Meeting Title: Brainforge Second Phase Interview Date: 2026-03-20 Meeting participants: Haricesh Ratnaharan, Greg Stoutenburg
WEBVTT
1 00:00:15.220 ⇒ 00:00:17.170 Haricesh Ratnaharan: Hey! Hey!
2 00:00:17.170 ⇒ 00:00:18.130 Greg Stoutenburg: Yes, me too.
3 00:00:18.480 ⇒ 00:00:19.870 Haricesh Ratnaharan: Nice to meet you!
4 00:00:20.180 ⇒ 00:00:22.729 Greg Stoutenburg: Sorry I’m a little, a little late getting on here.
5 00:00:22.730 ⇒ 00:00:24.209 Haricesh Ratnaharan: No, you’re good, you’re good.
6 00:00:25.080 ⇒ 00:00:28.160 Greg Stoutenburg: Yeah, well, well, welcome.
7 00:00:28.370 ⇒ 00:00:46.979 Greg Stoutenburg: I am just pulling up your stuff here. My name’s Greg Stautenberg. I’ve been with Brainforge for a couple of months. I work as a, as a data strategist and analyst. So, this is the second phase interview, the one that’s, like, more about, like, the specifics of the role and problem solving and things like that, rather than, like.
8 00:00:46.980 ⇒ 00:00:50.309 Greg Stoutenburg: You know, the sort of more general,
9 00:00:50.310 ⇒ 00:00:55.069 Greg Stoutenburg: first-round stuff, so… Sure. Yeah, let me, I guess I’ll just start by saying, you know, like.
10 00:00:55.600 ⇒ 00:01:01.050 Greg Stoutenburg: Hi, and tell me about yourself, and how’d you end up… how’d you end up on this other end of the call?
11 00:01:01.240 ⇒ 00:01:15.249 Haricesh Ratnaharan: Yeah, sure. So I’m Hari. I ended up here because Utam messaged me on LinkedIn saying, hey, there’s this role that we’re interested, and you seem to have some of the qualifications, we’d love to start a conversation.
12 00:01:15.280 ⇒ 00:01:38.520 Haricesh Ratnaharan: And so, I, met with Utam, so we, connected. I also connected with Robert, and, they seemed like outstanding people. The role itself seemed like the next step in my career, and, you know, that kind of jump-started the whole interview process. And so got to the first round, and then now I’m here. So happy to talk more about myself, and…
13 00:01:38.520 ⇒ 00:01:39.670 Haricesh Ratnaharan: But, yep.
14 00:01:39.950 ⇒ 00:01:47.479 Greg Stoutenburg: Cool, awesome. Yeah, yeah, my path was similar. So, nice, yeah. I mean, I guess just… excuse me.
15 00:01:47.650 ⇒ 00:01:53.410 Greg Stoutenburg: late afternoon here, ECUS. No, not late afternoon, never mind, mid-afternoon.
16 00:01:53.700 ⇒ 00:02:01.279 Greg Stoutenburg: Yeah, so, like, what roles have you had previously that, are relevant to this kind of work?
17 00:02:01.590 ⇒ 00:02:06.619 Haricesh Ratnaharan: Yeah, sure. So, I started my career, in, data science, so,
18 00:02:06.620 ⇒ 00:02:27.289 Haricesh Ratnaharan: background is computer science, did my master’s in data science, and then, I jumped straight into data science, whereas most people go to some type of industry, they study up and kind of get familiar with some type of domain, take that information to data science, or to become a data scientist or whatever. I kind of started the opposite way. So, I started in the electric industry, so, did two years at Electric Power Research Institute.
19 00:02:27.290 ⇒ 00:02:35.029 Haricesh Ratnaharan: Where I was a data scientist for asset management. So did a lot of ETL work, some grunt work, just, you know, starting out of college. It was kind of…
20 00:02:35.200 ⇒ 00:02:44.089 Haricesh Ratnaharan: really getting the feel for technology, but I realized I was only able to grow as much as the research side would let me grow. And so that’s why I decided to join the consulting world.
21 00:02:44.090 ⇒ 00:03:08.249 Haricesh Ratnaharan: So there, I joined Cognizant for a year. That jump-started, my abilities to work with enterprise-level tools. So got really into AWS, and, worked, like, clinical data and then some rental car data. That was cool, but then I, really wanted to sort of swim with the big fishes, and so that’s where I joined Deloitte, where I’ve been for the past almost 4 years now. There I’ve worked.
22 00:03:08.250 ⇒ 00:03:21.400 Haricesh Ratnaharan: as a data scientist, data engineer, and primarily as a data strategist, and then most recently as a, in data governance, where I’ve been running my own… where I’ve been taking ownership of, development.
23 00:03:21.400 ⇒ 00:03:35.110 Haricesh Ratnaharan: production, and then as well as delivery of this sort of reference database that we’re working on. And it’s been sort of very eye-opening. I’ve been able to sort of actually sell that work, the specific governance work.
24 00:03:35.110 ⇒ 00:03:45.680 Haricesh Ratnaharan: because it’s an internal project, and there’s a lot of governance that just needs to happen at Deloitte, which has surprisingly not happened up until this point, so that’s good for us, and maybe…
25 00:03:45.770 ⇒ 00:03:54.679 Haricesh Ratnaharan: eventually will be good for Deloitte, as we’re able to sort of package this sort of process, and then reapply it for all the other future projects that they have.
26 00:03:55.160 ⇒ 00:03:58.439 Haricesh Ratnaharan: And so I’ve kind of worn many hats, within the data world.
27 00:03:59.050 ⇒ 00:04:04.839 Greg Stoutenburg: Okay, okay, cool. So, when you say, you know, you’ve owned this project, what does that look like?
28 00:04:05.400 ⇒ 00:04:07.050 Haricesh Ratnaharan: Yep, so,
29 00:04:07.130 ⇒ 00:04:21.949 Haricesh Ratnaharan: So as I mentioned, this is an internal project, so I actually jumped in at the inception, so we got funding for this specific project, so it’s defining reference data and centralizing it in one location. As of, you know, the problem statement is that you have
30 00:04:22.019 ⇒ 00:04:39.129 Haricesh Ratnaharan: all these folks at Deloitte, accountants, auditors, just everybody, since you know how big Deloitte can be, they’re sending spreadsheets of reference and referential codes to each other, and that can get very messy quickly, especially as data becomes outdated.
31 00:04:39.130 ⇒ 00:04:44.799 Haricesh Ratnaharan: And so, there was a call to centralize it, so that there’s one sort of source of truth that
32 00:04:44.800 ⇒ 00:04:55.650 Haricesh Ratnaharan: constantly gets updated, and users can consistently go there, and have reliable data that they’d be able to pass around from there on, but knowing that as a source of truth. So, the…
33 00:04:55.650 ⇒ 00:05:00.449 Greg Stoutenburg: Sorry, the users that you mentioned, these are other folks at Deloitte?
34 00:05:00.450 ⇒ 00:05:02.719 Haricesh Ratnaharan: These are other folks, so it’s U.S. Deloitte folks.
35 00:05:02.720 ⇒ 00:05:03.310 Greg Stoutenburg: Okay.
36 00:05:04.040 ⇒ 00:05:05.500 Haricesh Ratnaharan: So,
37 00:05:05.730 ⇒ 00:05:22.569 Haricesh Ratnaharan: Yeah, so, got the funding, so leadership got the funding prior to me joining, and they, but the project hadn’t been kicked off yet. So, I joined in, into their data governance team, and they said, hey, we have this specific part of a project workstream that we need sort of kicked off.
38 00:05:22.760 ⇒ 00:05:38.419 Haricesh Ratnaharan: We need to sort of work out the kinks of what, defining scope, defining some milestones for the project, as well as, sort of what the realistic timeline would be, as well as going in and hiring who we need to, hire onto the project. And so I actually
39 00:05:38.530 ⇒ 00:05:41.990 Haricesh Ratnaharan: owned it from Point Zero, I mean, I guess…
40 00:05:42.050 ⇒ 00:05:56.840 Haricesh Ratnaharan: zero would be getting the funding, but point… from point one onwards. And so we’re approaching delivery in a couple months. But it’s been… since the kickoff, to the hiring, to the actual project planning, to working with my technical team to sort of get this thing lifted up.
41 00:05:56.850 ⇒ 00:06:04.130 Haricesh Ratnaharan: And then working with, directly talking to stakeholders. And then as well as, sort of, now that, sort of, the majority of the work is done, sort of.
42 00:06:04.130 ⇒ 00:06:21.419 Haricesh Ratnaharan: to other stakeholders who might be interested in this project, and sort of pitching it to them and saying, hey, look, we’ve been real successful with the, talent architecture modernization team. We can do this for your financial team, or we can do this with your, growth and purpose team. So there’s, like, different teams that we can actually repurpose this same project with.
43 00:06:21.960 ⇒ 00:06:29.530 Greg Stoutenburg: Cool, nice. Was that one of the, one of the goals of the project, is to make sure that it’s something that’s… that can scale and repeat?
44 00:06:29.760 ⇒ 00:06:36.770 Haricesh Ratnaharan: So it wasn’t originally the goal, of the project. The goal was to just establish, because, Deloitte’s kind of going through this
45 00:06:36.870 ⇒ 00:06:55.239 Haricesh Ratnaharan: talent modernization is a revamp, so they’re re-establishing titles and stuff like that, and so the main goal was to establish a central location for reference data. We realized, as we spoke to stakeholders throughout the project, to get data for the… for the… for the reference data values, there are far…
46 00:06:55.240 ⇒ 00:07:09.289 Haricesh Ratnaharan: greater use cases for the project, than we anticipated. And we sort of began slowly pitching it to these stakeholders who were already involved with the project, and they said, oh, I would love to see this in this other area as well. And so, being able to make those connections,
47 00:07:09.300 ⇒ 00:07:22.869 Haricesh Ratnaharan: were… it was unexpected, but it was a pleasant, unexpected value that came out of it. And so, for the latter, for the past, like, one month and a half, we’ve been sort of planning on how we can repackage this thing, and it’s been really successful.
48 00:07:22.870 ⇒ 00:07:29.020 Greg Stoutenburg: Yeah, it does sound like it’s been pretty successful. What… what makes you want to move away from there to here, then?
49 00:07:29.440 ⇒ 00:07:30.629 Haricesh Ratnaharan: Yeah, so.
50 00:07:30.880 ⇒ 00:07:34.689 Greg Stoutenburg: I know it’s a first-round kind of question, but I just feel like it’s a natural follow-up. Yeah, of course.
51 00:07:34.690 ⇒ 00:07:46.450 Haricesh Ratnaharan: Yeah, makes total sense. So, the thing is that I’ve really enjoyed this process, not only just… because I… for most of my, career, I’ve been sort of in the technical weeds, and as,
52 00:07:46.450 ⇒ 00:08:02.189 Haricesh Ratnaharan: as I joined Deloitte, I’ve gotten more and more into the strategy side, and to see myself grow exponentially, and see, I can do this, like, I’m like, okay, well, can I take this elsewhere and really make, make a difference? Whereas Deloitte, I can only go as far as the project will let me.
53 00:08:02.430 ⇒ 00:08:07.380 Greg Stoutenburg: This is your project there, and then you’ll go back to doing other sort of analytical and data science sort of work.
54 00:08:07.380 ⇒ 00:08:18.980 Haricesh Ratnaharan: whatever’s, like, sort of, like, whatever. They say that you can sort of choose what you want to do, sometimes that’s the case, sometimes it’s, like, you just kind of get what you get. So I… to be able to sort of consistently do this type of work is kind of what.
55 00:08:18.980 ⇒ 00:08:26.919 Greg Stoutenburg: Okay, cool. That’s great. So I’ll ask some of the, some of the questions that are sort of like.
56 00:08:27.250 ⇒ 00:08:46.379 Greg Stoutenburg: peg for this interview stage, with that context, that’s really helpful. So thinking about… and you can talk about… you can answer these questions with respect to the project that you just described, or anything else in your experience, or even if it’s not something you’ve experienced, then just, you know, your knowledge. How do you turn a vague question into an analysis plan?
57 00:08:48.140 ⇒ 00:08:58.320 Haricesh Ratnaharan: Yeah, sure. So, I mean, this project started out big. I mean, the kickoff was, like, okay, we just want to establish reference data, in one single spot.
58 00:08:58.320 ⇒ 00:09:12.570 Haricesh Ratnaharan: okay, I usually like to write out this question, or write out the vague question, and nitpick aspects of it and expand that way, right? So, for example, like, you know, we want to centralize reference data. Okay, well.
59 00:09:12.670 ⇒ 00:09:20.380 Haricesh Ratnaharan: which reference data are we centralizing? Do we have, any scope narrowed down? You know, like, there’s all sorts of reference data that’s out there.
60 00:09:20.530 ⇒ 00:09:27.599 Haricesh Ratnaharan: That led to… by me asking that question, that led to us defining 32 prioritized data… critical data elements.
61 00:09:27.600 ⇒ 00:09:42.169 Haricesh Ratnaharan: that are going to… that served to bolster our scope and our project moving forward. Okay, cool. Then, where is this data going to be centralized, right? What tools are we going to be using? And that actually caused a hiccup, right?
62 00:09:42.170 ⇒ 00:09:59.939 Haricesh Ratnaharan: by me asking that, we originally were leaning towards Informatica, but then we found out, you know, a month later that Informatica’s gonna be stood down in a couple years. There’s no point in, sort of, working with a product that’s going to be stood down. Let’s work with, in this case, it ended up being Databricks. And so,
63 00:10:00.120 ⇒ 00:10:05.299 Haricesh Ratnaharan: There was a lot of conversations and a lot of branches of questions that came out of that, and
64 00:10:05.300 ⇒ 00:10:28.409 Haricesh Ratnaharan: a lot of, alright, let’s just nail down that we are going to use Databricks, like, you know, I don’t want 6 months down the line, we have this project, everything’s kind of in Databricks, and then, you know, we are like, oh no, we need to switch back to Informatica, or whatever. So, ensuring that the work is there. So, that was another thing. So it’s really dissecting the question and expanding and expanding more targeted questions is kind of how I like to go about it. That way, we can have measurable.
65 00:10:28.410 ⇒ 00:10:38.740 Haricesh Ratnaharan: measurable, work and measurable, like, things that we can put pen to paper, and officially sort of box out, the type of work that’s going to be done. So it’s, like, it’s…
66 00:10:38.810 ⇒ 00:10:48.689 Haricesh Ratnaharan: that’s how I’ve found has been the easiest and most prudent way to sort of go about it. I’m sure there’s other ways, but for the most… for this project specifically, that’s been really effective for me.
67 00:10:48.930 ⇒ 00:10:51.409 Greg Stoutenburg: Yeah, great, great, great.
68 00:10:51.560 ⇒ 00:11:06.109 Greg Stoutenburg: So, since you’re building something that’s supposed to be, something that allows for self-service from other people at Deloitte, how do you ensure clients can operate your work without you? How do you make it self-serviceable, in other words?
69 00:11:06.110 ⇒ 00:11:29.480 Haricesh Ratnaharan: Yeah, yeah, that’s a good question. So, we’ve sort of baked in, about almost 2 or 3 months of sort of a knowledge transfer. So that involves identifying, identifying who the, specific admins or end-user admins are going to be, who are going to be sort of managing and maintaining this, this specific database, in this example. And then from there, it’s going to be,
70 00:11:29.700 ⇒ 00:11:45.859 Haricesh Ratnaharan: specific, who are the stakeholders, we need to start identifying… building out the documentation and documenting every single thing that we’re doing, so that when we do officially kick off KT to these, stakeholders, everything can be as clear as day. There is not a single, sort of.
71 00:11:45.980 ⇒ 00:12:03.719 Haricesh Ratnaharan: aspect of the technical work that’s not accounted for. And then also, there’s also a prediction factor, so, you know, we also need to bake in, like, is there, you know, enhancements or additional things that the client wants to see, or the stakeholders want to see in the product? We need to bake that in, and
72 00:12:03.720 ⇒ 00:12:17.740 Haricesh Ratnaharan: how… what is the process, for adding in new features? What is the process for adding in new critical data elements, right? So I mentioned we’re working with 32, but in the future, we’ll be adding more and more, as this, reference data grows. So.
73 00:12:17.740 ⇒ 00:12:28.219 Haricesh Ratnaharan: how do we go about doing this, right? So, to be able to foresee all of the different components of this, not just for how it works right now, but how it will work in the future, is kind of how I got about that.
74 00:12:28.680 ⇒ 00:12:35.450 Greg Stoutenburg: Yeah, yeah, yeah, okay, so kind of trying to anticipate where someone’s going to need to be able to use something, making sure that they’re able to do it that way.
75 00:12:35.450 ⇒ 00:12:36.319 Haricesh Ratnaharan: Yeah, yeah, exactly.
76 00:12:36.320 ⇒ 00:12:42.700 Greg Stoutenburg: No. Describe explaining technical findings to a non-technical executive.
77 00:12:43.550 ⇒ 00:12:47.949 Haricesh Ratnaharan: Sure. So I like to start with the outcome. Usually what they’re,
78 00:12:47.950 ⇒ 00:13:11.539 Haricesh Ratnaharan: what they’re… trying to sort of weed out what they’re looking for. I mean, if I already know what they’re looking for, then I like to start from that point, and then backtrack to, some of the technical work that’s required to get to their outcome, so that they have the light at the end of the tunnel, in their mind, as we talk through the technical stuff. So basically, why should you care about the technical stuff, and how does this impact your end goal?
79 00:13:12.440 ⇒ 00:13:35.430 Haricesh Ratnaharan: I found that to be really helpful. I try not to get too technical unless there’s other technical SMEs from the client side, that, can join in and participate in the conversation. But, throughout the entire conversation, I try to make sure it is a conversation. Like, I’m getting some feedback, it’s not just a lecture where I’m just telling you how everything’s gonna be done. I want questions, I want to be able to, engage with the person, the stakeholder, and say.
80 00:13:35.620 ⇒ 00:13:37.080 Haricesh Ratnaharan: You know, basically.
81 00:13:37.500 ⇒ 00:14:00.650 Haricesh Ratnaharan: this is sort of the… this is what we’re going to be doing, does it make sense to you? Are there any improvements, or do you think we need to… are there any process things that are on your end that we might need to anticipate, to be baked into the product, or into the process, or whatever? So, I try to make it a more lively and engaging conversation, and then as well as making sure that they understand how this kind of affects their bottom line.
82 00:14:00.650 ⇒ 00:14:03.630 Haricesh Ratnaharan: And then that way it ties the two, and…
83 00:14:03.630 ⇒ 00:14:07.610 Haricesh Ratnaharan: There isn’t any, there isn’t any sort of zoning out during the conversation.
84 00:14:07.940 ⇒ 00:14:14.719 Greg Stoutenburg: Yeah, yeah, yeah. No, I like that. I like the emphasis on, like, you’re having a conversation with the executive rather than just giving a lecture, because
85 00:14:14.720 ⇒ 00:14:39.310 Greg Stoutenburg: one of the things that’s important in our business, as we do, like, weekly updates to clients, which do involve, you know, a presentation, and here’s some things we did for you, and things like that, is, it becomes very, you know, it’s… not only is it boring for everyone to have someone just read off a slide for them, but also, it just kind of seems like it’s a waste of time. Like, if it’s just a matter of FaceTime, and here’s what we did, that’s an email. So, you know, that’s…
86 00:14:39.310 ⇒ 00:14:57.220 Greg Stoutenburg: you know, you caught a good one there. When do you… in the past, where have there been experiences where you’ve had a client disagree with the approach that you took to a problem? And, how did you handle that? Or… or if not, like, the approach you took to a problem, what your findings were, or things like that?
87 00:14:57.470 ⇒ 00:14:59.490 Haricesh Ratnaharan: Yeah, sure. So,
88 00:14:59.550 ⇒ 00:15:10.250 Haricesh Ratnaharan: being as part of a data governance team, there’s different work streams, so mine is the reference data, but I’ve, I’ve, sort of co-worked with a data lineage team, and,
89 00:15:10.250 ⇒ 00:15:11.970 Haricesh Ratnaharan: With that,
90 00:15:11.970 ⇒ 00:15:34.049 Haricesh Ratnaharan: leadership and one of the stakeholders came to us and asked, hey, do you think we could do the same type of lineage that we’re doing in Informatica, but in Databricks? And, you know, I was like, yeah, we could look into it, for sure. And so, I had my technical team sort of look into it. Originally, they came back to me and said, I don’t think it’s possible to do exactly what we’re doing on Informatica.
91 00:15:34.120 ⇒ 00:15:53.739 Haricesh Ratnaharan: I looked into it, and I think that’s where it’s helpful to be a technical person, because I can actually, like, know what we need to be looking for. And I also saw that there isn’t an exact one-to-one mapping to be able to do that. Databricks has just not matured into that aspect yet, without doing a lot of rework, which was the biggest thing.
92 00:15:54.320 ⇒ 00:15:55.330 Haricesh Ratnaharan: So…
93 00:15:55.500 ⇒ 00:16:06.830 Haricesh Ratnaharan: we brought that, I brought that to my leadership before the stakeholder and said, hey, you know, I built a presentation, said, hey, I think this is where we’re at, this is where we want to get to, and here’s the disconnect.
94 00:16:06.940 ⇒ 00:16:10.639 Haricesh Ratnaharan: I think what we can do is, sort of, if we wanted to
95 00:16:11.470 ⇒ 00:16:20.909 Haricesh Ratnaharan: be proactive and plan out what their type of work would be. We can say that, you know, the rework would need to be established first, and then we can do the migration to Databricks.
96 00:16:21.390 ⇒ 00:16:25.279 Haricesh Ratnaharan: So, coming to them with a plan, really hoped. And then.
97 00:16:25.280 ⇒ 00:16:48.699 Haricesh Ratnaharan: once we did bring it to the stakeholder, there was some pushback, like, you know, of course, nobody wants to do rework, especially because they’ve just done, like, the past 6 months of putting everything into Informatica. We also brought an additional SME, a third-party objective SME into the conversation, who’s worked with Informatica and Databricks, and said, yeah, this isn’t really possible. If we want to have a further conversation, feel free to, you know.
98 00:16:48.700 ⇒ 00:16:58.840 Haricesh Ratnaharan: bring in, we can… we can do that, but I think that really bolstered everything. There was… there was a level of, like, you know, obviously nobody wants to rework, but at the same time, if we can get
99 00:16:58.840 ⇒ 00:17:06.260 Haricesh Ratnaharan: if you’re getting the same information from 3 different parties, I think, like, that’s kind of where everything’s headed towards. So,
100 00:17:06.260 ⇒ 00:17:23.239 Haricesh Ratnaharan: they were happy at the end, and they were like, okay, yeah, let’s just table this conversation, we’ll continue doing the work we’re doing right now. So, that’s kind of how we handled it, just kind of being as proactive as possible, even if the news isn’t great, just being… trying to be upfront, and then if… if, you know, the news isn’t great, these are our alternatives.
101 00:17:23.720 ⇒ 00:17:39.180 Greg Stoutenburg: Yeah, yeah, yeah, yeah. Yeah, okay, that’s fantastic. What’s your… yeah, one more of these, and we’ll just talk. What’s your approach to quick wins in your first 30 days? So, say, you know, you get a… you get a contract here, what do you do, sort of, right out of the gate?
102 00:17:39.640 ⇒ 00:17:42.929 Haricesh Ratnaharan: Yeah, sure. I mean, like, from a planning perspective,
103 00:17:43.130 ⇒ 00:18:06.290 Haricesh Ratnaharan: establishing cadences, with stakeholders, making sure that, they, know that we’re going to be part of the conversation the entire way, building out sort of a milestone. So I do, like, a five-week milestone plan of, like, what are we doing week one, two, these are the accomplishments, and these accomplishments are going… are going to take us, into the next six months, into the next 180 days, or whatever.
104 00:18:06.480 ⇒ 00:18:28.380 Haricesh Ratnaharan: So I start with those two things, and then we… internally, I have sort of a timeline… I build out a timeline, like, okay, guys, this is what we’re working with, and I usually like to bake in buffer periods for, you know, you never know what happens, there’s project delays and stuff like that. And then the… depending on the type of work, that… that milestone is supposed to illustrate some of those quick wins within the 30 days.
105 00:18:28.380 ⇒ 00:18:31.120 Haricesh Ratnaharan: So for example, with my data governance project, right? So.
106 00:18:31.120 ⇒ 00:18:54.120 Haricesh Ratnaharan: We built out a solution architecture diagram, we built out a sort of a roadmap for all the stakeholders who need to be part of the conversation in order to get their buy-in, what are the data sources that… where we’re going to be pulling the reference data from. So, like, as much as we can sort of proactively, decide, based on the outcome and based on the SOP,
107 00:18:54.130 ⇒ 00:19:12.559 Haricesh Ratnaharan: we try to lay that out as much as possible. And that’s been really effective. It gets leadership off my back, because then everybody knows and it’s visible what we’re going to be doing. And then after that, it’s… during those 5 weeks, we sort of readjust depending on what other additional tasks are going to be involved, within the work itself.
108 00:19:12.890 ⇒ 00:19:19.579 Greg Stoutenburg: Yeah, yeah, yeah, yeah. What do you think? Do you think there’s anything in general that usually serves as a quick win for a client?
109 00:19:20.490 ⇒ 00:19:23.070 Greg Stoutenburg: Besides some of the operational things that you just mentioned?
110 00:19:23.070 ⇒ 00:19:26.320 Haricesh Ratnaharan: Yeah, so, I mean,
111 00:19:26.950 ⇒ 00:19:30.390 Haricesh Ratnaharan: quick wins, I’m trying to think, like,
112 00:19:30.840 ⇒ 00:19:35.599 Haricesh Ratnaharan: establish, like, I mean, if, like, I’m just thinking about from, like, my data engineering days,
113 00:19:35.990 ⇒ 00:19:58.259 Haricesh Ratnaharan: any low-hanging fruit, like, for example, like, if it’s a huge data migration project, if there’s data that, for example, that can be migrated over without having… with having the least amount of risk, with the least amount of overhead, we go ahead, try to do that with, obviously, communication with the client. So anything that’s non-risk, anything that’s non, sort of, has to go through the bureaucratic process.
114 00:19:58.260 ⇒ 00:20:10.459 Haricesh Ratnaharan: we try to get that out of the way, right? And we save the big… sort of the big blocks, for when we do need the… do need that. So, for example, there was an AWS migration that we need to do, and a lot of the work that,
115 00:20:10.460 ⇒ 00:20:16.130 Haricesh Ratnaharan: A lot of the… a lot of the data was sort of financial service industry data, so there was a lot of
116 00:20:16.130 ⇒ 00:20:26.170 Haricesh Ratnaharan: red tape around it, but then there was some operational data that we could easily move over, into S3, and so that stuff we… we’re like, okay, we’ve already done it, you don’t even need to think about it,
117 00:20:26.280 ⇒ 00:20:35.799 Haricesh Ratnaharan: we can move on to the big ticket items. So those were some examples of some quick wins. It just depends on the type of work, I guess, but that’s kind of…
118 00:20:35.950 ⇒ 00:20:36.969 Haricesh Ratnaharan: what I’m thinking.
119 00:20:37.280 ⇒ 00:20:45.260 Greg Stoutenburg: Yeah, cool, cool. Did Robert Newton talk much about what sorts of clients we have and sorts of projects that we do?
120 00:20:45.550 ⇒ 00:21:02.960 Haricesh Ratnaharan: Yeah, so, I mean, they talked, they talked about how, like, it’s not necessarily… so it’s definitely not, staff augmentation, they try to really establish the connection with clients, and, the idea is sort of long-term, and there’s a lot of data engineering work, but I, you know, I’d love to hear more from your perspective.
121 00:21:02.960 ⇒ 00:21:14.570 Greg Stoutenburg: Yeah, yeah, yeah. Yeah, I mean, we’ve got a… we’ve got a variety of clients, and that includes… I mean, we’ve got customers in…
122 00:21:14.790 ⇒ 00:21:28.730 Greg Stoutenburg: e-com and, sort of… I’ll say medical retail, I don’t know exactly what to call them. We’ve got customers that, I mean, it’s SaaS, just sort of, like, all over the place.
123 00:21:28.730 ⇒ 00:21:35.780 Greg Stoutenburg: And with a lot of different needs. Now, I’m sure, you know, with your background, you know, one of the things we do is we do a lot of, you know, database
124 00:21:35.780 ⇒ 00:21:56.300 Greg Stoutenburg: type work. We do a lot of data modeling that’s part of things like standing up BI tools. Helped one customer migrate from Tableau into Omni a month ago, helping one customer go from spreadsheets to BI. So I’ve been involved in things from there, to things like conversion rate optimization.
125 00:21:56.300 ⇒ 00:22:05.810 Greg Stoutenburg: For, you know, e-com. So, kind of all over the place in terms of, like, what… who the clients are and what they want to see.
126 00:22:05.850 ⇒ 00:22:13.239 Greg Stoutenburg: But the work streams, like, the specific types of projects are the ones that, you know, Utam or Robert would have mentioned.
127 00:22:13.240 ⇒ 00:22:32.460 Greg Stoutenburg: But yeah, I mean, I guess I… my… sort of my follow-on to that was, what sort of work do you want to be doing? Are there, you know, is there a certain direction you want to go? I mean, you’ve mentioned, like, the project management kind of thing that you’re doing with data being, like, the kind of work you want to do, but is there…
128 00:22:32.550 ⇒ 00:22:38.529 Greg Stoutenburg: Anything you can say about, like, a certain client you want to have, or anything related to industry, or anything like that?
129 00:22:39.230 ⇒ 00:23:01.469 Haricesh Ratnaharan: I mean, so I don’t have any specific, I’ve been cross-industry for my entire career, pretty much, so there isn’t any specific industry. You know, being at Deloitte, I’ve been mostly in the FSI sector, so a lot of my, like, financial clients, but I’ve sprinkled in public sector, right, so I’ve worked with the state of Tennessee before, I’ve also worked with, some health, some health industries,
130 00:23:01.510 ⇒ 00:23:09.129 Haricesh Ratnaharan: the retail health, like CVS and stuff like that. So, there… it’s kind of been all over the place. I don’t have any specific,
131 00:23:09.130 ⇒ 00:23:23.130 Haricesh Ratnaharan: industry, and even, like, the type of clients, I’ve kind of worked on a myriad of, with, several different clients, so again, that’s also not, not anything I’m specific, towards. Yeah.
132 00:23:23.570 ⇒ 00:23:30.309 Haricesh Ratnaharan: it’s… right now, it’s… because it’s been consulting for the past 5 plus years, it’s… I’ve kind of worked with all sorts.
133 00:23:30.310 ⇒ 00:23:43.399 Greg Stoutenburg: All over. Yeah. Cool. Well, I mean, just 3 minutes left, I’m just, you know, I’m just at IC, helping out with interviews, so anything I can answer, though, I’d be happy to, if you have any questions for me.
134 00:23:43.400 ⇒ 00:23:48.419 Haricesh Ratnaharan: Yeah, I mean, what is… What, so, what does a,
135 00:23:49.680 ⇒ 00:23:52.990 Haricesh Ratnaharan: First of all, I guess, how’s been your experience with Brain Forge?
136 00:23:54.070 ⇒ 00:24:01.830 Greg Stoutenburg: Yeah, it’s been good. I mean, I… so, I came out of being a PM in product growth for a few different years at a couple different SaaS companies.
137 00:24:01.830 ⇒ 00:24:22.290 Greg Stoutenburg: And, so my experience here, you know, I’m… I’m not on, like, the analyst or engineer, background sort of path that a lot of people are here, so, I’ve gotten… I mean, I’d say I’ve gotten a lot of new experience that’s been really good. The team is very collegial. Things move very quickly. A lot of things are still being built out, because
138 00:24:22.290 ⇒ 00:24:37.769 Greg Stoutenburg: I mean, I guess, like, a year ago, it was just Robert and Tom, and, like, you know, a couple other folks doing some projects, and now it’s, you know, it’s gotten just a lot bigger, sort of quickly. And, you know, growth is exciting, but it also means that, like, you have to sort of, like, keep a grasp on what that
139 00:24:37.890 ⇒ 00:24:49.890 Greg Stoutenburg: that growth is going to feel like as things expand, so, you know, it’s been good. I think it’s been good. There’s a lot to take on, a… an environment that requires a lot of ownership.
140 00:24:50.350 ⇒ 00:24:57.550 Haricesh Ratnaharan: Gotcha. And how… you know, how has… or has… what does your day-to-day look like, or even, like, week-to-week, especially working with clients, and…
141 00:24:57.550 ⇒ 00:25:13.909 Greg Stoutenburg: Yeah, yeah, so we… I mean, some of the things we shoot for, like, weekly meetings with clients to show them what we’ve done and what’s coming next. I’m managing 4 different work streams right now, which is probably a little much. And but, you know, we sort of aim for, like, 3.
142 00:25:13.910 ⇒ 00:25:30.859 Greg Stoutenburg: I’ve done everything from mapping out, and so, like, one client I’ll meet with in an hour and a half, just, like, mapping out the user journey through the initial steps of their product to show, here are the events you should track, so that we can start doing experiments to, like, improve your conversion rates and things like that. I’ve done things from that to, like.
143 00:25:30.860 ⇒ 00:25:45.650 Greg Stoutenburg: leading a project where, not being a data engineer, I don’t even know how to do the data modeling, I’ve just led, like, the transition from one BI tool to another, and just, like, doing these things at the same time, you know? So, very different types of work. But yeah, the cadence is…
144 00:25:45.650 ⇒ 00:25:59.660 Greg Stoutenburg: is just staying aligned with the team, updating tickets in linear, making sure there’s a clear project plan that’s actually, you know, realistic. It’s an important one. You know, ambitious, but realistic. So, yeah, I mean, I hope that… I hope that helps a little bit.
145 00:25:59.660 ⇒ 00:26:03.180 Haricesh Ratnaharan: Yeah, you know, have you done any, like, selling, or, like, how does that kind of…
146 00:26:03.500 ⇒ 00:26:14.360 Greg Stoutenburg: Yeah, no, that’s a good question. So, I’ve done a… I’ve made a few pitches, and, and done some selling. That’s how… I mean, that’s how one of the migration projects is happening.
147 00:26:14.360 ⇒ 00:26:27.759 Greg Stoutenburg: another BI tool set up that’s sort of in the process of being sold. I’m running a pilot to hopefully get them 3 weeks from now to scale, and then, you know, they buy that, and then our work is that we did that, and now we manage it. So…
148 00:26:28.070 ⇒ 00:26:45.920 Greg Stoutenburg: Selling is part of it as well. The way that it’s gone for me is I’m onboarded to a client, I’m doing some work for the client, identify a need, and… or someone else has identified a need, and then, you know, sort of work with them to go, you know, hey, you know, we can get you to here if we do this, here’s an expanded scope of work if we want to go that direction.
149 00:26:46.410 ⇒ 00:26:57.380 Haricesh Ratnaharan: Gotcha. And, you know, I know we’re at time here, but last question is, like, so I’m assuming that process involves, like, collaborating with the team, like, okay, you know, here’s the need, what do we need to do to get to that, you know, to fulfill that need?
150 00:26:57.380 ⇒ 00:27:11.969 Greg Stoutenburg: Yeah, yeah, yeah. Yeah, and it’s, you know, it’s a place where, while there’s a high degree of ownership, you’re not just, like, on your own. So, you know, I mean, I… even just… just yesterday morning, for example, I was looking at all these things that needed to be done before a client meeting that’s pretty important. Like, I think…
151 00:27:11.970 ⇒ 00:27:36.279 Greg Stoutenburg: one of our… one of our highest margin, clients that I’m working with, and I just had, like, wall-to-wall meetings, and I’m like, Utom, I gotta do something about this, and he just, like, kicked a whole bunch of meetings off my calendar so that I could, you know, do the work that I needed to do, and then it was like, alright, cool, you know, let’s… let’s… let’s be in touch throughout the day and get feedback regularly as I’m… because I was building this project plan to show, to show the client, and then, you know, and then it went great. So, like, you… you have that kind of,
152 00:27:36.280 ⇒ 00:27:41.310 Greg Stoutenburg: Resource available to you as well, even on those days that are, you know, sort of wall-to-wall on the calendar.
153 00:27:41.470 ⇒ 00:27:44.019 Haricesh Ratnaharan: Gotcha, gotcha. Okay. Yeah. Oh, that sounds good.
154 00:27:44.020 ⇒ 00:27:45.110 Greg Stoutenburg: Yeah, cool.
155 00:27:46.140 ⇒ 00:27:55.689 Greg Stoutenburg: Okay, great. Well, great to meet you, great to talk with you. I’m just, you know, like I said, I’m just one step in the process, but I’ll give my feedback to the team, and then, whoever’s been in touch will be in touch.
156 00:27:55.690 ⇒ 00:27:56.630 Haricesh Ratnaharan: Sounds good. Thanks so much.
157 00:27:56.850 ⇒ 00:27:58.090 Greg Stoutenburg: Nice meeting you, Harry. See ya.