Meeting Title: Brainforge Recruitment Chat with Mateo Date: 2026-03-13 Meeting participants: Kaela Gallagher, Mateo
WEBVTT
1 00:01:05.060 ⇒ 00:01:07.310 Kaela Gallagher: Hi, Matteo, how’s it going?
2 00:01:08.460 ⇒ 00:01:12.170 Mateo: It’s going good. How about you? I’m sorry, I was caught up, you know.
3 00:01:12.370 ⇒ 00:01:13.860 Mateo: No meeting. Sorry for that.
4 00:01:14.090 ⇒ 00:01:19.679 Kaela Gallagher: No worries at all. Thanks for taking some time for me. I know I,
5 00:01:19.740 ⇒ 00:01:36.439 Kaela Gallagher: had reached out, via LinkedIn, and, yeah, just super curious about your, your background. I’m, leading people and recruiting efforts for Brainforge, and so just always looking to chat with good talent.
6 00:01:36.450 ⇒ 00:01:41.980 Kaela Gallagher: So yeah, I guess just starting off, like, curious why you might be open to new roles.
7 00:01:42.760 ⇒ 00:01:48.369 Mateo: Sure, of course. Well, right now, I’m working, as a contractor for a…
8 00:01:48.630 ⇒ 00:02:02.889 Mateo: Yeah, for a U.S.-based consulting company. Contract is about to end. The last project we are delivering, the last sprint. We’re under negotiations of knowing, but it’s only part-time.
9 00:02:03.000 ⇒ 00:02:10.099 Mateo: By the way, so… so yeah, I’m looking, I’m really open to explore more opportunities, or… Yeah, so…
10 00:02:10.360 ⇒ 00:02:13.850 Mateo: A little bit more stable, yeah, and long-term.
11 00:02:14.040 ⇒ 00:02:18.670 Kaela Gallagher: Yeah, about how many hours a week are you doing with them currently?
12 00:02:19.980 ⇒ 00:02:22.370 Mateo: Around 20.
13 00:02:22.550 ⇒ 00:02:24.439 Kaela Gallagher: Okay. 20 to 30, yeah.
14 00:02:25.100 ⇒ 00:02:29.190 Kaela Gallagher: Okay, okay, got it. And when does that contract come to a close?
15 00:02:30.150 ⇒ 00:02:35.669 Mateo: I think we’re missing 2 sprints, I think in about 30 days, yeah.
16 00:02:36.160 ⇒ 00:02:37.500 Kaela Gallagher: Okay. Okay.
17 00:02:37.820 ⇒ 00:02:46.839 Kaela Gallagher: Cool. If we, like, wanted you to start sooner than 30 days, is that something you’re open to do, or…
18 00:02:47.510 ⇒ 00:02:53.230 Mateo: Yeah, I think that will be okay. Just one week, two weeks will be enough.
19 00:02:53.750 ⇒ 00:03:04.529 Kaela Gallagher: Okay, okay, got it. Can you tell me more about the kind of, like, projects you’re doing with them? You’re working, like, in an analytics engineer capacity?
20 00:03:05.020 ⇒ 00:03:14.450 Mateo: Yeah, that’s correct, but it has switched more to a data engineer position, but yeah, I’ve been doing a lot of things.
21 00:03:14.700 ⇒ 00:03:21.580 Mateo: Implementations, migrations, also consulting and training with the…
22 00:03:21.780 ⇒ 00:03:34.739 Mateo: with their, like, technical team of the client. But yeah, everything really, around Snowflake, mostly they are, like, Snowflake partners, so that’s one thing that…
23 00:03:34.870 ⇒ 00:03:38.319 Mateo: they use. Sometimes, there are some
24 00:03:38.910 ⇒ 00:03:43.770 Mateo: like, looker projects as well, where I chime in and step in.
25 00:03:43.970 ⇒ 00:03:48.360 Mateo: Solar thing, but yeah, mostly it’s… Basically, for…
26 00:03:48.480 ⇒ 00:03:55.789 Mateo: Maybe small teams or companies that need just, like, one person to handle back-end, front-end
27 00:03:55.930 ⇒ 00:04:00.869 Mateo: In terms of data, and kind of manages, like, the runtime.
28 00:04:01.210 ⇒ 00:04:09.569 Mateo: their own project management is done by Kim’s also. So yeah, it’s really cool. It has been a great journey so far.
29 00:04:09.840 ⇒ 00:04:13.389 Kaela Gallagher: Okay, okay, awesome.
30 00:04:13.740 ⇒ 00:04:22.359 Kaela Gallagher: Curious, like, what about analytics engineering is, like, the most interesting to you? Like, why do you enjoy the work?
31 00:04:23.010 ⇒ 00:04:28.999 Mateo: It’s… it’s really good, because I was working as a, like, a data analyst. I worked for, I think.
32 00:04:29.100 ⇒ 00:04:32.500 Mateo: Three, four years as a data analyst, on…
33 00:04:32.730 ⇒ 00:04:43.599 Mateo: things were basically… it was a black box. You would deliver, like, data in the format you needed, and you don’t really, like, needed to pay attention on what
34 00:04:43.620 ⇒ 00:04:58.959 Mateo: processes were happening, like, behind scenes, and I… I’m, like, a curious person, so I… I decided to explore and take the backend part, the engineering part, to…
35 00:04:58.970 ⇒ 00:05:05.899 Mateo: To really understand how data flows, and not just, like, use data, but understand how
36 00:05:06.310 ⇒ 00:05:20.309 Mateo: Where does it come from, what transformations? And if you kind of, like, master that other part, you also master the analytics and insights part, and yeah, it’s really useful for…
37 00:05:20.460 ⇒ 00:05:23.779 Mateo: All the team on the company to have someone with
38 00:05:23.930 ⇒ 00:05:32.770 Mateo: Like, a whole knowledge of the whole pipeline, because if something breaks, or if there’s something new that they need, that person will help to
39 00:05:32.950 ⇒ 00:05:36.260 Mateo: Basically, establish, goals and, and several…
40 00:05:36.610 ⇒ 00:05:40.760 Mateo: Several things, so… so yeah, it’s… it’s a nice career.
41 00:05:41.150 ⇒ 00:05:49.020 Kaela Gallagher: Yeah, okay, okay, awesome. Yeah, just to tell you, like, a little bit more about Brainforge and what we’re hiring for right now.
42 00:05:49.240 ⇒ 00:06:02.679 Kaela Gallagher: we are, like, a data and AI consulting company, so working with clients across, many different industries, supporting, like, their data and AI strategy, so we have
43 00:06:02.680 ⇒ 00:06:13.709 Kaela Gallagher: Our team is kind of split into three parts. We have a strategy team, a data team, and an AI team. So this analytics engineering position would sit on our data… data team.
44 00:06:15.070 ⇒ 00:06:36.380 Kaela Gallagher: And yeah, we’re fully, fully remote, we have team all over the world, but we do align to U.S. time zones, so, we would ask you to either work, like, Central or Eastern U.S. time zones, which, I think, if I remember correctly, I traveled to Colombia before. It’s pretty similar to Colombian time zones.
45 00:06:36.380 ⇒ 00:06:41.259 Mateo: Yeah, it’s history, yeah, it’s history here, so it’s okay, it’s fine.
46 00:06:41.260 ⇒ 00:06:46.929 Kaela Gallagher: Perfect. Awesome. Yeah, that’s kind of the overview. Curious if you have any questions for me.
47 00:06:47.800 ⇒ 00:07:02.219 Mateo: Well, maybe if you can talk to me a little bit more about the process, and the company as well. Well, I did some research, I know that you guys do data consulting, but it will be nice to hear from you as well.
48 00:07:02.600 ⇒ 00:07:18.419 Kaela Gallagher: Yeah, yeah. So in terms of the process, what it would look like is, our interview process is 3 rounds. The first round is kind of just, like, a cultural fit, getting to know more about your experience and seeing if that aligns with us.
49 00:07:18.460 ⇒ 00:07:30.529 Kaela Gallagher: The second round is diving deeper into your technical side. It doesn’t involve any, like, live coding per se, but, really diving deeper into your technical skills and
50 00:07:30.530 ⇒ 00:07:40.860 Kaela Gallagher: Something that’s really important for our engineers is being able to communicate really complex concepts in a way that makes sense, because our engineers do interact with our clients.
51 00:07:40.860 ⇒ 00:07:43.380 Kaela Gallagher: So that round kind of looks into that.
52 00:07:43.380 ⇒ 00:08:01.679 Kaela Gallagher: And then our third round is, a challenge. So, we’ll give you, like, a take-home challenge, and then you’ll come to the final panel interview with your solution, and present that, why you chose your solution, why you went the route that you did, and the panel will ask you, questions about it. So…
53 00:08:01.780 ⇒ 00:08:04.790 Kaela Gallagher: Yeah, that’s kind of the overview there.
54 00:08:06.580 ⇒ 00:08:08.460 Mateo: Sounds great, sounds great, sounds good.
55 00:08:08.650 ⇒ 00:08:09.070 Kaela Gallagher: Okay.
56 00:08:09.070 ⇒ 00:08:09.810 Mateo: Thank you for that.
57 00:08:10.310 ⇒ 00:08:14.970 Kaela Gallagher: Yeah, yeah, of course. Any other, like, questions that I can help answer?
58 00:08:15.530 ⇒ 00:08:20.859 Mateo: No, I think we’re good, we’re good. Thank you very much for the couple minutes.
59 00:08:20.860 ⇒ 00:08:35.530 Kaela Gallagher: Yeah, yeah, absolutely. I can have you kind of start the interview process if it’s something that sounds of interest to you. And yeah, I can shoot over an email with, like, a booking link for… for the first round.
60 00:08:36.120 ⇒ 00:08:37.959 Mateo: Yeah, of course, that would be great.
61 00:08:38.950 ⇒ 00:08:43.359 Kaela Gallagher: Okay, awesome. And just confirming, like, you prefer…
62 00:08:43.480 ⇒ 00:08:48.440 Kaela Gallagher: An analytics engineer role instead of, like, a data engineer position.
63 00:08:49.300 ⇒ 00:09:03.770 Mateo: Like, I’m open for both, actually, but yeah, I think that during the interviews, maybe the technical and the challenges, we’ll know what fit will I be best, like, accommodating it.
64 00:09:04.090 ⇒ 00:09:05.740 Mateo: But yeah, I’m really open.
65 00:09:05.980 ⇒ 00:09:13.079 Kaela Gallagher: Okay. Okay. Yeah, analytics engineer is definitely, like, a bigger, bigger need for us right now.
66 00:09:13.250 ⇒ 00:09:16.119 Kaela Gallagher: What kind of analytics tools have you worked with?
67 00:09:17.040 ⇒ 00:09:24.069 Mateo: Mostly DVT. Like I told you, I work also with LookML.
68 00:09:25.240 ⇒ 00:09:30.269 Mateo: for models, Snowflake, Airflow, as well.
69 00:09:30.600 ⇒ 00:09:31.690 Mateo: GCP.
70 00:09:31.860 ⇒ 00:09:42.079 Mateo: AWS services, Redshift, yeah, and GCP also built in products, I think one’s called Dataprep.
71 00:09:42.200 ⇒ 00:09:44.030 Mateo: Another school, Alteryx.
72 00:09:44.650 ⇒ 00:09:51.729 Mateo: But… but yeah, I think the visualization tools, of course, Tableau, RVI, Looker Studio.
73 00:09:52.130 ⇒ 00:09:58.190 Kaela Gallagher: Okay. Have you heard of a tool called Omni? It’s, like, kind of similar to Power BI?
74 00:09:58.710 ⇒ 00:10:00.040 Mateo: Opening, no.
75 00:10:00.040 ⇒ 00:10:00.420 Kaela Gallagher: Okay.
76 00:10:00.420 ⇒ 00:10:01.550 Mateo: Okay.
77 00:10:01.810 ⇒ 00:10:10.450 Kaela Gallagher: That’s what we’re using with a lot of our clients, but it’s very, very similar. I’m sure you would pick it up quickly.
78 00:10:10.450 ⇒ 00:10:11.020 Mateo: Good.
79 00:10:11.370 ⇒ 00:10:21.699 Kaela Gallagher: Cool. Okay, yeah, I think that’s all I have for you. I’ll get the first round invite sent over, appreciate your time today, and yeah, just let me know if you need anything at all during the process.
80 00:10:22.110 ⇒ 00:10:23.540 Mateo: Of course, I will. Thank you very much.
81 00:10:23.540 ⇒ 00:10:24.460 Kaela Gallagher: Okay, cool, cool.
82 00:10:24.730 ⇒ 00:10:25.250 Mateo: Ronnie’s.
83 00:10:25.250 ⇒ 00:10:27.229 Kaela Gallagher: for your time, Ms. Have a good weekend.