Meeting Title: Data Engineer Interview with Vashdev Date: 2025-07-23 Meeting participants: Amber Lin, Vashdev Heerani
WEBVTT
1 00:00:28.930 ⇒ 00:00:29.960 Amber Lin: Hi! There!
2 00:00:30.250 ⇒ 00:00:31.870 Vashdev Heerani: Hello! Hello!
3 00:00:36.310 ⇒ 00:00:37.720 Amber Lin: Nice to meet you.
4 00:00:37.990 ⇒ 00:00:40.579 Vashdev Heerani: Nice to meet you, too. Good morning.
5 00:00:40.720 ⇒ 00:00:44.119 Amber Lin: Good morning. It’s probably nighttime for you right.
6 00:00:44.340 ⇒ 00:00:46.180 Vashdev Heerani: Yeah. It’s nighttime for me.
7 00:00:46.180 ⇒ 00:00:50.870 Amber Lin: I see I know you talked to Utam already. How was that.
8 00:00:52.200 ⇒ 00:00:52.720 Vashdev Heerani: Sorry!
9 00:00:53.290 ⇒ 00:00:55.420 Amber Lin: Did you talk to Utam already.
10 00:00:56.640 ⇒ 00:01:06.329 Vashdev Heerani: Yeah, it. It was very nice he was. He’s very kind. Person. Yeah, I I really enjoyed talking to him.
11 00:01:06.780 ⇒ 00:01:07.979 Amber Lin: That’s awesome.
12 00:01:08.620 ⇒ 00:01:15.329 Amber Lin: Okay, I’m just looking at the notes that that Utam took.
13 00:01:16.463 ⇒ 00:01:18.010 Amber Lin: Let me
14 00:01:18.440 ⇒ 00:01:31.120 Amber Lin: quickly pull it up so quickly introduction for myself. So my name is Amber. I’m a project manager on this team. And I’ve joined around. Let’s say 4.
15 00:01:32.010 ⇒ 00:01:38.990 Amber Lin: I joined around early March, so it’s been like 4 more, 4 or a little bit more months now.
16 00:01:39.380 ⇒ 00:01:39.930 Vashdev Heerani: Oh!
17 00:01:39.930 ⇒ 00:01:47.380 Amber Lin: And so I mostly help people when I interview them. I help them understand what it’s like to work
18 00:01:47.520 ⇒ 00:01:48.720 Amber Lin: in the team.
19 00:01:49.100 ⇒ 00:01:50.750 Vashdev Heerani: And so.
20 00:01:52.830 ⇒ 00:01:55.069 Amber Lin: I think, for this interview.
21 00:01:56.170 ⇒ 00:02:04.799 Amber Lin: I know Uta Morty did the screening interview. I know he asked you about your career goals and then professional strengths, and things that you’re
22 00:02:05.690 ⇒ 00:02:07.330 Amber Lin: not interested in.
23 00:02:07.590 ⇒ 00:02:08.990 Amber Lin: And
24 00:02:12.190 ⇒ 00:02:24.799 Amber Lin: I think today is mostly to. I wanted to get an get an understanding of each company you worked at, and to just get more details on that, and then, after that, feel free to ask me any questions about what it’s like to work
25 00:02:25.363 ⇒ 00:02:27.030 Amber Lin: to work in the company.
26 00:02:28.340 ⇒ 00:02:41.180 Vashdev Heerani: Okay, so let me introduce myself. I did. I did graduation back in 2,017. Then I started working, writing back in code
27 00:02:41.683 ⇒ 00:03:06.296 Vashdev Heerani: for for a company in the Islamaba, the capital of the Pakistan for I worked with them around 8 months, and then I I always wanted to work on the data science machine learning kind of stuff. So I move myself to to join another company where I I I write models for the machine learning data, science,
28 00:03:06.940 ⇒ 00:03:08.170 Vashdev Heerani: kind of stroke.
29 00:03:08.340 ⇒ 00:03:15.569 Vashdev Heerani: And then back in 2,019, I joined another company where I started working as a data engineer.
30 00:03:15.700 ⇒ 00:03:34.780 Vashdev Heerani: So they they gave me the client. They they outsource me with the the Us. Client there, where they they were mainly working on the presence inmates data. So they they cover almost 17%, 70% of us and Canada
31 00:03:35.593 ⇒ 00:03:53.479 Vashdev Heerani: inmates. And they, they, my, my goal was to to get the data from a different data sources and then clean their data according to their business requirement. And then move that data to to to the data warehouse, where and other teams
32 00:03:53.510 ⇒ 00:03:59.449 Vashdev Heerani: we’re responsible to create a dashboard for for the for the higher management. So.
33 00:03:59.940 ⇒ 00:04:15.500 Vashdev Heerani: Yes. So I I’ve been working with them around 5 years. So I I created a different data steam for them. Initially, we create, we created, using the bash scripts. Then then, as far.
34 00:04:15.590 ⇒ 00:04:39.824 Vashdev Heerani: then, Hado, then then, lastly, I work on the snowflake, and and then I I work in the informatica systems. So I, I created a different streams, data streams for them. The the reason for creating a different streams was was, they have a different data source. And they don’t want want to create a
35 00:04:40.570 ⇒ 00:04:51.139 Vashdev Heerani: the process data and the single sources. So they created a different data sources. So I, I was responsible to create a a different data data stream for them.
36 00:04:52.460 ⇒ 00:04:56.690 Amber Lin: I see, okay, sounds good.
37 00:04:57.150 ⇒ 00:04:57.780 Vashdev Heerani: And like.
38 00:04:57.780 ⇒ 00:05:07.169 Vashdev Heerani: now, yeah, right now, I am doing a master as well in the in the data science, and particularly on the Gen. Jen, AI. And Llms.
39 00:05:07.730 ⇒ 00:05:08.310 Amber Lin: Hmm.
40 00:05:08.620 ⇒ 00:05:09.429 Amber Lin: Let’s see.
41 00:05:09.900 ⇒ 00:05:23.740 Amber Lin: Okay, so sorry. Just to clarify. I just wanna document it down. I I’m just looking at your resume. You said the last firm you looked at was worked out, was called 10 pearls
42 00:05:24.849 ⇒ 00:05:28.580 Amber Lin: and then you said you were hired to do
43 00:05:29.069 ⇒ 00:05:34.550 Amber Lin: you just help me sum it up in a very short sentence. I just want to be able to write that down.
44 00:05:34.860 ⇒ 00:05:38.610 Vashdev Heerani: Okay, i i i was hired to create a data pipeline for them.
45 00:05:39.120 ⇒ 00:05:47.330 Amber Lin: Data pipeline. Okay? And then I guess, what accomplishments were you most proud of? At that job?
46 00:05:48.190 ⇒ 00:06:17.069 Vashdev Heerani: So at that job they they were using Aws service, which was very costly. So what I did, I actually I actually reduced that cost by using another source with the, with the with the open source project. So I I what I did, I actually, I actually separated storage and computation separately. So what about separately?
47 00:06:17.160 ⇒ 00:06:24.999 Vashdev Heerani: So that was my proud. So we we we saved around 5,000 per month dollar us.
48 00:06:25.000 ⇒ 00:06:26.760 Amber Lin: Hmm! Wow!
49 00:06:27.110 ⇒ 00:06:27.510 Vashdev Heerani: Yeah.
50 00:06:28.510 ⇒ 00:06:51.880 Vashdev Heerani: so that that was my proud work. And actually, I I created an other another cost, saving stuff as well, which was actually we. We used Aws service, which, which initially was up for all 20 for all day, like 24 h. But our system use it for 8 h, so.
51 00:06:52.610 ⇒ 00:06:59.829 Vashdev Heerani: 1616 extra hours were was, was like our system was up, but it was not in the use, so.
52 00:07:00.540 ⇒ 00:07:19.090 Vashdev Heerani: But but because, aws yeah, our Amazon charge on when the machine is up. So what I did, I actually created a automate system which actually trigger, or which check, if there is no usage of that service, it automatically shut down that that.
53 00:07:19.090 ⇒ 00:07:20.190 Amber Lin: Who’s an admin.
54 00:07:20.190 ⇒ 00:07:20.560 Vashdev Heerani: I see.
55 00:07:20.560 ⇒ 00:07:21.080 Amber Lin: Yes.
56 00:07:21.080 ⇒ 00:07:27.339 Vashdev Heerani: Again next day. It it it shut it. It start up that machines and start working on that.
57 00:07:27.340 ⇒ 00:07:29.759 Amber Lin: Wow, okay, that’s really great.
58 00:07:30.100 ⇒ 00:07:30.760 Vashdev Heerani: Yeah.
59 00:07:31.500 ⇒ 00:07:34.729 Amber Lin: A lot of our clients look for that type of cost savings.
60 00:07:34.730 ⇒ 00:07:37.589 Vashdev Heerani: Yeah, 1 1 more thing. I did.
61 00:07:37.750 ⇒ 00:07:53.330 Vashdev Heerani: So what actually, I did, I. So I I mostly work with the data. So and I, I like the client was very very much into the clean cleanness of the data.
62 00:07:53.450 ⇒ 00:08:02.960 Vashdev Heerani: So what I did, I actually created a task that take the data on the daily basis and send me the notification via email or via teams.
63 00:08:03.330 ⇒ 00:08:10.640 Vashdev Heerani: So when there is any issue, so I I get the notification. And I resolve before client, notice that.
64 00:08:11.120 ⇒ 00:08:14.200 Vashdev Heerani: Stuff. So this is another thing.
65 00:08:14.200 ⇒ 00:08:16.489 Amber Lin: That’s awesome created.
66 00:08:16.630 ⇒ 00:08:18.199 Amber Lin: It’s system.
67 00:08:21.530 ⇒ 00:08:30.230 Amber Lin: that’s awesome. Okay? I think the next question I have is kind of on the flip side. So what were some low points during that job.
68 00:08:32.068 ⇒ 00:08:34.010 Vashdev Heerani: Can you repeat it again.
69 00:08:34.289 ⇒ 00:08:50.119 Amber Lin: What were some low points during that job? Because you just said a lot of high points. And that’s awesome. We want to hear about what cause every job has this low points, and what what was it like there? So when did it not go? Well, when was it really really tough?
70 00:08:51.770 ⇒ 00:08:58.049 Vashdev Heerani: Yeah, sometime. It. It was like I. I created a system which
71 00:08:58.510 ⇒ 00:09:05.139 Vashdev Heerani: which was around like it was rolling around the 3 platforms like aws, azure.
72 00:09:05.140 ⇒ 00:09:05.540 Amber Lin: Oh!
73 00:09:05.540 ⇒ 00:09:16.080 Vashdev Heerani: Sequel. So it was very tough to handle like like handle for for one for one person or 2 person. So
74 00:09:17.105 ⇒ 00:09:28.700 Vashdev Heerani: and and the the stuff was very, very tough, like it was Docker I system, and the Cicd was also implemented. So when we hire a
75 00:09:29.600 ⇒ 00:09:33.650 Vashdev Heerani: 3 or 4 people to work on those by my side.
76 00:09:33.770 ⇒ 00:09:41.209 Vashdev Heerani: So they found the difficulty and the documentation. It it I I have created everything. I have documented everything
77 00:09:41.621 ⇒ 00:10:03.380 Vashdev Heerani: but but they they thought that this documentation is is very complicated or the system is very complicated. So we, we need a time to process all these the the kind of stuff. So this happened happened in my my in my job. So what I did, I actually simplified everything like
78 00:10:03.380 ⇒ 00:10:24.480 Vashdev Heerani: like the documentation the the systems that that use a multiple platform. So I reduce that plate the the platform dependency to to the single platform. So multiple dependency was reduced so initially that that was my my low point. But I I also improve, improved that that thing as well.
79 00:10:25.440 ⇒ 00:10:34.479 Amber Lin: Okay, wait. So you guys hired another like 3 to 4 people to do the job. Were they helpful in the end? Cause I know you said they were quite confused.
80 00:10:35.540 ⇒ 00:10:49.230 Vashdev Heerani: Yeah, yeah. So they they were like, when we then we we simplify our documentation. And I, I had a session. I had a multiple session with them on each and every component within.
81 00:10:49.230 ⇒ 00:10:49.730 Amber Lin: Oh!
82 00:10:49.730 ⇒ 00:11:12.620 Vashdev Heerani: So what I did. I actually I had a session with them. Then I I gave them the documentation access to to them. And then I asked them if if you feel any difficulty to understand those document documentation with my session, so whenever they they point out, pointed out that we, we find a difficulty in in this particular.
83 00:11:12.970 ⇒ 00:11:13.320 Amber Lin: Section.
84 00:11:13.320 ⇒ 00:11:16.500 Vashdev Heerani: So I improved that that kind of say, sections.
85 00:11:16.920 ⇒ 00:11:20.049 Amber Lin: Oh, okay, were you managing those 3 to 4 people?
86 00:11:20.790 ⇒ 00:11:24.670 Vashdev Heerani: Yes, I I did. I did manage those people.
87 00:11:25.043 ⇒ 00:11:25.790 Amber Lin: I see.
88 00:11:27.410 ⇒ 00:11:47.880 Amber Lin: Okay, sounds good. And I think the next question is, who were the people you worked with? I know you mentioned the 3 to 4 people you were managing. What is it like? What kind of peers did you have? What kind of managers did you have. Can you give me a sense of what the people are like.
89 00:11:51.106 ⇒ 00:12:01.260 Vashdev Heerani: So so the manager that I had the technical lead that I had were also a technical like they they they, the manager, was also technical.
90 00:12:01.770 ⇒ 00:12:02.130 Amber Lin: Wow!
91 00:12:02.130 ⇒ 00:12:11.339 Vashdev Heerani: I was management. I was managed by them, and and and the the thing that I I led was was also like
92 00:12:11.833 ⇒ 00:12:27.569 Vashdev Heerani: like we. We had a conversation on the daily basis in the start, like a standard meeting, where we discuss all the all the outcome of the yesterday and the planning for today. And then we have a
93 00:12:27.570 ⇒ 00:12:43.106 Vashdev Heerani: a weekly meeting on the Friday that we we plan for for whole week, and then we have a meet meeting on the Thursday, where we discussed all, all the activity of the week so this kind of activity that we we use
94 00:12:44.496 ⇒ 00:12:46.679 Vashdev Heerani: To manage the resources.
95 00:12:47.810 ⇒ 00:12:51.770 Amber Lin: Yeah, I see. What was it like working with your manager?
96 00:12:54.835 ⇒ 00:13:06.630 Vashdev Heerani: So my manager, so initially, initially, my manager well, has more than 18 year of working experience.
97 00:13:06.630 ⇒ 00:13:07.100 Amber Lin: Wow!
98 00:13:07.100 ⇒ 00:13:20.260 Vashdev Heerani: So she’s working in the Google right now. So she she was very technically competent. So I really love to work with her. So she like
99 00:13:20.510 ⇒ 00:13:47.139 Vashdev Heerani: like i i i really love her quality to. Listen most of my stuff so initially, whenever I I have a problem I I go with with to discuss with with her that I have this kind of problem. So she used to say that okay, and talk more about this problem. So initially.
100 00:13:47.670 ⇒ 00:13:54.940 Vashdev Heerani: I tried to talk more about more and more about that problem. And eventually I found the solution from my
101 00:13:55.770 ⇒ 00:13:56.619 Vashdev Heerani: discussion here.
102 00:13:57.145 ⇒ 00:13:57.670 Amber Lin: So.
103 00:13:57.670 ⇒ 00:14:05.389 Vashdev Heerani: That’s where. So that’s the that is the very good thing that I love about her working with with her. Yes.
104 00:14:05.640 ⇒ 00:14:10.319 Amber Lin: I see. So she kind of led you to your own solution.
105 00:14:10.320 ⇒ 00:14:11.460 Vashdev Heerani: Yeah, yeah.
106 00:14:11.460 ⇒ 00:14:12.210 Amber Lin: I see.
107 00:14:12.210 ⇒ 00:14:23.020 Vashdev Heerani: Yeah, whenever, whenever I like, I I go out of the solution. She pointed out that you’re you’re going to and run wrong direction to come again
108 00:14:23.430 ⇒ 00:14:25.540 Vashdev Heerani: this point, and and continue from there.
109 00:14:26.230 ⇒ 00:14:40.480 Vashdev Heerani: So this kind of solution I really love in in that in with with her I I learned how to to find that solution with the different like different solution. For for particular problem.
110 00:14:41.120 ⇒ 00:14:41.920 Amber Lin: Wow!
111 00:14:42.580 ⇒ 00:14:51.609 Amber Lin: What is it like to work without her cause? I know she’s at Google now. So is there anyone to manage you anymore? Like, what is it like.
112 00:14:52.150 ⇒ 00:14:59.790 Vashdev Heerani: Yeah, after that, after that I I work with another guy he.
113 00:14:59.970 ⇒ 00:15:06.610 Vashdev Heerani: He is very, very cooperative, but the tech stack is not matching with my my tech stack his tech stack.
114 00:15:07.160 ⇒ 00:15:17.180 Vashdev Heerani: It is a little different. So in that case I I had to. I had to carry everything that that, I am responsible for for my job.
115 00:15:17.840 ⇒ 00:15:18.440 Vashdev Heerani: So.
116 00:15:18.550 ⇒ 00:15:31.140 Vashdev Heerani: but eventually he he is also very supportive. To to to find any problem, to find any anything with with the company.
117 00:15:31.800 ⇒ 00:15:40.299 Amber Lin: I see I see that sounds great, and I bet you learned a lot from your manager, and then you probably apply that on how you manage the team.
118 00:15:42.230 ⇒ 00:16:00.000 Vashdev Heerani: Yes, so kind of kind of that. So I I learned from from Mary. So she so the way she managed me. I also do the same thing, applies the same thing to to the, to the mentee that I I have right now.
119 00:16:01.560 ⇒ 00:16:06.099 Amber Lin: That’s awesome. Why are you considering leaving that job? Then.
120 00:16:07.340 ⇒ 00:16:11.599 Vashdev Heerani: So it’s kind of 6 year with them. So.
121 00:16:11.600 ⇒ 00:16:11.970 Amber Lin: Oh!
122 00:16:12.779 ⇒ 00:16:25.990 Vashdev Heerani: It’s I. I wanted to to move to switch to another company, to explore more, to find the more opportunity in the data inside our
123 00:16:26.100 ⇒ 00:16:28.120 Vashdev Heerani: in the Llm. As well.
124 00:16:28.750 ⇒ 00:16:29.829 Amber Lin: Hmm, okay.
125 00:16:30.010 ⇒ 00:16:31.300 Amber Lin: Sounds good.
126 00:16:33.590 ⇒ 00:16:34.940 Amber Lin: All right.
127 00:16:37.680 ⇒ 00:16:49.000 Amber Lin: Let’s see, I think we have one more time to talk about one more company. Let’s see.
128 00:16:53.140 ⇒ 00:16:54.120 Amber Lin: Actually.
129 00:16:54.350 ⇒ 00:17:01.549 Amber Lin: actually, I want to give you some time to ask me questions. You know what I’m gonna I’m gonna skip the questions I already originally had.
130 00:17:02.343 ⇒ 00:17:13.230 Amber Lin: I know you talked with Utam about your career goals? Can you give me just like a 1 or 2 sentence, quick overview of your career goals.
131 00:17:14.089 ⇒ 00:17:30.909 Vashdev Heerani: Okay. So so it’s it’s like, I love to to code. But I really love to write a very short code. So I don’t want to spend repeated to code and kind of so by. So I I
132 00:17:30.909 ⇒ 00:17:45.709 Vashdev Heerani: I thought about why not switching to data science or data engineering. That’s why I moved myself from web development to to data engineering side. So right now, I I do. I write very specific code.
133 00:17:46.030 ⇒ 00:17:46.640 Amber Lin: Oh!
134 00:17:46.640 ⇒ 00:17:48.970 Vashdev Heerani: To do the very complex job.
135 00:17:49.200 ⇒ 00:18:12.140 Vashdev Heerani: So my career goal is to to to improve myself in data engineering field and also work on the Llm. Llm. And Gen. AI as well, because Gen. AI is is the future. And you know, yeah. So so I I really love to work on on those technologies.
136 00:18:12.300 ⇒ 00:18:32.580 Amber Lin: Yeah, that sounds great. I I think a lot of our people, especially for our internal tools. I bet Uta already told you. But we help set up our internal data? So that we can do our own AI agents and create our own AI platform. But really like
137 00:18:32.690 ⇒ 00:18:34.330 Amber Lin: without.
138 00:18:34.550 ⇒ 00:18:48.279 Amber Lin: we have a data engineer right now. He’s getting so so busy. But without him we couldn’t have set up a lot of the things, because we only have a wish and utam to who does data, engineering and.
139 00:18:48.280 ⇒ 00:18:48.940 Vashdev Heerani: And.
140 00:18:49.590 ⇒ 00:19:04.210 Amber Lin: So whenever they get time, our work goes really fast on the AI side. But whenever they’re stuck our AI engineers, they’re trying to learn data engineering. But it doesn’t compare with someone who has a lot of experience in that.
141 00:19:04.640 ⇒ 00:19:12.699 Vashdev Heerani: Yes, and one more interesting thing, that’s my class. We did a bachelor together.
142 00:19:13.300 ⇒ 00:19:15.390 Vashdev Heerani: We were roommate. Yeah.
143 00:19:16.590 ⇒ 00:19:17.470 Amber Lin: Wow!
144 00:19:17.580 ⇒ 00:19:18.220 Vashdev Heerani: That’s so.
145 00:19:18.220 ⇒ 00:19:25.120 Amber Lin: Cool. That’s so cool. Yeah. Do you have any questions for me? I would love to answer them.
146 00:19:25.443 ⇒ 00:19:33.849 Vashdev Heerani: Yeah, I I just wanted to know about the project that you are managing right now, and the way you you manage the project as well.
147 00:19:33.850 ⇒ 00:19:42.986 Amber Lin: Hmm my current projects. So currently, I’m managing. Let’s see,
148 00:19:44.200 ⇒ 00:19:57.009 Amber Lin: managing one AI project 1 1 AI client. 2 pretty big data clients. And then there’s 1 that’s just starting out. So
149 00:19:58.510 ⇒ 00:20:27.580 Amber Lin: our teams are usually around, say, 2 to 4 people plus the project manager. And then we generally, for, because I usually manage a bigger projects, the ones that’s more exploratory or like early stages. Usually Tom takes them or someone else manages them. So my projects are usually clients have signed like a 3 to 6 month contract, which for a consultancy is relatively long. And
150 00:20:27.630 ⇒ 00:20:31.029 Amber Lin: so our project processes. We’re just building out
151 00:20:31.380 ⇒ 00:20:41.229 Amber Lin: the Pmo and deciding on how we we’re gonna manage different projects. What kind of meeting cadences we’re gonna do. So usually we have the project initiation
152 00:20:41.550 ⇒ 00:20:47.960 Amber Lin: where it it comes from sales. So the product comes from sales. We make out we make out the
153 00:20:48.150 ⇒ 00:20:48.980 Amber Lin: lot.
154 00:20:49.950 ⇒ 00:21:05.840 Amber Lin: the virtuals that we want to do what the goals for. The projects are. And this is where I work with my tech lead and with sales to decide. Okay, what is the roadmap look like for this? Because I can create the tickets, but
155 00:21:05.840 ⇒ 00:21:23.920 Amber Lin: I don’t. I don’t think it will make too much sense for the engineering. Now that I have AI, it’s really helpful. So I do. The 1st pass the tech leak goes in to say, Oh, that takes this amount of time. That we don’t need that one. And so after that, we start off the project and we usually have
156 00:21:24.190 ⇒ 00:21:35.569 Amber Lin: 2 week sprints. So you mentioned, you guys probably do one week sprints. So we start off. Usually start off planning on Monday. And then we have daily. Every day we have stand ups, and then
157 00:21:35.850 ⇒ 00:21:53.109 Amber Lin: at the end of the sprint we have a retro, and sometimes in the middle, we have, like a grooming to make sure all the tickets are up to date, and then the cycle goes on and we have syncs with the stakeholders. We use slack. So people, a lot of the clients send in request via slack
158 00:21:53.463 ⇒ 00:22:08.220 Amber Lin: and usually I try to triage them. So I I kind of respond to the clients and say, Oh, do we need this. And oh, I will make sure I communicate that to the team. So I kind of serve as the middle between all those requests.
159 00:22:09.530 ⇒ 00:22:16.649 Amber Lin: Let’s see, yeah. And I think about the thing about the Pmo right now, because it’s still developing
160 00:22:17.020 ⇒ 00:22:28.219 Amber Lin: a lot of times we’ll find out. Oh, there’s an issue. And then sometimes the team members are taking way too long like, that’s a recent issue we were addressing, like what to do when things are over the due date.
161 00:22:28.300 ⇒ 00:22:55.349 Amber Lin: And it’s more okay. When do we escalate? Who do we escalate to are there? What are we gonna talk about? Because, like, usually, if everything’s fine. Then there’s not much I need to do, but it’s tough when there’s something going wrong. And for me it’s really nice. If I have a tech lead, because then the engineers can tell me anything I was like, oh, yeah, you’re right. Then the tech lead will be like, no, that’s they’re not.
162 00:22:55.793 ⇒ 00:22:59.910 Amber Lin: That’s not true. So that’s someone that’s a position I would need.
163 00:23:01.200 ⇒ 00:23:03.370 Vashdev Heerani: Okay. Okay. Very. Nice.
164 00:23:05.400 ⇒ 00:23:07.850 Amber Lin: Yeah. Any other questions.
165 00:23:08.280 ⇒ 00:23:11.670 Vashdev Heerani: I don’t think so. I have any other questions.
166 00:23:11.880 ⇒ 00:23:20.219 Amber Lin: Okay. I mean, you could ask me about how like, what time people work? What the next steps are or like.
167 00:23:21.371 ⇒ 00:23:24.789 Amber Lin: I don’t know what questions people usually want to ask.
168 00:23:27.040 ⇒ 00:23:35.410 Vashdev Heerani: So I I usually ask about the weather that we that you are in, and the time zone that you are right now.
169 00:23:36.147 ⇒ 00:23:42.029 Amber Lin: I see. I’m in la. I think most of our clients are in the Us.
170 00:23:42.130 ⇒ 00:23:43.260 Amber Lin: So
171 00:23:43.880 ⇒ 00:24:07.719 Amber Lin: usually we would like you to have a certain overlap with the Us. Time zones don’t need to be the entire day, but at least say like 4 h of overlap our team. We have a lot of team members in the Philippines. So our design team. So our marketing team, which has content design. And then our AI engineers.
172 00:24:08.127 ⇒ 00:24:15.890 Amber Lin: some of them are in the Philippines. We have a few people in India, and then I know there’s a few in Europe.
173 00:24:16.540 ⇒ 00:24:41.650 Amber Lin: so our time zones are all over the place. Our main meetings take place in the Us. Mornings. Cause. I don’t want it to be too late for people and I think we don’t require a strict 9 to 5, so as long as there’s an overlap as as long as things get done, and within the reasonable time that you’re there for any client meetings that needs you. I think that’s
174 00:24:42.200 ⇒ 00:24:43.200 Amber Lin: and I don’t think.
175 00:24:43.200 ⇒ 00:24:43.790 Vashdev Heerani: Yeah.
176 00:24:43.970 ⇒ 00:24:49.469 Amber Lin: Yeah. And I think you can flexibly adjust your hours across across the week.
177 00:24:50.042 ⇒ 00:25:00.669 Amber Lin: Like some days I do. 6 h, some days I do. 10. So like it depends on depends on. As long as I work as done. I don’t think they really care.
178 00:25:02.020 ⇒ 00:25:03.290 Vashdev Heerani: Okay. Okay.
179 00:25:03.530 ⇒ 00:25:04.120 Amber Lin: Yeah.
180 00:25:09.480 ⇒ 00:25:10.160 Vashdev Heerani: So I.
181 00:25:11.200 ⇒ 00:25:25.979 Vashdev Heerani: I think I have another question that usually we we used to ask in in the meeting when we start our. So we mostly start over meeting like. What is the weather? And your side.
182 00:25:28.169 ⇒ 00:25:32.360 Amber Lin: Well, my weather right now is very gloomy. It’s very gray. What about you.
183 00:25:32.865 ⇒ 00:25:35.899 Vashdev Heerani: So it’s it’s very hard. Right?
184 00:25:38.450 ⇒ 00:25:39.110 Vashdev Heerani: Yeah.
185 00:25:39.110 ⇒ 00:25:40.810 Amber Lin: Wow, I
186 00:25:41.010 ⇒ 00:25:46.649 Amber Lin: that’s crazy. I know. In the Philippines some of my team members are experiencing a lot of floods. So.
187 00:25:46.650 ⇒ 00:25:47.160 Vashdev Heerani: Oh, it’s good!
188 00:25:47.160 ⇒ 00:25:47.760 Amber Lin: Be tough.
189 00:25:47.760 ⇒ 00:25:52.500 Vashdev Heerani: So it’s here. It’s not cleared condition, but it’s a rainy condition. So.
190 00:25:52.500 ⇒ 00:25:53.470 Amber Lin: Yeah. Yesterday.
191 00:25:53.470 ⇒ 00:26:01.230 Vashdev Heerani: They train now it’s a it’s about to rain, but not not now. So it’s a it’s a it’s a hard no.
192 00:26:01.490 ⇒ 00:26:02.400 Amber Lin: Wow!
193 00:26:03.720 ⇒ 00:26:04.440 Vashdev Heerani: Okay.
194 00:26:04.440 ⇒ 00:26:12.219 Amber Lin: I see, I mean had very high words of you. I I wish we can
195 00:26:12.350 ⇒ 00:26:29.808 Amber Lin: start working together soon. Because I know I am very short of people, and I need need help, but I’ll let him coordinate with you. I don’t know if there’s another. I think there’s either one more step, or we usually just start a trial period.
196 00:26:30.560 ⇒ 00:26:38.529 Amber Lin: What is the what is the situation like with your company? Do you have to give them like a month. Notice before you leave.
197 00:26:40.070 ⇒ 00:26:46.560 Vashdev Heerani: Yes, it’s a it’s a week, so I can start immediately. Yes.
198 00:26:47.070 ⇒ 00:26:48.670 Amber Lin: Oh, I see. Okay.
199 00:26:48.990 ⇒ 00:26:51.450 Vashdev Heerani: Yes, that’s really good to know.
200 00:26:51.790 ⇒ 00:27:13.480 Amber Lin: Yeah, I think Utah will get in touch with you about like the salary negotiations, and then the logistics, or when to start kind of what your responsibilities are. I don’t think I will be able to answer those for you, but reach out to him if he ever, just if he ever doesn’t respond. Just keep emailing him. He just forgets he gets so many emails.
201 00:27:14.590 ⇒ 00:27:15.360 Vashdev Heerani: Yeah.
202 00:27:15.360 ⇒ 00:27:21.779 Amber Lin: Okay, yeah, thank you so much for this conversation. It’s really great talking to you.
203 00:27:21.780 ⇒ 00:27:24.199 Vashdev Heerani: It was. It was very nice talking to you.
204 00:27:24.930 ⇒ 00:27:25.990 Amber Lin: Alright! Have a great day.
205 00:27:25.990 ⇒ 00:27:27.152 Vashdev Heerani: Thank you. Bye.
206 00:27:27.540 ⇒ 00:27:28.410 Amber Lin: Bye.