Meeting Title: BF Interview: Awaish <> Venkata Date: 2025-12-08 Meeting participants: Venkata Prasad Krupananda, Awaish Kumar
WEBVTT
1 00:02:10.889 ⇒ 00:02:11.459 Venkata Prasad Krupananda: Hello.
2 00:02:12.810 ⇒ 00:02:14.099 Awaish Kumar: Yeah, aye.
3 00:02:15.060 ⇒ 00:02:16.710 Venkata Prasad Krupananda: I cannot hear you, Avish.
4 00:02:18.410 ⇒ 00:02:20.339 Awaish Kumar: Hello, can you hear me now?
5 00:02:21.310 ⇒ 00:02:24.090 Venkata Prasad Krupananda: No, I cannot hear you yet. Just a minute.
6 00:02:25.910 ⇒ 00:02:27.239 Venkata Prasad Krupananda: Can you say something now?
7 00:02:28.590 ⇒ 00:02:29.370 Awaish Kumar: Hello.
8 00:02:29.370 ⇒ 00:02:30.590 Venkata Prasad Krupananda: Yeah, I can hear you.
9 00:02:32.630 ⇒ 00:02:34.469 Awaish Kumar: How are you doing?
10 00:02:34.810 ⇒ 00:02:37.000 Venkata Prasad Krupananda: Yeah, I’m doing good. How are you doing?
11 00:02:37.570 ⇒ 00:02:38.760 Awaish Kumar: How good is that?
12 00:02:40.780 ⇒ 00:02:44.100 Awaish Kumar: Yeah, my name is Avesh Kumar, and I’m a data engineer.
13 00:02:44.100 ⇒ 00:02:45.400 Venkata Prasad Krupananda: Okay.
14 00:02:45.400 ⇒ 00:02:51.740 Awaish Kumar: I haven’t… here, from other IO, and have been in another engineering field for, like,
15 00:02:52.090 ⇒ 00:02:54.000 Awaish Kumar: More than 9 years now?
16 00:02:54.830 ⇒ 00:03:03.220 Awaish Kumar: I’ve been… Like, kind of working, building data infrastructure, and building end-to-end data pipelines.
17 00:03:03.390 ⇒ 00:03:06.479 Awaish Kumar: Okay. Yeah, so what about you?
18 00:03:06.740 ⇒ 00:03:13.159 Venkata Prasad Krupananda: So you’ve been working with, Brain Forge for the last full… the whole nine years, or recently?
19 00:03:14.000 ⇒ 00:03:14.830 Awaish Kumar: Hang on.
20 00:03:17.100 ⇒ 00:03:18.230 Venkata Prasad Krupananda: For the whole nine years?
21 00:03:19.560 ⇒ 00:03:20.560 Awaish Kumar: For a year.
22 00:03:20.560 ⇒ 00:03:22.810 Venkata Prasad Krupananda: Couldn’t hear, okay, I couldn’t hear you properly.
23 00:03:27.230 ⇒ 00:03:28.160 Venkata Prasad Krupananda: So, yeah.
24 00:03:28.620 ⇒ 00:03:29.480 Venkata Prasad Krupananda: Yeah.
25 00:03:30.270 ⇒ 00:03:32.430 Awaish Kumar: What about you? Like, can you introduce.
26 00:03:33.440 ⇒ 00:03:38.990 Venkata Prasad Krupananda: Yeah, definitely. So, my full name goes Venkara Prasakrupananda, and I work as a…
27 00:03:39.340 ⇒ 00:03:50.980 Venkata Prasad Krupananda: data analyst when it comes to product marketing and product integration, and with a background in data analytics, and also marketing analytics, basically. And I’ve got a little over, I can say.
28 00:03:50.980 ⇒ 00:04:04.830 Venkata Prasad Krupananda: four and a half to five years of experience, working across industries and, like, consulting, and also higher education, and also digital marketing and advertising. And, I also worked with Dell Technologies back in India.
29 00:04:04.840 ⇒ 00:04:21.130 Venkata Prasad Krupananda: And most recently, I’ve been with McKinsey & Company, where I focus on product performance, and also the analysis on the product performance using various tools and softwares, and data pipelines using SQL and Python, and also
30 00:04:21.130 ⇒ 00:04:29.839 Venkata Prasad Krupananda: leveraging tools to, tools basically like mixed panel amplitude to track those, product analyzing data.
31 00:04:29.840 ⇒ 00:04:49.300 Venkata Prasad Krupananda: And also track user behavior, I can say, and also drive insights for product teams. And yeah, I really enjoy the intersection of data, product, and, you know, marketing analytics in this particular role. And yeah, I had a word with Uptam, I think last week, last Wednesday, and yes.
32 00:04:49.300 ⇒ 00:04:50.609 Venkata Prasad Krupananda: So here I am.
33 00:04:50.610 ⇒ 00:04:54.650 Venkata Prasad Krupananda: Going forward to the next round, and that’s a little bit about myself, I can say.
34 00:04:56.120 ⇒ 00:04:59.679 Awaish Kumar: Okay, so where are you currently based?
35 00:05:00.280 ⇒ 00:05:02.409 Venkata Prasad Krupananda: I’m based in Austin, Texas.
36 00:05:03.000 ⇒ 00:05:03.780 Awaish Kumar: Okay.
37 00:05:03.780 ⇒ 00:05:04.570 Venkata Prasad Krupananda: Yeah.
38 00:05:05.000 ⇒ 00:05:10.589 Awaish Kumar: So, like, so you have been, like, doing more of,
39 00:05:12.050 ⇒ 00:05:16.900 Awaish Kumar: In the last four and a half years, you mentioned we have been doing product analysis, data analysis…
40 00:05:17.000 ⇒ 00:05:23.539 Venkata Prasad Krupananda: Yes, I started off as a digital marketing analyst, and then right now, yes, for the last
41 00:05:23.710 ⇒ 00:05:28.069 Venkata Prasad Krupananda: I can say hardcore 3 years has been product marketing analyst.
42 00:05:29.350 ⇒ 00:05:30.090 Awaish Kumar: Okay.
43 00:05:30.960 ⇒ 00:05:31.610 Awaish Kumar: Boom.
44 00:05:33.780 ⇒ 00:05:37.720 Awaish Kumar: How… yeah, if… if you are given…
45 00:05:39.370 ⇒ 00:05:47.190 Awaish Kumar: a project, like, if you onboard, for example, in Brain Forge, and you are working for a client XYZ,
46 00:05:47.290 ⇒ 00:05:54.040 Awaish Kumar: They have some amplitude set up, they have some, Mixed panel setup.
47 00:05:55.330 ⇒ 00:05:55.804 Awaish Kumar: Monthly
48 00:05:56.510 ⇒ 00:06:02.569 Awaish Kumar: We don’t know, like, they’re… even they don’t know, like, the person who’s been working on these tools is, for example.
49 00:06:03.040 ⇒ 00:06:04.050 Awaish Kumar: Has left.
50 00:06:04.370 ⇒ 00:06:15.950 Awaish Kumar: So, all… Would you come… on board and start investigations, learn existing things. So what would be your approach?
51 00:06:17.510 ⇒ 00:06:29.419 Venkata Prasad Krupananda: In that situation, I would say, it’s a pretty challenging situation, though, first of all. And first thing is to get access to, you know, all the existing analytics workspace.
52 00:06:29.420 ⇒ 00:06:41.659 Venkata Prasad Krupananda: Or, get comfortable with the teammates, and also get to know them, and where they are at, when it comes to what they’re working on. Like you said, Amplitude and Mixpanel, and, start by
53 00:06:41.660 ⇒ 00:06:56.569 Venkata Prasad Krupananda: reviewing the event taxonomy or data structure, and I would look at how, you know, the events are named, or what properties are being tracked, and also
54 00:06:56.690 ⇒ 00:07:01.369 Venkata Prasad Krupananda: If there are stakeholders waiting for us for the decision, I would…
55 00:07:01.510 ⇒ 00:07:12.850 Venkata Prasad Krupananda: want to know what data is exactly needed by them, and whether they align with the product keys, or even user flows. And next, I would,
56 00:07:12.980 ⇒ 00:07:24.640 Venkata Prasad Krupananda: I would usually pull the track… pull the tracking plan, and also the documentation, basically, if it exists. If not, I would reconstruct it manually, and also by exploring the list of,
57 00:07:24.640 ⇒ 00:07:34.350 Venkata Prasad Krupananda: events, or the future upcoming events, and produce them to product features, and analyze the product features again. And that helps me understand what’s being tracked versus
58 00:07:34.350 ⇒ 00:07:46.520 Venkata Prasad Krupananda: what’s missing, or what is yet to be tracked. So, then I would think of data quality, if there is data involved, and checking for duplicate events, making sure the data is accurate for
59 00:07:46.520 ⇒ 00:07:55.259 Venkata Prasad Krupananda: further analysis, I would say, will help the senior management, or even stakeholders. So I would…
60 00:07:55.430 ⇒ 00:08:09.310 Venkata Prasad Krupananda: I would start off by getting comfortable with the data, and also with the team members, and what you guys are working on, and make sure we are on the same page to start off with.
61 00:08:10.690 ⇒ 00:08:11.550 Awaish Kumar: Okay.
62 00:08:12.750 ⇒ 00:08:19.080 Awaish Kumar: So So, for example, there’s a team…
63 00:08:20.180 ⇒ 00:08:25.710 Awaish Kumar: So, like, I’m the only person who is working on amplitude index panel. I left.
64 00:08:25.880 ⇒ 00:08:26.939 Awaish Kumar: the company.
65 00:08:27.120 ⇒ 00:08:28.230 Awaish Kumar: Now…
66 00:08:29.130 ⇒ 00:08:34.040 Awaish Kumar: I share the access keys with the team, so you can come in and log in to the platforms.
67 00:08:34.230 ⇒ 00:08:36.709 Awaish Kumar: You have access to all dashboards and,
68 00:08:37.350 ⇒ 00:08:43.330 Awaish Kumar: this stuff. Like, we are the admin now, whether… But, like,
69 00:08:43.640 ⇒ 00:08:47.630 Awaish Kumar: There’s no documentation, basically. There’s no definitions.
70 00:08:47.850 ⇒ 00:08:48.670 Venkata Prasad Krupananda: Paul.
71 00:08:50.280 ⇒ 00:08:56.390 Awaish Kumar: And, so… Yeah, so, like,
72 00:08:58.340 ⇒ 00:09:02.390 Awaish Kumar: the step to, like, get KPIs from…
73 00:09:02.600 ⇒ 00:09:11.989 Awaish Kumar: all the requirements from stakeholders is a kind of step where you want to optimize it, or create an ideal view of it, but the.
74 00:09:12.610 ⇒ 00:09:16.080 Awaish Kumar: The first problem is that whatever is working
75 00:09:16.220 ⇒ 00:09:19.639 Awaish Kumar: In whatever state that is, like, just to maintain it.
76 00:09:21.180 ⇒ 00:09:25.060 Venkata Prasad Krupananda: Yeah. So, if that is the case,
77 00:09:25.230 ⇒ 00:09:41.109 Venkata Prasad Krupananda: My first focus would be, probably, stabilizing the existing setup. Like, when I’m given access to all of those, data or, logins or anything, I would
78 00:09:41.200 ⇒ 00:09:53.310 Venkata Prasad Krupananda: want to stabilize the existing setup, first of all, before making any changes, so I would start by doing a full audit for the current amplitude or mixed panel projects, basically checking,
79 00:09:53.310 ⇒ 00:10:07.479 Venkata Prasad Krupananda: which event are still firing, and which ones are broken, and whether, you know, the data ingestion is consistent or not. And I would use the live event stream in both tools.
80 00:10:07.770 ⇒ 00:10:23.440 Venkata Prasad Krupananda: verify, that events are coming through in real time, and also match those, expected structure. And that is something I can think about, definitely. And then I would like to export, those event lists and create,
81 00:10:23.670 ⇒ 00:10:47.630 Venkata Prasad Krupananda: I can say a quick tracking inventory spreadsheet. I can say a basic spreadsheet on Excel, I can say, and just to document what’s there, and what is active, and what is not… what is unused. And also, if I notice any broken or missing events, I would flag those, and while working with other various teams, like engineering team, to confirm whether the tracking
82 00:10:47.680 ⇒ 00:11:04.010 Venkata Prasad Krupananda: code still exists, or, you know, in regards to the product. And also, I would set up those basic alerts or, on the dashboards to make sure everybody is on the same page, and everybody has been updated, with what I am working on.
83 00:11:04.290 ⇒ 00:11:12.360 Venkata Prasad Krupananda: I think that is something I can think of to start off with, when I’m the only person who’s given access to.
84 00:11:13.780 ⇒ 00:11:19.740 Awaish Kumar: Okay, and then, like… So, for example, now that you have set up the…
85 00:11:20.330 ⇒ 00:11:27.720 Awaish Kumar: tool, if we want to migrate data from the… the… Platform to some warehouse.
86 00:11:27.920 ⇒ 00:11:29.779 Awaish Kumar: Did you have any experience with that?
87 00:11:31.720 ⇒ 00:11:39.060 Venkata Prasad Krupananda: So basically, you’re asking, extracting and moving the data from these platforms to the warehouse, you mean…
88 00:11:39.280 ⇒ 00:11:43.249 Venkata Prasad Krupananda: And it can be anything, building a dashboard, or something like that.
89 00:11:43.350 ⇒ 00:11:44.440 Venkata Prasad Krupananda: Am I correct?
90 00:11:44.440 ⇒ 00:11:45.440 Awaish Kumar: Anything like that.
91 00:11:45.440 ⇒ 00:11:45.780 Venkata Prasad Krupananda: Yeah.
92 00:11:45.780 ⇒ 00:11:48.020 Awaish Kumar: It goes from a platform caller.
93 00:11:48.080 ⇒ 00:11:49.110 Venkata Prasad Krupananda: We’ll get home.
94 00:11:49.290 ⇒ 00:12:09.520 Venkata Prasad Krupananda: Yeah, so, I have handled that kind of setup a few times. So, in my previous roles, I can say, especially at McKinsey, and Adani Group as well, back in India, I worked on, integrating product analytics data from tools like Google Analytics, Amplitude, and also Mixpanel into data warehouses such as Snowflake and Redshift.
95 00:12:09.520 ⇒ 00:12:18.659 Venkata Prasad Krupananda: I wouldn’t consider myself an expert when it comes to Snowflake and Accustrative. I obviously did it with team member support and help, but I have done that.
96 00:12:19.100 ⇒ 00:12:26.240 Awaish Kumar: Okay, like, I want to know just what tools you have used, or what kind of setup you’ve had while moving data from
97 00:12:26.710 ⇒ 00:12:29.800 Awaish Kumar: these platforms to, for example, Snowflake.
98 00:12:30.440 ⇒ 00:12:40.590 Venkata Prasad Krupananda: When it comes to tools, we’ve used a mix of ETL tools and custom scripts, depending on the complexity, for example, that McNC, we use.
99 00:12:42.260 ⇒ 00:12:44.080 Awaish Kumar: Was it the team?
100 00:12:44.510 ⇒ 00:12:49.300 Awaish Kumar: Sorry? Engineer in the team, or was it you, yourself, who worked on more?
101 00:12:49.300 ⇒ 00:12:51.520 Venkata Prasad Krupananda: This was… this was myself, this was myself.
102 00:12:52.170 ⇒ 00:12:59.950 Venkata Prasad Krupananda: So, yeah, these tools I’ve worked on, like, I’ve worked on Fibetrand, Amplitude, Mixpanel, and also
103 00:13:00.280 ⇒ 00:13:14.979 Venkata Prasad Krupananda: intermediate level of Snowflake, and also I’ve handled schema mapping and incremental loads pretty smoothly. And also, at my previous company, I’ve built more custom pipeline using Python and SQL, and we would call
104 00:13:14.980 ⇒ 00:13:31.560 Venkata Prasad Krupananda: We’d call it an Amplitude Expert API, or Mixed Panel API to pull those data into JSON format, and also then process it through AWS S3, and also Athena before loading it into Redshift.
105 00:13:31.560 ⇒ 00:13:34.150 Venkata Prasad Krupananda: So yeah, I can think of those tools.
106 00:13:34.150 ⇒ 00:13:37.410 Venkata Prasad Krupananda: That I’ve worked on in my previous roles.
107 00:13:37.950 ⇒ 00:13:40.829 Awaish Kumar: Okay, and do you have any experience with UBT as well?
108 00:13:41.240 ⇒ 00:13:42.450 Venkata Prasad Krupananda: Can you repeat that?
109 00:13:43.250 ⇒ 00:13:45.729 Awaish Kumar: Do you have any experience with the DBT?
110 00:13:46.230 ⇒ 00:13:49.669 Venkata Prasad Krupananda: dbt, yes, I do have experience with dbt, mostly in context of
111 00:13:49.670 ⇒ 00:14:12.499 Venkata Prasad Krupananda: data transformation and, and, modeling after the data lands in the warehouse. So, for example, at McKinsey, once we moved, event data from amplitude to, this one, Snowflake, and also I used DBD to build modular SQL models, and also, basically cleaning, joining, and also
112 00:14:12.510 ⇒ 00:14:19.310 Venkata Prasad Krupananda: Aggregating the raw event tables into analytics performance dashboards or datasets.
113 00:14:19.790 ⇒ 00:14:22.009 Awaish Kumar: What are seeds in dbt?
114 00:14:22.390 ⇒ 00:14:23.510 Venkata Prasad Krupananda: What are seeds?
115 00:14:24.620 ⇒ 00:14:29.649 Venkata Prasad Krupananda: Seeds are, the CVS files that are,
116 00:14:30.800 ⇒ 00:14:36.480 Venkata Prasad Krupananda: I can say, filed up in a profile. Basically, it’s CSV files.
117 00:14:39.290 ⇒ 00:14:44.970 Awaish Kumar: Yeah, like, CC files, but, like, what is the concept of DBDC? It’s like… what…
118 00:14:44.970 ⇒ 00:14:56.159 Venkata Prasad Krupananda: So scenes in dbt are basically those static CSV files that you can upload into the dbt project, and also treat, like, data set tables.
119 00:14:56.390 ⇒ 00:14:58.490 Venkata Prasad Krupananda: That is where you extract the data from.
120 00:14:59.490 ⇒ 00:15:04.240 Awaish Kumar: Okay, so now that you have the data, this tool set up.
121 00:15:05.060 ⇒ 00:15:10.300 Awaish Kumar: And, for example, you did a, audit of your tool.
122 00:15:10.570 ⇒ 00:15:15.869 Awaish Kumar: How are you going to share that with your… With your client.
123 00:15:16.010 ⇒ 00:15:17.149 Awaish Kumar: the findings.
124 00:15:18.300 ⇒ 00:15:19.709 Awaish Kumar: What will be your…
125 00:15:19.710 ⇒ 00:15:21.570 Venkata Prasad Krupananda: Method of communication.
126 00:15:22.590 ⇒ 00:15:29.699 Venkata Prasad Krupananda: Okay, the first thing is, when it comes to method of communication, I would say,
127 00:15:29.920 ⇒ 00:15:42.650 Venkata Prasad Krupananda: I would handle it by keeping it structured, but conversational. First, I would summarize those key findings in a clear visual format, usually a Power BI dashboard or a Tableau dashboard, and also… or a…
128 00:15:42.650 ⇒ 00:15:54.679 Venkata Prasad Krupananda: or even a slide deck that highlights the main issues, and also, like, missing events, and also inconsistent tracking. And then I prepare a written summary, because a written summary is very important for them to
129 00:15:54.680 ⇒ 00:16:05.989 Venkata Prasad Krupananda: have a document of, and also maybe in Confluence or Google Doc, and also that includes a lot of technical details, and also screenshots, and recommended next steps.
130 00:16:05.990 ⇒ 00:16:14.699 Venkata Prasad Krupananda: And, I think those are the two main things that I can think of when it comes to communicating the data to the stakeholders.
131 00:16:17.410 ⇒ 00:16:24.520 Awaish Kumar: Yeah, but, like, I more want to understand how you keep up the engagement with the client. So, for example.
132 00:16:24.520 ⇒ 00:16:25.550 Venkata Prasad Krupananda: You…
133 00:16:25.660 ⇒ 00:16:26.700 Awaish Kumar: got…
134 00:16:26.870 ⇒ 00:16:34.200 Awaish Kumar: Like, working in a company is, different than, you know, like, in a product… in a product-based company.
135 00:16:34.200 ⇒ 00:16:34.670 Venkata Prasad Krupananda: Beautiful.
136 00:16:35.030 ⇒ 00:16:36.569 Awaish Kumar: Again, consistency.
137 00:16:36.570 ⇒ 00:16:36.950 Venkata Prasad Krupananda: Yes, sir.
138 00:16:36.950 ⇒ 00:16:42.700 Awaish Kumar: You know, this company, you… you are, like, just… you meet and stand up, you say a few words, and…
139 00:16:43.490 ⇒ 00:16:44.990 Venkata Prasad Krupananda: Yeah, correct, correct.
140 00:16:45.820 ⇒ 00:16:49.610 Awaish Kumar: But, like, in a consultancy, when you are with a client.
141 00:16:49.620 ⇒ 00:16:51.080 Venkata Prasad Krupananda: You know, it’s part of…
142 00:16:51.080 ⇒ 00:16:59.070 Awaish Kumar: work to do, who might not. Maybe we don’t… we won’t have stand-ups daily with the client, or… Yes.
143 00:17:01.080 ⇒ 00:17:14.429 Awaish Kumar: You got to… you have to work on something like this auditing the tool, which might require more than a day, or two, or three, to just come up with some findings and share.
144 00:17:14.819 ⇒ 00:17:18.179 Awaish Kumar: Okay. So how do you keep up the engagement with the client?
145 00:17:21.099 ⇒ 00:17:44.810 Venkata Prasad Krupananda: Okay, when it comes to… I know, I know we spoke about… even me, Utam, mentioned this a lot, that there are no DH stand-ups, it’s gonna… it’s gonna be a weekly communication or anything, so that’s a really good point, and honestly, that’s something I’ve learned to be, intentional about. So, what I usually do is set up a lightweight async communication rhythm. For example, I’d send a…
146 00:17:44.890 ⇒ 00:18:06.169 Venkata Prasad Krupananda: I can say a daily or every other day update, from my side. It doesn’t have to be a one-on-one meeting or something. It can also be something on Slack, because I know you guys use Slack, or just an email, just a short note, like, you know, here’s what I completed, here’s what I’m working on, and here’s what
147 00:18:06.180 ⇒ 00:18:16.559 Venkata Prasad Krupananda: data we have gathered for your needs, and also, if it’s a longer project, I can say… I can think of, like, a tool audit. I’ll also share a progress
148 00:18:16.680 ⇒ 00:18:24.579 Venkata Prasad Krupananda: tracker, maybe a simple Google Sheet or a Notion board, where the client can see what stage
149 00:18:24.720 ⇒ 00:18:32.990 Venkata Prasad Krupananda: I’m working on when it comes to, each, steps or the process in the…
150 00:18:33.080 ⇒ 00:18:51.560 Venkata Prasad Krupananda: task, and also that keeps them in the loop, I feel, without needing for those regular, constant meetings. So, yeah, so when it comes to visualizing it, to be quicker, I would use Loom to record those videos, short videos, walking through
151 00:18:51.640 ⇒ 00:19:00.210 Venkata Prasad Krupananda: early findings or visual processes. So, yeah, that is basically what I can, think of, and what I’ve done previously.
152 00:19:02.360 ⇒ 00:19:06.639 Awaish Kumar: Okay, so… You will be with the…
153 00:19:06.900 ⇒ 00:19:10.459 Awaish Kumar: communicating through Slack,
154 00:19:11.950 ⇒ 00:19:13.710 Awaish Kumar: Zoom.
155 00:19:13.710 ⇒ 00:19:14.060 Venkata Prasad Krupananda: So.
156 00:19:14.650 ⇒ 00:19:20.840 Awaish Kumar: Sorry, sorry, the meetings, and so, like, what I’m trying to get is, like.
157 00:19:22.120 ⇒ 00:19:30.400 Awaish Kumar: Like, you… you are communicating daily basis by giving updated status, like, status updates, like, that’s…
158 00:19:30.400 ⇒ 00:19:31.650 Venkata Prasad Krupananda: That’s like a…
159 00:19:31.750 ⇒ 00:19:34.909 Awaish Kumar: Like, could be a good idea.
160 00:19:34.910 ⇒ 00:19:35.650 Venkata Prasad Krupananda: Okay.
161 00:19:38.190 ⇒ 00:19:43.220 Awaish Kumar: But I’m… I’m not sure, like, how… how do you think about it? Like, sometimes it’s just, like, the…
162 00:19:43.920 ⇒ 00:19:44.239 Venkata Prasad Krupananda: I mean.
163 00:19:44.240 ⇒ 00:19:48.680 Awaish Kumar: I did something tangible, like, which you can share?
164 00:19:48.680 ⇒ 00:19:51.760 Venkata Prasad Krupananda: Yes, yes, obviously. So,
165 00:19:52.520 ⇒ 00:20:01.350 Venkata Prasad Krupananda: For example, there are projects, or there are tasks that go for days together, like you said, like mentioned. So, instead of just saying,
166 00:20:02.090 ⇒ 00:20:09.799 Venkata Prasad Krupananda: it’s gonna be, like, what I feel is, if you don’t have the daily conversation, or even a daily message, it’s gonna be, like.
167 00:20:10.270 ⇒ 00:20:24.920 Venkata Prasad Krupananda: we didn’t work on anything today, just, you know, the client might feel, okay, there’s nothing productive happening today. So, instead, you can just let them know, you know, hey, I’ve been working on this, even if I didn’t
168 00:20:25.030 ⇒ 00:20:33.190 Venkata Prasad Krupananda: get any outcome. I can say, hey, we tried doing this, or show them a proof or something, just like what I mentioned. Yeah, go ahead.
169 00:20:33.190 ⇒ 00:20:39.640 Awaish Kumar: How… okay, so how good… like, I would, then say, how good are you with, preparing the…
170 00:20:39.950 ⇒ 00:20:45.140 Awaish Kumar: Presentations, for example, like, exec-level slides.
171 00:20:45.610 ⇒ 00:20:47.940 Venkata Prasad Krupananda: Yeah, yeah, when it comes to…
172 00:20:48.130 ⇒ 00:20:51.020 Venkata Prasad Krupananda: When it comes to dashboards, yes, I can say…
173 00:20:51.210 ⇒ 00:21:03.469 Venkata Prasad Krupananda: I am, with Power BI, I’m almost an expert in building dashboards, and I’ve not used a lot of Tableau. I started off with Power BI, and I’ve done a lot of PowerPoint presentations.
174 00:21:03.470 ⇒ 00:21:04.370 Awaish Kumar: Oh, thank God.
175 00:21:04.610 ⇒ 00:21:07.509 Awaish Kumar: It’s just PowerPoint, for example.
176 00:21:07.830 ⇒ 00:21:08.410 Venkata Prasad Krupananda: Okay.
177 00:21:08.410 ⇒ 00:21:13.349 Awaish Kumar: You worked on something, you did your audit, like, now your findings, right?
178 00:21:14.150 ⇒ 00:21:16.819 Awaish Kumar: You have to share that with the, maybe, CEO.
179 00:21:18.900 ⇒ 00:21:24.790 Awaish Kumar: So how, like, then it needs to be a… Like, exact level,
180 00:21:25.090 ⇒ 00:21:29.610 Awaish Kumar: PowerPoint slide, which basically shares all your findings.
181 00:21:29.900 ⇒ 00:21:30.370 Venkata Prasad Krupananda: Yes.
182 00:21:30.370 ⇒ 00:21:31.760 Awaish Kumar: Lucille.
183 00:21:31.890 ⇒ 00:21:34.180 Venkata Prasad Krupananda: So how are you preparing?
184 00:21:34.180 ⇒ 00:21:35.600 Awaish Kumar: No slides, so…
185 00:21:36.120 ⇒ 00:21:53.800 Venkata Prasad Krupananda: So, yeah, it is a very high-level and insight-focused, when it comes to executive level. So, the goal, is to be on that executive desk, is… is… is… is not show all the data. That is what I feel. It’s to tell a page story.
186 00:21:53.800 ⇒ 00:22:01.399 Awaish Kumar: Oh, like, what… what is… like, have you worked on that? Have you worked creating those PPTs before?
187 00:22:01.400 ⇒ 00:22:02.080 Venkata Prasad Krupananda: Yes.
188 00:22:02.160 ⇒ 00:22:13.799 Awaish Kumar: Have you… Do you have the… the… like, the… I would say, What’d you… Yeah, I would say, like.
189 00:22:14.520 ⇒ 00:22:18.770 Awaish Kumar: The insights to look at, like, okay, these are the numbers which are…
190 00:22:19.100 ⇒ 00:22:25.250 Awaish Kumar: Like, there will be chunk, like, a good chunk of information you might have got from your order.
191 00:22:25.360 ⇒ 00:22:31.450 Awaish Kumar: Like, then to decide, like, these are the ones which should be… which could be the meaningful for the…
192 00:22:31.670 ⇒ 00:22:45.239 Awaish Kumar: for the CEO to look at, like, not everything is… the CEO might be interested in. What should be the content, number one, then what should be the design? So, like, have you worked on… on doing that, or whatever?
193 00:22:45.490 ⇒ 00:23:02.779 Venkata Prasad Krupananda: Yes, I have worked on that. When it comes to prioritization really matters when it comes to PowerPoint presentation, especially in that executive level. So, what I usually do is start by mapping every finding back to the business goal, or KPIs, so these two
194 00:23:02.780 ⇒ 00:23:15.550 Venkata Prasad Krupananda: when you’re aligning your data with the business goals and your KPIs that matter for the executive level, this is what I feel. For example, you know, if the CEO is focused on
195 00:23:15.550 ⇒ 00:23:25.119 Venkata Prasad Krupananda: user retention, or a feature adoption, I’ll filter the insights that directly influence those two metrics.
196 00:23:25.260 ⇒ 00:23:29.630 Venkata Prasad Krupananda: So that… it’s basically being on point with
197 00:23:29.950 ⇒ 00:23:36.580 Venkata Prasad Krupananda: the required answer, and the data, and explanation for that. So they… so then they’ll look
198 00:23:36.580 ⇒ 00:23:53.279 Venkata Prasad Krupananda: Then I can look at the impact versus effort, and what changes could drive the biggest improvement when it comes to… that process starts off automatically after the data has been shown to the executive level. So this is what I can think of. It should be on point.
199 00:23:53.320 ⇒ 00:23:58.149 Venkata Prasad Krupananda: And… Yeah, you should basically answer the question, or what they’re looking for.
200 00:23:58.620 ⇒ 00:24:00.109 Awaish Kumar: Okay.
201 00:24:00.230 ⇒ 00:24:05.720 Awaish Kumar: So… Yeah.
202 00:24:06.360 ⇒ 00:24:12.300 Awaish Kumar: And how good are you with, like, like, and what are your… Basically, the…
203 00:24:12.890 ⇒ 00:24:20.960 Awaish Kumar: What is… what is your more… Given the… Opportunity to work on
204 00:24:21.250 ⇒ 00:24:31.119 Awaish Kumar: data analyst side, data… engineering side, or data analysis side? Like, what do you think…
205 00:24:31.860 ⇒ 00:24:39.610 Awaish Kumar: Where would you would like to work on? Which part of the flow you… you find yourself the.
206 00:24:39.610 ⇒ 00:24:41.580 Venkata Prasad Krupananda: Yeah, because…
207 00:24:41.640 ⇒ 00:24:59.039 Venkata Prasad Krupananda: since I started off as a marketing analyst, and worked towards and through data analyst roles, and also used a lot of data analyst, like, tools and platforms, I think I can say my biggest strength and interests lean more towards data analysis and product analytics.
208 00:24:59.040 ⇒ 00:25:15.780 Venkata Prasad Krupananda: So I really enjoy the part where I can, you know, transform around raw data, clean it, model it using SQL and other Python and other softwares. And also, I’ve worked on… I’ve worked with Amplitude and also Mixpanel, and also dashboard building is one of my favorites. So I can say.
209 00:25:16.020 ⇒ 00:25:35.220 Venkata Prasad Krupananda: playing around with that, tools like Google Analytics to analyze the user behavior based on the product integration, and I can say I’m 90% more towards data analysis, and then that said, I do have a decent understanding of data engineering workflow, because I’ve
210 00:25:35.360 ⇒ 00:25:54.780 Venkata Prasad Krupananda: obviously collaborated with other teams in my previous roles, and also built ETL pipelines, and, you know, using various other tools like AWS Redshift, Snowflake, and also maintained that data quality before it matched the analysis layer. So I can say I have a touch of
211 00:25:54.880 ⇒ 00:26:00.300 Venkata Prasad Krupananda: data engineering background, but I lean more towards data analysis role.
212 00:26:01.990 ⇒ 00:26:06.480 Awaish Kumar: Okay, and yeah, so, like,
213 00:26:07.750 ⇒ 00:26:14.069 Awaish Kumar: One of the final questions would be to ask, like, how…
214 00:26:16.360 ⇒ 00:26:23.850 Awaish Kumar: Who are you with the context switching? Like, you… like, here, you might have to work on more than one…
215 00:26:24.390 ⇒ 00:26:25.070 Awaish Kumar: Huh.
216 00:26:25.660 ⇒ 00:26:29.610 Venkata Prasad Krupananda: More than… more than… what? Your voice is a little breaking. Can you say that again? More than…
217 00:26:29.610 ⇒ 00:26:31.099 Awaish Kumar: More than one client.
218 00:26:31.100 ⇒ 00:26:32.950 Venkata Prasad Krupananda: Client, okay, yeah, okay,
219 00:26:33.430 ⇒ 00:26:38.330 Awaish Kumar: So… how good are you with context switching? So…
220 00:26:38.880 ⇒ 00:26:44.790 Awaish Kumar: You might be working, like, for half a day, you work on one client, maybe as an hour.
221 00:26:45.020 ⇒ 00:26:45.920 Awaish Kumar: Would I…
222 00:26:46.040 ⇒ 00:26:52.339 Awaish Kumar: Another, or something like that, or… there could be some urgent issues, and you have been called to…
223 00:26:52.720 ⇒ 00:26:54.699 Awaish Kumar: Help someone, like, things like that.
224 00:26:54.940 ⇒ 00:26:55.930 Awaish Kumar: So…
225 00:26:56.090 ⇒ 00:27:11.500 Venkata Prasad Krupananda: Yeah, contact switching is one of the main character when it comes to this, so that’s a fair question. Honestly, I think I’ve had quite a bit of practice with that. At McKinsey, I was often supporting two to three clients’ projects in parallel, so, each with…
226 00:27:11.500 ⇒ 00:27:19.329 Venkata Prasad Krupananda: Different data stacks, and also timelines, and… and what really helped me was being organized and,
227 00:27:19.570 ⇒ 00:27:26.200 Venkata Prasad Krupananda: basically organized and intentional about how I manage that time, and I usually start my day by
228 00:27:26.710 ⇒ 00:27:37.560 Venkata Prasad Krupananda: Of course, prioritizing those tasks, which has to be done first, and across the clients, and I use, again, going through those tasks and maintaining that rapport with each client.
229 00:27:37.600 ⇒ 00:27:47.740 Venkata Prasad Krupananda: on a day-to-day basis is very important, and then, yes, that is, something that I’ve worked previously when it comes to contact switching and,
230 00:27:48.090 ⇒ 00:27:50.429 Venkata Prasad Krupananda: I’ve handled that well, I can say.
231 00:27:51.640 ⇒ 00:27:53.620 Awaish Kumar: Okay, yeah, I think that’s…
232 00:27:53.620 ⇒ 00:27:54.040 Venkata Prasad Krupananda: And.
233 00:27:54.040 ⇒ 00:27:57.479 Awaish Kumar: That’s my side. If you have any other questions, you can…
234 00:27:58.450 ⇒ 00:28:18.129 Venkata Prasad Krupananda: So, I just want to add on this for the last question. So, communication is very important. I’ve learned that in the last couple of tasks that I have had, and I know I’ll be deep into analysis for one client, and I’ll update the others on my availability to progress ahead of time. So, communication is the main thing to maintain the repo, and…
235 00:28:18.130 ⇒ 00:28:21.470 Venkata Prasad Krupananda: The base of… Contact switching, I would say.
236 00:28:21.940 ⇒ 00:28:25.230 Venkata Prasad Krupananda: So, yeah, other than that,
237 00:28:26.090 ⇒ 00:28:28.789 Venkata Prasad Krupananda: Did you ask if I had any questions about the role?
238 00:28:28.790 ⇒ 00:28:34.849 Awaish Kumar: Yeah, that’s it from my side. If you… now we still have 5 minutes if you want to ask anything.
239 00:28:35.540 ⇒ 00:28:52.100 Venkata Prasad Krupananda: No, I don’t have any questions today, because I spoke well in depth with Utam. I think he answered most of my questions, and also, since we’re running out of time now, these are 4 minutes, I think I’m good as of now. I have another meeting tomorrow with Amber, I believe.
240 00:28:52.100 ⇒ 00:28:53.080 Awaish Kumar: I’m… yeah.
241 00:28:53.080 ⇒ 00:28:56.349 Venkata Prasad Krupananda: She might be, like, able to give you more…
242 00:28:56.410 ⇒ 00:29:01.149 Awaish Kumar: On our product analytics side, or what kind of work she’s doing.
243 00:29:01.150 ⇒ 00:29:02.880 Venkata Prasad Krupananda: So she works on product analytics?
244 00:29:03.890 ⇒ 00:29:07.709 Awaish Kumar: Yeah, she’s kind of a product analytics, data analytics kind of…
245 00:29:07.710 ⇒ 00:29:08.630 Venkata Prasad Krupananda: Okay, okay.
246 00:29:09.120 ⇒ 00:29:10.290 Awaish Kumar: all over.
247 00:29:10.290 ⇒ 00:29:13.410 Venkata Prasad Krupananda: But I’m more like a EDE person.
248 00:29:13.410 ⇒ 00:29:15.560 Awaish Kumar: So… Yeah.
249 00:29:15.720 ⇒ 00:29:20.970 Awaish Kumar: what I want, like, what I wanted is that, like, in our teams right now, we have,
250 00:29:22.500 ⇒ 00:29:40.750 Awaish Kumar: need for a person who can basically do the analysis and basically share that with client and keep the engagement. So normally we lose track of, like, while we are working on AED side, and that’s all the back-end work, and normally we…
251 00:29:41.100 ⇒ 00:29:44.610 Awaish Kumar: Apart from just giving an update that we.
252 00:29:44.610 ⇒ 00:29:45.180 Venkata Prasad Krupananda: Yes, yes.
253 00:29:45.180 ⇒ 00:29:47.420 Awaish Kumar: There’s nothing much.
254 00:29:47.620 ⇒ 00:29:52.110 Awaish Kumar: For… for a client to get interested in that engagement.
255 00:29:52.220 ⇒ 00:29:55.979 Awaish Kumar: So only the analyst part is the one where you basically
256 00:29:56.100 ⇒ 00:30:01.280 Awaish Kumar: Figure out some great insights for… from their data, and share… and make them interested.
257 00:30:01.560 ⇒ 00:30:02.489 Venkata Prasad Krupananda: E is correct.
258 00:30:02.860 ⇒ 00:30:04.319 Awaish Kumar: In the, in the work we are doing.
259 00:30:04.570 ⇒ 00:30:09.820 Awaish Kumar: So, yeah, that’s a very important part of the communication, keeping up the engagement.
260 00:30:10.030 ⇒ 00:30:12.519 Awaish Kumar: It’s an important part of the…
261 00:30:12.570 ⇒ 00:30:16.220 Venkata Prasad Krupananda: The role, basically. Yeah, for this role as well, yeah.
262 00:30:17.010 ⇒ 00:30:29.609 Venkata Prasad Krupananda: Okay, yeah. So yeah, about the role, I’m well informed, and also, yeah, like we spoke, yes, this aligns very well with my background as well. So, yeah, I don’t have any questions as of now.
263 00:30:29.610 ⇒ 00:30:31.269 Awaish Kumar: Okay, thank you.
264 00:30:31.270 ⇒ 00:30:34.170 Venkata Prasad Krupananda: Yeah, thank you so much for the time, Avish.
265 00:30:34.760 ⇒ 00:30:38.740 Awaish Kumar: Okay, yeah, let us know if you have anything in the meantime.
266 00:30:38.740 ⇒ 00:30:42.490 Venkata Prasad Krupananda: I’ll just shoot an email to you or Utham, I’ll just get in touch with you.
267 00:30:43.800 ⇒ 00:30:48.359 Awaish Kumar: Yeah, after that, like, Ricoh will be going to contact you on the next…
268 00:30:48.360 ⇒ 00:30:51.420 Venkata Prasad Krupananda: Yeah, yes, definitely, okay, I’ll be looking forward to that.
269 00:30:52.260 ⇒ 00:30:53.789 Venkata Prasad Krupananda: Yeah, thank you. Bye-bye.