Meeting Title: Brainforge Data Engineer Interview Date: 2026-04-15 Meeting participants: Nikhil G, Kaela Gallagher
WEBVTT
1 00:01:22.200 ⇒ 00:01:24.060 Kaela Gallagher: Hi, how’s it going?
2 00:01:24.250 ⇒ 00:01:27.130 Nikhil G: Hey, doing great, how about you?
3 00:01:27.510 ⇒ 00:01:31.380 Kaela Gallagher: Doing well. Thanks for taking some time for me, I appreciate it.
4 00:01:31.770 ⇒ 00:01:33.219 Nikhil G: Yeah, thanks, thanks for…
5 00:01:34.770 ⇒ 00:01:39.769 Kaela Gallagher: Yeah, can you remind me, how did we get in touch?
6 00:01:40.560 ⇒ 00:01:42.810 Nikhil G: From the Slack, channel.
7 00:01:43.010 ⇒ 00:02:00.639 Kaela Gallagher: Okay, okay, that’s right, awesome. Yeah, thanks so much for showing your interest, appreciate it. I can tell you, like, a little bit more about Brainforge and kind of what we’re looking for, and then would love to get to know, like, a little bit more about you as well.
8 00:02:00.790 ⇒ 00:02:10.569 Kaela Gallagher: But yeah, I guess just starting off Brainforge, we’re a data and AI consultancy, so, working with small to medium-sized organizations.
9 00:02:10.880 ⇒ 00:02:19.990 Kaela Gallagher: And we provide 3 service lines, data, AI, and strategy and analytics, and sometimes kind of a combination of those for our clients.
10 00:02:20.030 ⇒ 00:02:36.980 Kaela Gallagher: We are currently looking for a data engineer. In general, all of our engineering positions, we look for candidates that can contact switch easily. Because we’re consulting, you’d likely be working with 2-3 clients at a time.
11 00:02:37.130 ⇒ 00:02:56.820 Kaela Gallagher: So we need you to be able to bounce between those. And then because we’re still a smaller organization, we’re about 25 to 30 people right now, it is important that our engineers are very strong communicators, because, our engineers are joining client calls in front of clients. So,
12 00:02:56.820 ⇒ 00:03:08.240 Kaela Gallagher: those are kind of our main things. On the data engineering side, I would say, like, dbt and Snowflake are kind of the main technologies we look for, and
13 00:03:08.770 ⇒ 00:03:21.649 Kaela Gallagher: Yeah, right now, our entire team is on a 1099 basis, so, independent contracting. So also just wanted to call that out and make sure that that’s something you’re open to as well.
14 00:03:22.490 ⇒ 00:03:30.269 Nikhil G: Yeah, yeah, definitely, yeah, that aligns with what I’m looking for, and then it’s exciting, like, what you guys are doing, so probably
15 00:03:30.510 ⇒ 00:03:45.550 Nikhil G: as the world is transitioning towards more AI, and then the data-driven decisions, you know, like, this is a good time to join, such organization and make some impact, make some impact, you know, using my expertise and interest in the same area, yeah.
16 00:03:46.100 ⇒ 00:03:52.719 Kaela Gallagher: Awesome, awesome. I guess, what is making you interested in exploring a new role right now?
17 00:03:52.970 ⇒ 00:03:56.030 Nikhil G: Yeah, as I mentioned, you know, like.
18 00:03:56.190 ⇒ 00:03:56.590 Kaela Gallagher: Boom!
19 00:03:56.590 ⇒ 00:04:10.630 Nikhil G: organizations are adapting to this new tech stack, and then the… they wanted to leverage AI, you know? In order to leverage the AI, you need to have the foundation clear, you know? Like, your data needs to be pretty clean.
20 00:04:10.630 ⇒ 00:04:20.720 Nikhil G: variable structures, and then reliable so that you can apply those AI functions on top of it, and then get the meaningful results out of it, you know? So…
21 00:04:20.720 ⇒ 00:04:32.950 Nikhil G: In the last, like, more than 10 years, I have worked in the data industry to build similar systems from the scratch, be it migration project or building the data warehouses system.
22 00:04:32.950 ⇒ 00:04:54.549 Nikhil G: from the scratch, you know? So, I think that, like, I saw these job descriptions, and then you guys are doing the both things, data plus AI, you know, and then since, like, I have recently worked heavily on the AI side of it, or the implementations, and the workflows, and then the data qualities, and then…
23 00:04:54.550 ⇒ 00:05:03.049 Nikhil G: related stuff, so using LLM and all that stuff, so I think this is, this was very interesting for me, you know, so…
24 00:05:03.050 ⇒ 00:05:09.610 Nikhil G: that’s why I just, applied for it. So, yeah, looking forward to learn more, and then, to…
25 00:05:09.680 ⇒ 00:05:12.160 Nikhil G: To understand, yeah, what you guys are doing.
26 00:05:12.770 ⇒ 00:05:23.509 Kaela Gallagher: Okay, okay, cool. I know you mentioned doing data warehousing. What kind of data warehouses have you worked with? And I guess, in general, like, what would you consider to be your tech stack?
27 00:05:23.940 ⇒ 00:05:42.339 Nikhil G: Yeah, definitely. So, yeah, going back to, like, I have worked on the… all the legacy databases to migrate and then build the data warehouse system, you know? So, in the legacy, like, Oracle, MySQL, Postgres, and all that SQL server, you know, like, that was the source to migrate to the Snowflake.
28 00:05:42.340 ⇒ 00:05:51.760 Nikhil G: Then, Redshift, you know, even in the Databricks, I have worked, but heavily, I have worked on the Snowflake recently, using the dbt.
29 00:05:51.780 ⇒ 00:05:54.509 Nikhil G: And DLT, you know, like,
30 00:05:54.930 ⇒ 00:06:13.990 Nikhil G: it’s end-to-end, like, I can build the entire Terraform-managed infrastructure for these, Snowflake, for the DVD, Airflow, you know, Airflow, like, I have heavily used Astronomer Airflow, MWA, to orchestrate all that sort of stuff, so, yeah.
31 00:06:16.730 ⇒ 00:06:22.519 Kaela Gallagher: Cool. I guess in your current position, you’re… you’re still with Streamworks right now?
32 00:06:22.670 ⇒ 00:06:23.390 Nikhil G: Yeah.
33 00:06:23.390 ⇒ 00:06:33.589 Kaela Gallagher: Okay, in that role, what kind of, like, tools, high level, are you building, and are you interacting with clients in that position?
34 00:06:34.070 ⇒ 00:06:56.459 Nikhil G: Yeah, yeah, so the Streamworks, like, through that company, I have worked with multiple clients, you know, and then, like, it was the short- to long-term assignment, you know, to build the data warehouses system, you know. Recently, as I mentioned, it was heavily focused on the Databricks and Snowflake.
35 00:06:56.470 ⇒ 00:07:13.780 Nikhil G: And to, using the DVD as a transformation layer, you know, and then along with that, like, for the orchestration, I have used the Airflow, state functions from the AWS side. I’m also AWS-certified Solution Architect.
36 00:07:13.910 ⇒ 00:07:18.900 Nikhil G: And even, like, I have a Snowflake certification, for the,
37 00:07:19.320 ⇒ 00:07:37.090 Nikhil G: for the architectural things, and then the others, you know. So, the tools, like the Python, SQL, you know, that covers, like, all of it, you know? Like, pick any tool, you have to just, need to do the coding in Python or SQL in the data engineering world right now.
38 00:07:37.620 ⇒ 00:07:47.570 Kaela Gallagher: Got it. When you were working with these clients, what was the project that you were doing? Was it, like, migration, or, like, what kind of projects have you done with… with sports?
39 00:07:47.750 ⇒ 00:08:12.680 Nikhil G: Yeah, yeah, in the different project, you know, it was, like, migration from on-premises, they had their own SQL Server data warehouse from a decade old to migrate that to Snowflake, you know, like, build all new ingestion pipelines, then do the transformation on top of it, you know? Or sometimes, like, already it was in the cloud, but it was poorly written.
40 00:08:12.680 ⇒ 00:08:13.849 Nikhil G: Menten, you know?
41 00:08:13.850 ⇒ 00:08:23.090 Nikhil G: to re-architect everything in the cloud itself, so I have work on the port, you know, like, from on-prem to cloud, on cloud to cloud as well.
42 00:08:23.850 ⇒ 00:08:34.500 Kaela Gallagher: Okay, okay. And in that role, were you on calls directly with the clients, or you were communicating through, like, a project manager? Like, how is that set up?
43 00:08:34.980 ⇒ 00:08:52.379 Nikhil G: No, I was the direct point of contact for all the decisions, and then the architects, so I was building the documentation as well, like, what we are proposing, and I was directly showing it to the clients, and get the approvals, and then directly implementation on that stuff using
44 00:08:52.380 ⇒ 00:09:00.719 Nikhil G: the, the other resources we had in team, like, I also did the mentoring as well, you know, in that sense, you know, so yeah.
45 00:09:01.360 ⇒ 00:09:05.620 Kaela Gallagher: Okay, okay, cool. And you’re based right now in Dublin?
46 00:09:06.000 ⇒ 00:09:06.540 Nikhil G: Yeah.
47 00:09:06.790 ⇒ 00:09:13.050 Kaela Gallagher: Okay. Are you open to working, like, a US time zone? Have you done that before?
48 00:09:13.390 ⇒ 00:09:19.970 Nikhil G: Yeah, I’ve already done that in the past, you know, like, covering both EST and then the Pacific time, so that’s not an issue.
49 00:09:20.490 ⇒ 00:09:29.340 Kaela Gallagher: Okay, okay, cool. Yeah, our entire team right now is working, pretty much, like, Eastern time hours, so…
50 00:09:29.340 ⇒ 00:09:30.150 Nikhil G: Look at that.
51 00:09:30.650 ⇒ 00:09:31.900 Kaela Gallagher: Yeah. Yeah.
52 00:09:31.900 ⇒ 00:09:37.229 Nikhil G: worst, but, like, still, I’m okay to work in the Pacific time as well, yeah.
53 00:09:37.770 ⇒ 00:09:40.650 Kaela Gallagher: Okay, is Pacific time better for you, or Eastern?
54 00:09:40.650 ⇒ 00:09:41.800 Nikhil G: Eastern, Eastern Time. Eastern time.
55 00:09:41.800 ⇒ 00:09:54.289 Kaela Gallagher: Yeah, okay, that’s what… that’s what I thought. Yeah, I’m based in, Los Angeles, so I work Pacific time hours, but most of our team is working, Eastern, because we have some team members in… in Europe or
56 00:09:54.490 ⇒ 00:09:58.010 Kaela Gallagher: Like, India, so…
57 00:09:58.010 ⇒ 00:10:01.800 Nikhil G: It’s a 4-hour difference between EST and PST, right? Yeah.
58 00:10:02.350 ⇒ 00:10:04.640 Kaela Gallagher: Three, yeah, 3 hours.
59 00:10:04.850 ⇒ 00:10:05.660 Nikhil G: Okay.
60 00:10:06.230 ⇒ 00:10:14.100 Kaela Gallagher: Yeah. Okay, cool. Are there any questions about Brainforge that I could help answer for you?
61 00:10:14.560 ⇒ 00:10:28.889 Nikhil G: Yeah, I just wanted to understand, like, how do you guys work? You know, like, you mentioned it’s a small team, so how many clients you’re currently working with? Do you work with the multiple clients at the same time? Like, how’s the team structure, and then the…
62 00:10:29.240 ⇒ 00:10:33.849 Nikhil G: Interview process, like, if you could help me with that as well, yeah.
63 00:10:34.580 ⇒ 00:10:52.549 Kaela Gallagher: Yeah, so in terms of our team structure, I mentioned kind of, like, the three service lines that we provide our clients. Most of our team falls within what we call delivery, which is those 3 service lines, and this role specifically, being a data engineer, would fall under our data team.
64 00:10:52.550 ⇒ 00:10:59.260 Kaela Gallagher: So that’s… that’s where you would sit. You would work with probably 2 to 3 clients at a time.
65 00:11:00.500 ⇒ 00:11:14.900 Kaela Gallagher: probably, like, one bigger project and two smaller projects, something like that. And, in terms of the interview process, it’s three rounds. So, the first round is kind of an overview of your experience.
66 00:11:16.230 ⇒ 00:11:35.420 Kaela Gallagher: And then assessing communication skills, because obviously I mentioned that’s really important. The second round is technical. There’s no, like, live coding or anything like that, but just, like, a deep dive into the technical projects that you’ve done. And then the third round, we give you a take-home challenge.
67 00:11:35.420 ⇒ 00:11:44.649 Kaela Gallagher: And you come to a final panel with the presentation ready, as if you’re presenting to, like, a stakeholder or a client.
68 00:11:45.980 ⇒ 00:11:57.450 Kaela Gallagher: So that’s… that’s how we have that set up. For each round, we’ll send you a booking link, so the sooner you book, each round, the… the quicker we move through the process.
69 00:11:58.030 ⇒ 00:11:59.350 Nikhil G: Sounds good, yeah, yeah.
70 00:11:59.450 ⇒ 00:12:04.399 Nikhil G: And I believe, like, you wanted to start this as soon as possible, right?
71 00:12:05.250 ⇒ 00:12:16.930 Kaela Gallagher: Yes, we can… we can move pretty quickly. Yeah, I would say we can complete interviews within a couple weeks, and then, we can onboard and start pretty quickly as well.
72 00:12:17.760 ⇒ 00:12:18.629 Nikhil G: Thanks, Luxury.
73 00:12:19.510 ⇒ 00:12:20.280 Kaela Gallagher: Okay.
74 00:12:21.380 ⇒ 00:12:23.500 Kaela Gallagher: Cool.
75 00:12:24.000 ⇒ 00:12:34.580 Kaela Gallagher: Yeah, if you have any questions come up throughout the process, just let me know, I’m happy to help however I can. And yeah, appreciate your time today. Thank you so much.
76 00:12:34.750 ⇒ 00:12:36.339 Nikhil G: Yeah, thank you. Thanks, Kayla. Thanks.
77 00:12:37.240 ⇒ 00:12:39.219 Kaela Gallagher: Yeah, have a great rest of your evening.
78 00:12:39.520 ⇒ 00:12:40.340 Nikhil G: You too, you too.
79 00:12:40.340 ⇒ 00:12:42.150 Kaela Gallagher: Alright, thank you, bye.