different product lines, like, this is in reality labs, like, VR, you know, smart glasses, etc.
187 00:28:31.420 ⇒ 00:28:38.709 Garrett: Anything from, like, a cost perspective, so, like, OPEX, CapEx, headcount, kind of breakdown, within their organization.
188 00:28:38.750 ⇒ 00:28:47.359 Garrett: With including, like, the latest actuals for that month, like, versus forecast. We’re seeing, like, you know, headcount, trends,
189 00:28:47.360 ⇒ 00:28:58.769 Garrett: changes, you know, contractor, FTE, things like that. So I was asked to produce that, as well as, provide, quarterly reporting into the research investment portfolio.
190 00:28:58.830 ⇒ 00:29:03.210 Garrett: So, this was also split by the different product lines to show
191 00:29:03.230 ⇒ 00:29:19.739 Garrett: Like, where the investments were being made, and, how did those investments, improve to the overall business value of the products? Like, you know, did it reduce, like, the weight of the glasses? You know, how many projects do we have, you know, contributing to things like that, you know, etc. So…
192 00:29:19.930 ⇒ 00:29:30.910 Greg Stoutenburg: Yeah, yeah, yeah, great, yeah. So, yeah, I mean, I guess one thing to… one thing to ask, you know, sort of make sure you’re aware of is that one of the things that we’re looking for, like, one of the… one of the roles we’re trying to staff is sort of like a.
193 00:29:30.910 ⇒ 00:29:31.410 Garrett: Yeah.
194 00:29:31.410 ⇒ 00:29:41.250 Greg Stoutenburg: a data and analytics, specialist who also, like, leads… leads a client project. So, you know, we’ve got folks on the team who are.
195 00:29:41.280 ⇒ 00:30:02.620 Greg Stoutenburg: basically, you know, receiving work, where someone else is customer-facing, they’re sort of receiving work and, you know, moving projects along and sort of discussing internally, but don’t really have much or any client contact, and so they aren’t sort of, like, at that first line. One of the things that we are interested in is, you know, folks who would also want to take on that sort of additional
196 00:30:02.620 ⇒ 00:30:15.240 Greg Stoutenburg: Role, where they’re doing things like they’re being that client-facing person, they’re providing those updates, they’re listening to what the clients have to say about the work that’s been done, other work that they might want to look into, other problems that they might have.
197 00:30:15.240 ⇒ 00:30:22.520 Greg Stoutenburg: And sort of help, you know, create projects, from that. What’s… what’s your reaction when you hear that?
198 00:30:22.830 ⇒ 00:30:39.939 Garrett: Yeah, absolutely, no, I think that sounds exciting. Love to kind of, like, scope out projects and, yeah, like, formulate proposals, and then, yeah, kind of, package it up that way so that it’s, formulated into, like, a, yeah, SOW or, you know, however that’s done, kind of insurance.
199 00:30:39.940 ⇒ 00:30:44.720 Greg Stoutenburg: You’re comfortable being a little salesy then, you know? Yeah. Get in, get to know the client and stuff. Yeah.
200 00:30:44.720 ⇒ 00:30:45.260 Garrett: Absolutely.
201 00:30:45.630 ⇒ 00:30:47.250 Greg Stoutenburg: Cool. I know…
202 00:30:47.250 ⇒ 00:30:54.290 Garrett: I feel like I’m a good, probably, probably, like, I’m technical, but I’m… I feel like I’m good on the relationship side as well, so I feel like I… I kinda…
203 00:30:54.430 ⇒ 00:30:59.499 Greg Stoutenburg: It’s probably a bonus, also. Good, yeah, good, good, yeah. I mean, yeah, it is.
204 00:31:00.270 ⇒ 00:31:04.689 Greg Stoutenburg: It is. Yeah, the technology will change, but people liking each other won’t.
205 00:31:04.900 ⇒ 00:31:06.080 Garrett: Exactly, yeah.
206 00:31:06.080 ⇒ 00:31:13.300 Greg Stoutenburg: I talked so much that it’s now the end of our time. Oh, yeah. I’m okay going another 2 minutes if you are. Do you have a hard stop right now?
207 00:31:13.300 ⇒ 00:31:15.190 Garrett: I know, I don’t have a hard stop, yeah.
208 00:31:15.190 ⇒ 00:31:36.969 Greg Stoutenburg: Okay, yeah, we’ll just keep it quick. So, I… I mean, I’m not on the hiring team, I’m just, you know, I’m… I’m a person in one of those roles that I just described. So, that’s why this is sort of the more, a little bit more the nuts and bolts, portion of the interview. So, I just wanted to, granted that there’s a lot I don’t know, or I can’t really speak to, do you have any questions that you’d like to ask about bringing.
209 00:31:37.510 ⇒ 00:31:50.929 Greg Stoutenburg: process, or anything like that? Yeah, I have, let’s see, I think I have a couple written down, and I’ll just maybe just choose one here. Sure. In the interest of time. And if I don’t know, or if I do know and can’t say, I’ll just say that. Okay. But I’ll answer what I can.
210 00:31:50.930 ⇒ 00:32:02.360 Garrett: So I guess, like, just, like, first coming into this role, what’s, kind of, like, the main problems that, that I would solve, like, just coming in? Would I, like, own, like, a project kind of, like, end-to-end?
211 00:32:03.020 ⇒ 00:32:16.480 Greg Stoutenburg: Yeah, so there are… Brainforge has lots of different clients with lots of different kinds of projects that we’re working on. Lots are like things that you said, and some are different. So…
212 00:32:16.480 ⇒ 00:32:24.949 Greg Stoutenburg: my experience, and, you know, I’ve seen this play out for others who have been brought on. I mean, I only started working anything resembling full-time hours in January, so…
213 00:32:24.950 ⇒ 00:32:25.560 Garrett: Oh, okay.
214 00:32:25.560 ⇒ 00:32:27.270 Greg Stoutenburg: I’m new as well.
215 00:32:27.700 ⇒ 00:32:41.339 Greg Stoutenburg: Is that, you know, they’ll… they’ll listen to what you say about what you’re interested in, and what you want to do, and what your experience is like, and slot you in where you want to go. And they seem to do a pretty good job helping people ramp up.
216 00:32:41.340 ⇒ 00:32:56.259 Greg Stoutenburg: You know, there are a lot of systems and processes in place that you can rely on, tons of tasks that are automated using AI, slash commands and things, and they’re really… and they really are building it all out now in response to things like feedback. Like, there are…
217 00:32:56.260 ⇒ 00:32:56.580 Garrett: Awesome.
218 00:32:56.580 ⇒ 00:33:00.840 Greg Stoutenburg: There are now commands and, and,
219 00:33:00.890 ⇒ 00:33:09.349 Greg Stoutenburg: folders and repos that exist, because I said, hey, here, hey team, this is something that would help me speed me up, and then someone, like, went and made it. Yeah, cool.
220 00:33:09.350 ⇒ 00:33:22.519 Greg Stoutenburg: But yeah, so there’s… that’s to say, like, you know, it’s a responsive environment, there’s a lot that’s in flux, which is, you know, not everybody’s comfort zone, but, you know, the plus side of things being in flux is that they’re responsive, so…
221 00:33:22.520 ⇒ 00:33:22.960 Garrett: Right.
222 00:33:24.170 ⇒ 00:33:32.040 Greg Stoutenburg: Yeah, so I think that’s just something that would be sorted out. To directly answer your question, like, where would I go? that is something that the team would sort out.
223 00:33:32.280 ⇒ 00:33:33.959 Garrett: Awesome. Cool. Yeah.
224 00:33:33.960 ⇒ 00:33:34.510 Greg Stoutenburg: No.
225 00:33:34.710 ⇒ 00:33:39.579 Garrett: Awesome, yeah, I think that was kind of the main thing, I had,
226 00:33:39.750 ⇒ 00:33:54.659 Garrett: And the program governance structure, is that something that, like, I would own, like, in terms of, you know, delivery promise? Because I think, like, each client, they’re expecting, like, it’s more, like, short-term projects, right? Like, speed to delivery, kind of?
227 00:33:55.210 ⇒ 00:33:56.600 Garrett: Is that understanding?
228 00:33:56.600 ⇒ 00:33:59.170 Greg Stoutenburg: Sometimes, I mean, sometimes it’s like, we’re gonna take…
229 00:33:59.170 ⇒ 00:34:00.460 Garrett: Longer, kinda, yeah.
230 00:34:00.460 ⇒ 00:34:05.129 Greg Stoutenburg: Yeah, yeah, and it’s gonna take, you know, several months to, you know, build some…
231 00:34:05.130 ⇒ 00:34:05.560 Garrett: Yeah.
232 00:34:05.560 ⇒ 00:34:22.119 Greg Stoutenburg: build a database, like, just absolutely from the ground up, and sometimes it’s a quick sprint where it’s like, you know, you mentioned Tableau, like, I just ran a project that was taking one of our clients, they were sick of Tableau, we were like, here’s Omni. Cool. In two weeks, all your stuff is gonna be in Omni.
233 00:34:22.120 ⇒ 00:34:23.129 Garrett: Okay, awesome, yeah.
234 00:34:23.139 ⇒ 00:34:26.299 Greg Stoutenburg: So, and then it’s just, like, done, you know, and then move on to the next thing, so…
235 00:34:26.300 ⇒ 00:34:32.600 Garrett: You guys don’t have any kind of, like, standard, like, range, it just kind of, like, depends on the client needs right now?
236 00:34:32.989 ⇒ 00:34:42.889 Greg Stoutenburg: It depends on what the client needs are, and now, there are… there, like, are docs for things like best practices around writing a statement of work,
237 00:34:42.889 ⇒ 00:34:43.439 Garrett: Yeah.
238 00:34:43.440 ⇒ 00:34:47.170 Greg Stoutenburg: There are docs and SOPs, yeah, like, they exist, but there isn’t…
239 00:34:47.870 ⇒ 00:35:12.850 Greg Stoutenburg: there will be some services, and we’re in this right now, there are some services where we’re gonna, make it kind of like, you know, pick out of a menu. So if a customer wants, for example, a product analytics sprint, in two weeks, we’ll promise them that, we will, like, audit your product, audit any analytics tool that you have right now, that’s a product analytics tool, come up with an event tracking plan, and then give you guidance on implementing
240 00:35:12.850 ⇒ 00:35:14.690 Greg Stoutenburg: it, but that’s all…
241 00:35:14.690 ⇒ 00:35:16.820 Garrett: features and stories, or something like that, right?
242 00:35:16.820 ⇒ 00:35:29.169 Greg Stoutenburg: Yeah, yeah, right, it’ll be like, yeah, and then internally, exactly, internally, you know, we feed in that SOW, and we go, you know, we issue the command for creating a bunch of linear tickets, and now we’ve got a new project that’s got the.
243 00:35:29.170 ⇒ 00:35:29.740 Garrett: Yeah.
244 00:35:29.740 ⇒ 00:35:32.410 Greg Stoutenburg: You know, 20 tickets, or whatever.
245 00:35:32.410 ⇒ 00:35:34.469 Garrett: Yeah, really nice workflow.
246 00:35:34.470 ⇒ 00:35:39.869 Greg Stoutenburg: Yeah, yeah, yeah, and all that stuff, that stuff exists already, but it’s also, like, continuing to be developed, yeah.
247 00:35:40.440 ⇒ 00:35:41.340 Greg Stoutenburg: Yep, yep.
248 00:35:41.340 ⇒ 00:35:41.930 Garrett: Awesome.
249 00:35:42.180 ⇒ 00:35:42.550 Greg Stoutenburg: Yo.
250 00:35:42.550 ⇒ 00:35:49.519 Garrett: Yeah, no, I mean, it sounds like a great opportunity. Yeah, definitely aligned, I think, with where my career’s been, like, the last 10 years. Cool.
251 00:35:49.520 ⇒ 00:35:56.850 Greg Stoutenburg: Alright, great. Cool. Well, if there’s no other questions then, I’ll let you go. Thanks for this very much. Yeah, thanks so much, guys.
252 00:35:56.850 ⇒ 00:35:57.940 Garrett: Chris, great meeting you.
253 00:35:57.940 ⇒ 00:36:02.839 Greg Stoutenburg: Yeah, nice to meet you as well. I’ll just… I’ll share my feedback with the team, and then, someone will reach out.
254 00:36:02.840 ⇒ 00:36:05.139 Garrett: Alright, thanks so much. Have a great rest of your day.
255 00:36:05.140 ⇒ 00:36:06.049 Greg Stoutenburg: See you soon. Bye.