Meeting Title: Brainforge Data Engineer Interview Chat Date: 2026-03-27 Meeting participants: Christina Knudson, Kaela Gallagher
WEBVTT
1 00:00:33.470 ⇒ 00:00:35.580 Kaela Gallagher: Hey, Christina, good morning.
2 00:00:36.020 ⇒ 00:00:37.100 Christina Knudson: Good morning.
3 00:00:37.390 ⇒ 00:00:38.219 Kaela Gallagher: How’s it going?
4 00:00:39.310 ⇒ 00:00:40.330 Christina Knudson: Thank you, how are y’all?
5 00:00:40.330 ⇒ 00:00:49.770 Kaela Gallagher: I’m doing well. Thanks for taking some time to chat with me today. I think I was in touch with Sarah, and then she mentioned, like.
6 00:00:49.770 ⇒ 00:01:01.010 Kaela Gallagher: I know somebody that might be a great fit for the roles that you have open. So, that’s why I reached out, and yeah, just excited to, like, learn a little bit more about you.
7 00:01:01.180 ⇒ 00:01:05.629 Kaela Gallagher: I guess just, like, starting off, curious why you’re looking for something new.
8 00:01:06.510 ⇒ 00:01:15.600 Christina Knudson: Yeah, so I’ve been at my current company for about three and a half years, and in that time, like, I have learned a lot.
9 00:01:15.600 ⇒ 00:01:27.600 Christina Knudson: But now, like, kind of my rate of learning new things has kind of slowed down a lot. Like, I’ve learned a lot of what we are already working on, and there’s just not a lot of need to, like.
10 00:01:27.600 ⇒ 00:01:32.720 Christina Knudson: Or time, really, to, like, learn new tools, or, like,
11 00:01:32.820 ⇒ 00:01:46.840 Christina Knudson: you know, make big changes, things like that. They really want, like, stability, without, you know, any of the risk of trying new things, really. And then also, like, when I joined three and a half years ago, then…
12 00:01:46.990 ⇒ 00:01:54.370 Christina Knudson: they made a new level for me, because they just had, like, data scientist and senior data scientist. So they added a…
13 00:01:54.610 ⇒ 00:02:02.320 Christina Knudson: level of lead for me. But it’s a really small company, and so… I don’t think they’re…
14 00:02:02.550 ⇒ 00:02:17.180 Christina Knudson: really is, like, a need to make another level higher than that. So there’s not, like, really an individual contributor path for growth. And then also, it’s a pretty, like, small, very flat organization, so there’s not really a need for, like.
15 00:02:17.260 ⇒ 00:02:29.420 Christina Knudson: you know, a data science manager either. So kind of between the not a lot of room for growth with my skills, and then not a lot of, like, promotion growth, then…
16 00:02:30.240 ⇒ 00:02:33.270 Christina Knudson: Yeah, those are, like, kind of the two things that are…
17 00:02:33.950 ⇒ 00:02:34.400 Kaela Gallagher: Yeah.
18 00:02:34.400 ⇒ 00:02:36.019 Christina Knudson: It’s driving me to look for something else.
19 00:02:36.230 ⇒ 00:02:42.659 Kaela Gallagher: Okay, that makes a lot of sense. And you’re currently with… it’s like an energy organization?
20 00:02:42.880 ⇒ 00:02:45.140 Christina Knudson: Yeah, that’s Exactly.
21 00:02:45.370 ⇒ 00:02:52.640 Kaela Gallagher: Okay, what, I guess, like, what kind of products or services do you guys provide, and what is your role in those?
22 00:02:53.330 ⇒ 00:02:56.239 Christina Knudson: Yeah, so what the company does is it, like.
23 00:02:56.380 ⇒ 00:03:11.890 Christina Knudson: buys and also, like, generates energy from, like, solar plants, and then sells it to the customer. So that’s, like, what it’s doing. And then what I do there, I’ve worked, like, on a lot of different projects over the years, but a lot of things…
24 00:03:11.890 ⇒ 00:03:27.650 Christina Knudson: Relating to, like, pricing and growth initiatives, a lot with, like, the marketing team to help them understand, like, how to allocate their marketing dollars, like, with, like, marketing mix and different channels and campaigns. And then…
25 00:03:27.880 ⇒ 00:03:45.919 Christina Knudson: Also, with the website, I helped launch a, pricing AI so that we can, like, maximize the customer lifetime value, for new customer acquisitions. So with that, we were able to, like, both lift the conversion rate and the…
26 00:03:45.920 ⇒ 00:03:51.459 Christina Knudson: Like, average customer lifetime value. So, that was pretty exciting to see, like, a…
27 00:03:51.800 ⇒ 00:03:59.479 Christina Knudson: really big growth in, like, aggregate customer lifetime value is, like, more than doubled 150% growth in…
28 00:03:59.810 ⇒ 00:04:02.879 Christina Knudson: Customer lifetime value. Yeah, so that was really fun.
29 00:04:03.120 ⇒ 00:04:12.149 Kaela Gallagher: Okay, okay, cool, yeah, it seems like you’ve had a wide variety of projects, but then, like, a lot of impact on what you’ve worked on, so that’s awesome.
30 00:04:12.380 ⇒ 00:04:15.070 Kaela Gallagher: And then where… where are you currently based?
31 00:04:15.610 ⇒ 00:04:16.589 Christina Knudson: Minneapolis.
32 00:04:16.820 ⇒ 00:04:24.679 Kaela Gallagher: Okay, okay, got it. Are you open to, like, relocation, or, do you think you’ll be in, like, Minneapolis long-term?
33 00:04:25.240 ⇒ 00:04:27.000 Christina Knudson: Planning to stick around here.
34 00:04:27.190 ⇒ 00:04:39.680 Kaela Gallagher: Okay, okay, no worries. We are currently, like, fully remote. We actually have team, like, globally, although we’re mostly working, like, Central and Eastern hours, in the U.S.
35 00:04:40.220 ⇒ 00:04:58.409 Kaela Gallagher: That being said, like, we have our CEO in Austin, as well as a couple other team members there, and then there’s also, like, 5 of us in LA, including myself, so we’re starting to build, like, a little bit of a presence in those locations, so definitely, like, a bonus, I would say, if people are located there or open to moving, but…
36 00:04:58.410 ⇒ 00:05:02.479 Kaela Gallagher: no worries, to be fully remote as well. That’s how… how we are right now.
37 00:05:02.480 ⇒ 00:05:03.620 Kaela Gallagher: Cool.
38 00:05:03.810 ⇒ 00:05:10.850 Kaela Gallagher: Cool. And then compensation-wise, like, what’s the range, you’d be targeting in order to make a move?
39 00:05:11.950 ⇒ 00:05:15.180 Christina Knudson: It kind of depends a lot…
40 00:05:15.590 ⇒ 00:05:23.139 Christina Knudson: on a lot of things. Like, I’m pretty well compensated right now at my current role, but then,
41 00:05:23.700 ⇒ 00:05:24.530 Christina Knudson: like…
42 00:05:25.140 ⇒ 00:05:34.730 Christina Knudson: I don’t know. Pay is, like, one piece of the whole finding a good job, and so it’s like, I wouldn’t want to stay in a job where it’s…
43 00:05:34.990 ⇒ 00:05:40.929 Christina Knudson: you know… kind of a dead end, just because I’m like, Really well paid, so…
44 00:05:40.930 ⇒ 00:05:41.320 Kaela Gallagher: Yeah.
45 00:05:41.460 ⇒ 00:05:43.930 Christina Knudson: Kinda… depends on a lot of things.
46 00:05:44.310 ⇒ 00:05:48.399 Kaela Gallagher: Okay, okay. What’s kind of the range that you’d be looking at?
47 00:05:49.560 ⇒ 00:05:54.529 Christina Knudson: Can we talk, like, maybe about the…
48 00:05:54.690 ⇒ 00:06:00.959 Christina Knudson: ranges for the roles that you’re hiring for. So, like, based on, like, what
49 00:06:01.690 ⇒ 00:06:07.009 Christina Knudson: you know about me so far, does it seem like one of the roles is, like, sounding like
50 00:06:07.180 ⇒ 00:06:09.170 Christina Knudson: A better fit than the other ones?
51 00:06:09.590 ⇒ 00:06:22.309 Kaela Gallagher: Yeah, so, to give you a little bit of context onto our open positions right now, the two that I think would be most of interest to you is a data engineering role and then an analytics engineering role.
52 00:06:22.310 ⇒ 00:06:38.570 Kaela Gallagher: I know you also mentioned marketing projects, and we actually have somebody on our team right now that specializes in MarTech who would be, like, looking for a data engineering partner to kind of be in that service line with him, so,
53 00:06:38.720 ⇒ 00:06:58.039 Kaela Gallagher: I could see, like, that as kind of a scope for you as well. But yeah, in terms of our, like, data engineering and analytics engineering roles, they’re fairly similar. They both fall within our data team, but then obviously the analytics engineer is going to have a little bit more focus on, you know, like.
54 00:06:58.240 ⇒ 00:07:01.150 Kaela Gallagher: BI and reporting, whereas the.
55 00:07:01.150 ⇒ 00:07:01.650 Christina Knudson: data.
56 00:07:01.650 ⇒ 00:07:11.379 Kaela Gallagher: is more traditional, like, Snowflake dbt, data engineering. So, curious, like, if either of those kind of stand out to you.
57 00:07:12.000 ⇒ 00:07:17.010 Christina Knudson: Yeah, the data engineering one sounds definitely, like, really interesting to me.
58 00:07:17.320 ⇒ 00:07:35.920 Kaela Gallagher: Okay, okay, cool. Yeah, in terms of, like, the pay range here, it definitely depends on, like, leveling and then, location as well, but I would say we’re targeting, probably, like, 140 to 180 for that role. Curious if that’s, like, even…
59 00:07:35.980 ⇒ 00:07:38.210 Kaela Gallagher: Similar to where you’re… you’re at.
60 00:07:38.860 ⇒ 00:07:49.729 Christina Knudson: Yeah, it’s a little bit lower than where I’m at, but the, like, 180 is closer to where I currently am, so, like, if…
61 00:07:49.940 ⇒ 00:07:53.230 Christina Knudson: Or on the, like, upper end of that, then that would probably work.
62 00:07:53.730 ⇒ 00:08:05.679 Kaela Gallagher: Okay, okay. Just for some context, too, into how we’re currently hiring, we have the entire team right now on, like, a 1099 basis, so contracting,
63 00:08:05.740 ⇒ 00:08:21.539 Kaela Gallagher: We do have plans in the future to convert people to W-2 and be able to start to offer benefits, but right now, like, our team is kind of at, flat, flat rates. So did want to give that context as well.
64 00:08:21.740 ⇒ 00:08:26.439 Kaela Gallagher: I guess with that being said, like, would you need more than 180 if it was, like, a flat?
65 00:08:27.500 ⇒ 00:08:33.720 Christina Knudson: So, like, the 140 to 180 is assuming that the person would be on the, like, 1099 basis? Just checking.
66 00:08:33.720 ⇒ 00:08:34.890 Kaela Gallagher: Yes, yes.
67 00:08:34.890 ⇒ 00:08:46.490 Christina Knudson: Okay, yeah, probably, but… We can kinda…
68 00:08:47.650 ⇒ 00:08:53.930 Christina Knudson: cross that bridge when we come to it. Like, definitely want to, like, learn more about the role, and then, like.
69 00:08:54.150 ⇒ 00:08:58.599 Christina Knudson: See, you know, what the whole situation looks like.
70 00:08:58.850 ⇒ 00:09:18.190 Kaela Gallagher: Okay, that makes sense. We’re also, you know, trying to put together, like, bonus structures, and we offer bonuses in different ways, whether that’s, like, referral bonuses, or, bonuses for earning certifications, or, different things like that. We have opportunities to,
71 00:09:18.440 ⇒ 00:09:37.670 Kaela Gallagher: acquire more earnings as well. So, yeah, we can discuss that more, in detail later. Yeah, just to give, like, a little bit more context, to the kinds of, like, projects that we work on. So, we’re a data and AI consulting company, and we work with a variety of clients, mostly, like, small and mid-sized organizations.
72 00:09:37.670 ⇒ 00:09:51.590 Kaela Gallagher: We have quite a few, like, CPG, clients that are, like, sold in Target, Walmart, like, you would recognize the brand names. We have, like, a healthcare client that we’ve been working with for over a year now.
73 00:09:51.630 ⇒ 00:09:54.929 Kaela Gallagher: like, some kind of B2B SaaS,
74 00:09:54.930 ⇒ 00:10:14.099 Kaela Gallagher: organizations as well, so, like, a wide variety of projects. You would likely be assigned to probably, like, 2 or 3 at a time, so, it’s definitely important to us that people can contact Switch pretty easily. And then we do bring our engineers onto calls with clients as well, so the communication aspect is important to us, too.
75 00:10:15.520 ⇒ 00:10:32.420 Kaela Gallagher: And then I mentioned, like, dbt, Snowflake, on, like, the BI side, we have been using a tool called Omni, so those… that’s just, like, an example of some of what we’re using, although, like, if our clients, you know, already
76 00:10:32.650 ⇒ 00:10:39.449 Kaela Gallagher: have something in place, we are able to kind of, you know, switch to that and support them in that as well, so…
77 00:10:39.730 ⇒ 00:10:43.139 Kaela Gallagher: That’s kind of an overview there. Curious if there’s, like.
78 00:10:43.270 ⇒ 00:10:45.560 Kaela Gallagher: Any questions I can help answer?
79 00:10:46.840 ⇒ 00:10:49.980 Christina Knudson: Like…
80 00:10:50.450 ⇒ 00:10:59.270 Christina Knudson: Okay, so it sounds like communication is pretty important, since you have, like, engineers on the calls, that sounds great, I’ve worked a lot with, like.
81 00:10:59.610 ⇒ 00:11:10.529 Christina Knudson: business stakeholders, and then, like, I did statistical consulting on the side for many years, so that sounds great. What else would you say is, like.
82 00:11:12.050 ⇒ 00:11:18.080 Christina Knudson: Some, like, characteristics that you’re hoping to see in the data engineer that you hire?
83 00:11:19.160 ⇒ 00:11:35.929 Kaela Gallagher: Yeah, I think, like, being able to… kind of building off, like, the communication piece, like, being able to talk about really technical concepts in a way that, like, makes sense is super important to us. Our interview process is structured to where, like, we’re not doing any
84 00:11:35.930 ⇒ 00:11:43.429 Kaela Gallagher: live coding. Like, we have a take-home technical, you know, assessment, but, like, our technical interview round is…
85 00:11:44.000 ⇒ 00:11:50.649 Kaela Gallagher: talking about really technical concepts, like, in depth. So that’s super important to us, for sure.
86 00:11:50.870 ⇒ 00:11:56.899 Kaela Gallagher: And then… I’m trying to think, like, what other characteristics I would…
87 00:11:57.060 ⇒ 00:12:03.509 Kaela Gallagher: call out. I think that’s really, like, the main thing outside of, you know, obviously having, like, the technical expertise.
88 00:12:04.140 ⇒ 00:12:12.990 Christina Knudson: Okay, yeah, that sounds great. I mean, I used to be a, like, math and statistics professor, and I have made, like, a couple hundred YouTube videos that I…
89 00:12:13.760 ⇒ 00:12:18.390 Christina Knudson: online, and they have, like, 2.5 million views, so I’d say, like.
90 00:12:18.570 ⇒ 00:12:29.540 Christina Knudson: definitely being able to explain really technical things in a way that makes sense without, like, making them dumb or, like, inaccurate. That’s, like, my…
91 00:12:29.690 ⇒ 00:12:32.700 Christina Knudson: Kinda… that’s, like, my thing, and…
92 00:12:33.000 ⇒ 00:12:38.730 Christina Knudson: A lot of people tell me, like, yeah, you can explain anything without making me feel dumb, so…
93 00:12:39.110 ⇒ 00:12:40.030 Christina Knudson: Yeah, that’s.
94 00:12:40.030 ⇒ 00:12:40.710 Kaela Gallagher: Okay.
95 00:12:41.210 ⇒ 00:12:47.130 Kaela Gallagher: Cool! I would love to, like, if you can maybe after this, like, send me a link to your…
96 00:12:47.130 ⇒ 00:12:47.640 Christina Knudson: Yeah.
97 00:12:47.640 ⇒ 00:12:48.129 Kaela Gallagher: Like, would love.
98 00:12:48.130 ⇒ 00:12:49.050 Christina Knudson: For sure.
99 00:12:49.050 ⇒ 00:12:51.370 Kaela Gallagher: have that on file for you as well, that’s really cool.
100 00:12:52.040 ⇒ 00:12:53.199 Christina Knudson: Yeah, I will do that.
101 00:12:53.320 ⇒ 00:12:59.360 Kaela Gallagher: Okay, awesome. Any, like, any other questions I can help answer?
102 00:12:59.930 ⇒ 00:13:13.620 Christina Knudson: Like, could you tell me about… more about the, like, what the interview process looks like? So you said there’s not, like, a coding, thing, but more of an explaining, but, like, can you tell me more about, like, those steps?
103 00:13:13.870 ⇒ 00:13:22.569 Kaela Gallagher: Yes, so the first round is, kind of, you know, cultural, like, about your experience, a few technical questions.
104 00:13:22.570 ⇒ 00:13:35.060 Kaela Gallagher: The second round really, like, dives deeper into the technical side, but no coding, like I mentioned. And then the final round is, like a take-home challenge that we’ll give you, like, we’ll send you kind of a GitHub link that has
105 00:13:35.080 ⇒ 00:13:53.749 Kaela Gallagher: all the directions, and you come to the final panel with your solution, like, send your code over ahead of time and kind of walk through that. So, typically, like, those three rounds we could get done in a couple weeks, just kind of up to you and, like, when you schedule through the booking links.
106 00:13:54.160 ⇒ 00:13:56.089 Christina Knudson: Okay, sounds good.
107 00:13:56.330 ⇒ 00:14:04.120 Kaela Gallagher: Yeah. Yeah, and after this, like, I can send you the link for the first round as well, so we can go ahead and get started.
108 00:14:04.470 ⇒ 00:14:09.089 Christina Knudson: Okay, cool, that sounds good. Let’s see…
109 00:14:09.420 ⇒ 00:14:12.509 Christina Knudson: Can you tell me, like, what the team is like now?
110 00:14:12.750 ⇒ 00:14:20.350 Kaela Gallagher: Yes, so we are about… I think we’re between 25 and 30 people right now,
111 00:14:20.480 ⇒ 00:14:36.210 Kaela Gallagher: There’s some of us that are, like, more internal, like me, for example, I’m people in recruiting, we have a sales team, but I would say most of our company falls within our delivery team, which is client-facing, and that’s split into 3 different service lines that we provide our clients.
112 00:14:36.210 ⇒ 00:14:45.040 Kaela Gallagher: So, one is data, that includes our data and analytics engineers, one is AI, and one is strategy and analytics.
113 00:14:45.040 ⇒ 00:14:57.130 Kaela Gallagher: And so, we’re able to offer solutions through, you know, those three service lines, and oftentimes with our clients, you know, we’re providing them with, like, multiple services at once.
114 00:14:57.710 ⇒ 00:15:12.099 Kaela Gallagher: Our, like, average length of engagement is a few months, but we do have clients that we’ve been working with for, like, over a year now, and we almost serve as, like, an in-house data team to them, so, just kind of depends.
115 00:15:13.340 ⇒ 00:15:19.480 Christina Knudson: Okay, cool. So then, like, when you… I’m just kinda… trying to understand this, like.
116 00:15:20.440 ⇒ 00:15:30.949 Christina Knudson: for the clients who are just clients for a few months, then, like, what does that, like, handoff process look like? Do they then get…
117 00:15:31.620 ⇒ 00:15:36.539 Christina Knudson: Like, do they already have their own data people? And then we’re just, like, helping
118 00:15:37.060 ⇒ 00:15:39.119 Christina Knudson: To, like, move things along more quickly?
119 00:15:39.340 ⇒ 00:15:53.219 Kaela Gallagher: Yes, so it depends a lot on, like, the project, the SOW that we outline. We typically have, like, specific deliverables that we need to provide to them by a certain time, but it could be, like.
120 00:15:53.520 ⇒ 00:16:08.089 Kaela Gallagher: a data warehouse transition project or something, and we come in to, like, you know, do that transition for them, or something, like, a little bit more short-term, where we’re, like, supplementing, maybe a data team that’s, like, already in place.
121 00:16:08.490 ⇒ 00:16:09.729 Christina Knudson: Got it.
122 00:16:12.030 ⇒ 00:16:14.100 Christina Knudson: Cool, I’m just taking some notes.
123 00:16:14.270 ⇒ 00:16:15.310 Kaela Gallagher: Yeah, no worries.
124 00:16:15.970 ⇒ 00:16:16.730 Christina Knudson: Okay.
125 00:16:16.830 ⇒ 00:16:20.180 Christina Knudson: What do you think is, like…
126 00:16:20.450 ⇒ 00:16:27.259 Christina Knudson: Cool about the, like, team or company? Like, what are reasons to, like, get excited about this?
127 00:16:27.660 ⇒ 00:16:40.620 Kaela Gallagher: Yeah, I mean, I think we’re… we are doing, like, really cool, kind of, like, cutting-edge projects. Like, I think just being in the data and AI space right now is super interesting. And…
128 00:16:40.940 ⇒ 00:16:54.729 Kaela Gallagher: One thing that I think is exciting is, like, we’re not just implementing these things for our clients, we actually have, like, a whole internal platform and, like, products built out that we’re using for ourselves, like.
129 00:16:54.920 ⇒ 00:17:08.760 Kaela Gallagher: for example, like, all of our calls are recorded, and we can go access transcripts and, you know, have, like, AI briefings of them and whatnot. Like, we have, like, really cool internal products as well that we’re using.
130 00:17:08.760 ⇒ 00:17:16.920 Kaela Gallagher: I also think just, like, being a smaller organization, there’s a lot of autonomy for each person, and you can almost…
131 00:17:16.990 ⇒ 00:17:25.000 Kaela Gallagher: I don’t want to say, like, build your own journey, but if you, like, express interest in something, we’re very, like.
132 00:17:25.160 ⇒ 00:17:36.680 Kaela Gallagher: open to supporting that. For example, we have an AI engineer right now who expressed interest in sales, and is, like, joining calls with our sales team, and having somebody technical there has actually, like.
133 00:17:36.900 ⇒ 00:17:45.329 Kaela Gallagher: served us really well. Like, it’s been a really cool partnership. So, I think, like, if you express something like that, we’re super open to supporting that as well.
134 00:17:45.330 ⇒ 00:17:45.910 Christina Knudson: Okay.
135 00:17:46.180 ⇒ 00:17:47.640 Christina Knudson: Cool. Nice.
136 00:17:48.000 ⇒ 00:17:50.260 Christina Knudson: Yeah. Yeah.
137 00:17:50.610 ⇒ 00:17:59.819 Christina Knudson: So, like, can I ask a question about those, like, three service lines again? You said there’s, like, data, AI, and then the third one is strategy and analytics. So, like…
138 00:18:00.320 ⇒ 00:18:01.470 Christina Knudson: those have?
139 00:18:01.610 ⇒ 00:18:03.169 Christina Knudson: a lot of…
140 00:18:03.650 ⇒ 00:18:10.099 Christina Knudson: overlap, like, if I think about the, like, Venn diagram. So, how do you, like, kind of…
141 00:18:10.440 ⇒ 00:18:13.630 Christina Knudson: Like, define each one, or, like, distinguish them.
142 00:18:14.950 ⇒ 00:18:25.559 Kaela Gallagher: Yeah. So, each of our, like, each person on our delivery team is going to fall into one of those service lines, and we have a role called, like.
143 00:18:25.840 ⇒ 00:18:27.090 Kaela Gallagher: SL?
144 00:18:27.240 ⇒ 00:18:30.139 Kaela Gallagher: Service Leader that helps…
145 00:18:30.150 ⇒ 00:18:43.590 Kaela Gallagher: like, lead each of those branches, maybe provides, like, development resources to the team. So that’s, like, internally how we support those, but then, obviously, like, being on a client, oftentimes, like.
146 00:18:43.590 ⇒ 00:18:50.580 Kaela Gallagher: We might have somebody from each service line kind of supporting that client, depending on the project.
147 00:18:51.000 ⇒ 00:18:51.630 Christina Knudson: Okay.
148 00:18:54.840 ⇒ 00:18:55.860 Christina Knudson: So, like…
149 00:19:00.250 ⇒ 00:19:08.200 Christina Knudson: When it’s, like, strategy analytics, is that, like… or strategy and analytics? Then… because, like, analytics…
150 00:19:08.450 ⇒ 00:19:13.720 Christina Knudson: And then data and AI, are kind of… some…
151 00:19:14.000 ⇒ 00:19:19.209 Christina Knudson: places, like, practically use those interchangeably. So, like.
152 00:19:19.620 ⇒ 00:19:23.619 Christina Knudson: What does the, like… maybe, like, what does each one work on?
153 00:19:23.740 ⇒ 00:19:27.769 Christina Knudson: To, like, help me understand, like, how these differ from each other.
154 00:19:27.940 ⇒ 00:19:36.400 Kaela Gallagher: Yeah, so our strategy and analytics team has two roles on it. One is something we call a CSO, client, success.
155 00:19:36.920 ⇒ 00:19:54.320 Kaela Gallagher: owner, sorry, I’m blanking, who is, like, the main representative and, like, account manager to the client. Like, represents Brainforge to the client, ensures client satisfaction, and ensures that the work streams are, like, being delivered on time.
156 00:19:54.500 ⇒ 00:20:13.150 Kaela Gallagher: The other role on our strategy and analytics team would be, like, a data analyst or, like, a product analyst. So maybe supporting the project with more of, like, that hands-on analytics work. So, a lot of our projects are going to have at least, like.
157 00:20:13.250 ⇒ 00:20:30.409 Kaela Gallagher: at minimum, a CSO from the strategy and analytics team on them, like, each of our clients is going to be in touch with the CSO, and then oftentimes, we also have one of our analysts on the client as well. So, our strategy and analytics team is involved in each one of our clients.
158 00:20:30.790 ⇒ 00:20:39.419 Kaela Gallagher: Then if, you know, we’re doing data work, we’re gonna have people from the data team. If we’re doing AI work, we’re gonna have people from the AI team also supporting those clients.
159 00:20:39.950 ⇒ 00:20:41.729 Christina Knudson: Okay, that makes sense now. Thanks.
160 00:20:42.290 ⇒ 00:20:43.120 Kaela Gallagher: Cool.
161 00:20:44.100 ⇒ 00:20:44.850 Christina Knudson: Okay.
162 00:20:45.440 ⇒ 00:20:55.010 Christina Knudson: Cool, cool. Yeah, what else do you think would be useful for me to, like, know about…
163 00:20:59.160 ⇒ 00:21:10.259 Kaela Gallagher: I’m trying to think, I think I’ve given you kind of, like, the main overview so far, in terms of, like, heading into the first interview. I know that…
164 00:21:11.010 ⇒ 00:21:26.240 Kaela Gallagher: the first interviewer away, she usually asks about, like, Snowflake and DBT, so I would be ready there. But other than that, like, I think your communication is super strong, so I’m sure you’ll do great.
165 00:21:26.690 ⇒ 00:21:28.130 Christina Knudson: Okay, cool.
166 00:21:28.520 ⇒ 00:21:40.189 Christina Knudson: Alright, so I’ll send you the link to my YouTube channel, and then you said you’re gonna send a, like, scheduling link for the first round,
167 00:21:41.140 ⇒ 00:21:46.850 Christina Knudson: Anything else that I should be sending you, or like… I should be thinking about.
168 00:21:48.370 ⇒ 00:21:58.769 Kaela Gallagher: I actually don’t have a resume for you, so if you’d be willing to send that as well. I have your LinkedIn profile, but, a resume can’t hurt.
169 00:21:59.560 ⇒ 00:22:00.469 Christina Knudson: Okay. Okay.
170 00:22:00.670 ⇒ 00:22:01.140 Kaela Gallagher: Cool!
171 00:22:01.140 ⇒ 00:22:01.590 Christina Knudson: Sounds good.
172 00:22:02.000 ⇒ 00:22:09.969 Kaela Gallagher: I’ll get this sent over, by probably lunchtime today, and then, yeah, let me know if you have any questions at all throughout the process.
173 00:22:10.180 ⇒ 00:22:11.139 Christina Knudson: Okay, sounds good.
174 00:22:11.140 ⇒ 00:22:12.060 Kaela Gallagher: Okay, thank you.
175 00:22:12.060 ⇒ 00:22:13.049 Christina Knudson: Thanks for explaining everything.
176 00:22:13.050 ⇒ 00:22:15.660 Kaela Gallagher: Christina? Yeah, of course. Talk to you later.
177 00:22:15.990 ⇒ 00:22:16.620 Christina Knudson: Aye.
178 00:22:16.620 ⇒ 00:22:17.790 Kaela Gallagher: Okay, bye.