Meeting Title: AI Data Engineering Career Discussion Date: 2026-02-03 Meeting participants: Robert Tseng, Ruixi Wen
WEBVTT
1 00:00:42.800 ⇒ 00:00:44.450 Robert Tseng: Hey, is that Miranda?
2 00:00:47.320 ⇒ 00:00:48.299 Robert Tseng: Didn’t you hear me?
3 00:00:54.370 ⇒ 00:00:58.320 Robert Tseng: Ella, hello. I cannot hear you.
4 00:01:08.720 ⇒ 00:01:14.459 Robert Tseng: Hello, hello, testing, testing… I guess I could switch.
5 00:01:14.490 ⇒ 00:01:16.150 Ruixi Wen: Let me know. I think there’s.
6 00:01:16.150 ⇒ 00:01:16.950 Robert Tseng: Oh, yeah.
7 00:01:17.120 ⇒ 00:01:18.010 Ruixi Wen: Yeah.
8 00:01:18.010 ⇒ 00:01:18.760 Robert Tseng: I can hear you now.
9 00:01:18.760 ⇒ 00:01:20.689 Ruixi Wen: Wrong with my microphone here?
10 00:01:21.790 ⇒ 00:01:24.579 Ruixi Wen: Yeah, hi! Hi, Earl Vern!
11 00:01:25.230 ⇒ 00:01:27.420 Robert Tseng: Hi, is… do you go by Miranda?
12 00:01:27.710 ⇒ 00:01:29.380 Ruixi Wen: Yeah, I go by Miranda, yeah.
13 00:01:29.380 ⇒ 00:01:31.359 Robert Tseng: Okay. Yeah, good to meet you.
14 00:01:31.760 ⇒ 00:01:37.380 Ruixi Wen: Yeah, nice to meet you too. Thank you so much for, taking this time to have this call with me. I bet you’re, like, very busy.
15 00:01:37.840 ⇒ 00:01:42.500 Robert Tseng: No, no, all good. Yeah, always happy to meet WBB people,
16 00:01:42.670 ⇒ 00:01:51.449 Robert Tseng: Yeah, I mean, your LinkedIn background was my wallpaper, or whatever, for my banner for a while, as well, so,
17 00:01:51.670 ⇒ 00:01:54.830 Robert Tseng: Yeah, I mean, you just graduated from WVV last year?
18 00:01:55.210 ⇒ 00:01:57.530 Ruixi Wen: Yeah, yeah, I graduated in May, yeah.
19 00:01:58.400 ⇒ 00:02:05.610 Robert Tseng: Wow, okay. Yeah, I’m sure your experience is very different than mine. Yeah, I was class of 2018, so…
20 00:02:06.120 ⇒ 00:02:09.020 Robert Tseng: Yeah, I don’t think we ever crossed paths.
21 00:02:09.830 ⇒ 00:02:18.020 Ruixi Wen: Yeah, yeah, yeah. But yeah, yeah, super nice to connect with you, like, I always found, like, the conversation with WQR so good, because we just, like…
22 00:02:18.220 ⇒ 00:02:23.090 Ruixi Wen: more or less share the same experience, and it’s definitely, like, unparalleled.
23 00:02:23.460 ⇒ 00:02:25.890 Robert Tseng: Yeah, where are you these days?
24 00:02:26.650 ⇒ 00:02:30.660 Ruixi Wen: Yeah, I moved to San Francisco, yeah, upon graduation, yeah.
25 00:02:30.910 ⇒ 00:02:36.860 Ruixi Wen: So, I’m seeing San Francisco City. And, you’re… you’re… are you in Texas, or…
26 00:02:36.860 ⇒ 00:02:42.989 Robert Tseng: My business partner is in Texas, the company is technically based in Texas, but I live in New York, so…
27 00:02:42.990 ⇒ 00:02:44.469 Ruixi Wen: Oh, I see, I see.
28 00:02:44.470 ⇒ 00:02:49.470 Robert Tseng: Yeah, but I grew up in the Bay Area, I go back often. What part of stuff are you in?
29 00:02:50.350 ⇒ 00:02:55.449 Ruixi Wen: Yeah, I live in, like, Design District, like, it’s kind of close to, like, Soma. Oh, yeah. Yeah.
30 00:02:55.450 ⇒ 00:02:57.370 Robert Tseng: Sure, yeah, no, I love that area.
31 00:02:58.190 ⇒ 00:03:02.140 Ruixi Wen: Yeah, yeah, it’s, like, next to Costco, Whole Foods, like…
32 00:03:02.150 ⇒ 00:03:06.280 Robert Tseng: Yeah, I was like, if you have a bunch of roommates, it’s a good place to be.
33 00:03:06.280 ⇒ 00:03:06.790 Ruixi Wen: Oh, yeah.
34 00:03:06.790 ⇒ 00:03:21.669 Robert Tseng: Oh, yeah. Yeah, and then, I guess you were saying that you were doing… I mean, congrats on your exit, I guess, doing go-to-market for, like, a startup that got acquired by Snowflake. How was that… how was that experience?
35 00:03:22.650 ⇒ 00:03:42.349 Ruixi Wen: Yeah, that experience was crazy, because I was only there, like, I joined in May, so I was on… I was there for, like, 6 months-ish, and then I heard, like, oh, we’re, like, two companies, one acquire us, like, one’s Astronomer, and one’s Snowflake, and it actually, like, moved, like, pretty fast, like, just, like, within months, like, they decided to close the deal and all that, so…
36 00:03:42.350 ⇒ 00:03:44.750 Ruixi Wen: It was honestly quite crazy, yeah, cause…
37 00:03:44.820 ⇒ 00:03:52.979 Ruixi Wen: Because I honestly think back, like, because the company was, like, going pretty well, at least, like, on the golden market side, and the products, like.
38 00:03:52.980 ⇒ 00:03:53.590 Robert Tseng: Yeah.
39 00:03:53.590 ⇒ 00:04:07.400 Ruixi Wen: They’re shaping, we’re doing great. It was kind of shocking for me to have this early exit, but I can tell it was, like, like, my… the founder wanted that, and and he feels, like, good… good opportunity, but yeah, it’s crazy that I experienced that, yeah.
40 00:04:07.590 ⇒ 00:04:24.170 Robert Tseng: Yeah, and I’m just curious, you know, I mean, I’m curious, like, I mean, because a lot of AI companies got bought out. Some, the team didn’t really get anything, some did, like, did you, did you get a part of an exit, or was it just, they just pushed your founders, and then the team disbanded, or kind of how did that end up happening?
41 00:04:25.190 ⇒ 00:04:34.829 Ruixi Wen: Yes, I didn’t get, like, much part from the, the exit, because, like, they required… for the equity vesting, they required, like, a one-year cliff, and that was.
42 00:04:34.830 ⇒ 00:04:35.350 Robert Tseng: Oh, aw.
43 00:04:35.350 ⇒ 00:04:36.840 Ruixi Wen: So…
44 00:04:36.840 ⇒ 00:04:37.959 Robert Tseng: Oh, that’s a bummer.
45 00:04:38.370 ⇒ 00:04:40.260 Ruixi Wen: Yeah, yeah, yeah.
46 00:04:40.680 ⇒ 00:04:42.890 Ruixi Wen: Yeah, but,
47 00:04:43.020 ⇒ 00:04:48.749 Ruixi Wen: Yeah, so that’s how it went. And actually, I got an offer to join, like, Snowflake, but…
48 00:04:48.870 ⇒ 00:05:04.220 Ruixi Wen: After considering, I feel like it’s not the path I want to go. Like, on one side, it’s like, Snowflake’s go-to-market is very different. They’re just, like, trying to all-compete with, like, Databricks. They have, like, over a thousand employees in North America for the sales team, and also they’re…
49 00:05:04.220 ⇒ 00:05:13.669 Ruixi Wen: very notorious for their, promotion track. Like, they barely promote people, internally. They always, like, hire, like, managers, or…
50 00:05:13.670 ⇒ 00:05:25.119 Ruixi Wen: high-level people, like, from all sides of the company, so I was thinking, like, that’s, like… and also, like, Snowflake was, like, kind of far, the community and all that, so I was, like, not really,
51 00:05:25.240 ⇒ 00:05:36.019 Ruixi Wen: but not very… like, it’s good for Canva, it’s, like, not really good for my career paths from here. Sure. So I still want to, like, be in, like, the startup word, overall, so, yeah.
52 00:05:37.280 ⇒ 00:05:43.689 Robert Tseng: Yeah, well, I mean, I’m… your background is very interesting to me. We do a lot of the same things, or, like.
53 00:05:44.160 ⇒ 00:05:47.390 Robert Tseng: We’re a data engineering firm at first, and…
54 00:05:47.760 ⇒ 00:06:01.919 Robert Tseng: Yeah, I mean, the idea of using AI to build data pipelines, like, we do that internally to speed up our internal data engineers. We didn’t build out a platform for it, but yeah, I mean, this is, like, very much the language that I speak to my team all the time.
55 00:06:02.060 ⇒ 00:06:04.920 Robert Tseng: And yeah, I mean, I guess, like…
56 00:06:04.920 ⇒ 00:06:14.580 Ruixi Wen: Like, you guys are even, like, the dbt tech stack and stuff, and that’s, like, exactly, like, what’s the product I was working for, like, they performed the best at as well, yeah.
57 00:06:14.580 ⇒ 00:06:22.169 Robert Tseng: Yeah, wait, so you guys are just, like, you… I mean, the best performing part of the product was, like, automating dbt development, pretty much? Yeah, dbt and Airflow.
58 00:06:22.530 ⇒ 00:06:23.130 Robert Tseng: Yeah.
59 00:06:23.990 ⇒ 00:06:32.290 Robert Tseng: Yep, I mean, that’s… I think you know our world, to some extent. I think to add on to that, for us,
60 00:06:32.800 ⇒ 00:06:41.160 Robert Tseng: I mean, I… so I lead go-to-market right now for our team. We’re, you know, basically Abbacco CEO. He leads delivery, I leave go-to-market.
61 00:06:41.470 ⇒ 00:06:54.500 Robert Tseng: Yeah, I mean, half our business is, like, CPG brands, so, yeah, probably doing… the brands we work with are bigger, big enough to, like, make sense to actually build out a data function, because there are a lot of.
62 00:06:54.500 ⇒ 00:06:54.910 Ruixi Wen: Absolutely.
63 00:06:54.910 ⇒ 00:07:13.259 Robert Tseng: smaller brands that get by without having to actually build and maintain their own pipe… the data pipeline. We prefer to land on that data in Snowflake, which is why we’re a preferred partner there and trying to get more attention with that community. But we obviously have… we have BigQuery and AWS and capabilities in-house as well, so…
64 00:07:14.690 ⇒ 00:07:23.399 Robert Tseng: Yeah, I guess from there, we basically are a fractional data team for those brands, so a lot of it is setting up the same, like, similar data models, like.
65 00:07:24.400 ⇒ 00:07:44.069 Robert Tseng: CPG Ecom data modeling is not that complicated. I mean, generally, it looks very similar, but every business has, like, a very… I mean, it’s not easy. There’s definitely certain parts that are unique to each business, but we’ve been able to reuse a lot of code across clients, which has helped us to scale a bit more now.
66 00:07:44.670 ⇒ 00:07:59.010 Robert Tseng: And then the other half of our business is, yeah, just kind of a random assortment, to be honest. Like, we work in healthcare, we work with, home services, insurance, and, like, the bet for the business is really that we want to, make
67 00:07:59.920 ⇒ 00:08:17.669 Robert Tseng: we want to help organizations, get all their data ready so that they can actually utilize AI effectively. I think the thesis is that the past 6 months, yeah, I mean, really the past 2 quarters, we experienced the most growth because organizations are realizing that
68 00:08:17.900 ⇒ 00:08:35.650 Robert Tseng: Yeah, there’s all these, like, AI pilots that people are able to go and, like, set up, but then it doesn’t actually really work in production unless it’s integrated very well with your existing data, your knowledge base. So you don’t really get away from the structured, kind of, like, data engineering principles that you… that, you know.
69 00:08:35.650 ⇒ 00:08:45.420 Robert Tseng: bigger enterprises have, and so I think that ended up getting us into more conversations where we’re like, okay, you tried implementing AI in your organization, you failed.
70 00:08:45.420 ⇒ 00:09:01.570 Robert Tseng: well, here’s how we would do it if we were to run it back with you and build it out more effectively. And that’s been kind of, like, a big pivot that we’ve taken over the past 6 months. But yeah, I’ll just kind of pause there. That’s kind of the gist of, like, where the company… how we position ourselves, where we’re headed.
71 00:09:01.570 ⇒ 00:09:06.160 Robert Tseng: We’re about 20 people, remote company, happy to answer any, any questions that you have.
72 00:09:07.720 ⇒ 00:09:13.450 Ruixi Wen: Yeah, yeah, yeah, thank you so much for sharing this background. I’m also, like, very much curious on, like,
73 00:09:13.590 ⇒ 00:09:31.399 Ruixi Wen: like, I think it’s, like, really makes sense, like, I think for, the data engineering part is definitely very critical to set up the data, be ready to be successful, like, it really cares, like, it really, what really matters, like, whether or not it actually works on their, like, workflow, whether or not it integrates with their workflow well. Exactly.
74 00:09:31.400 ⇒ 00:09:34.719 Ruixi Wen: Like, an add-on, like, it shouldn’t be like an add-on, it should be like a…
75 00:09:34.720 ⇒ 00:09:38.230 Ruixi Wen: part of the workflow, like, it comes very naturally. Yeah.
76 00:09:38.230 ⇒ 00:09:38.830 Robert Tseng: Yeah.
77 00:09:38.830 ⇒ 00:09:53.490 Ruixi Wen: I’m so curious, so, like, does the, does the product have, like, the UI UX? Is this, like, like, have a UI UX where, like, you put, like, natural language input, or how does the, how does the product, like, look like in general?
78 00:09:53.490 ⇒ 00:10:07.300 Robert Tseng: Yeah, so, I mean, what… kind of to… just to differentiate, we’re actually a services company, so you can think of us like a consultancy. So we’re structured, like, yeah, we don’t… I mean, we have… we build internal product to help our…
79 00:10:07.300 ⇒ 00:10:15.220 Robert Tseng: delivery team, but we basically have a bunch of board deployed engineers that work within clients, and so I’m basically running a consultancy.
80 00:10:15.260 ⇒ 00:10:31.289 Robert Tseng: Yeah, I mean, as far as, like, product goes, like, I mean, we custom build solutions for clients. We do have a pretty robust, like, internal data platform that we’ve built out to help our own teams, so that helps us to kind of consolidate a lot of, like, different tools that people would have
81 00:10:31.720 ⇒ 00:10:45.049 Robert Tseng: typically bought, like, a bunch of different tools off the shelf, but we kind of combined it all into one. And we actually work with a couple organizations to help them build out something that we’ve built for ourselves. So, that’s the only real, like, product development that we’re doing.
82 00:10:45.150 ⇒ 00:10:55.199 Robert Tseng: I would say, like, the more interesting work that we do is, yeah, on the services side, where we really go in, we work with, like, a marketing team, for example.
83 00:10:55.600 ⇒ 00:11:05.039 Robert Tseng: And we’re telling them, like, yeah, we’ll be able to, we’ll be able to help you, understand,
84 00:11:05.650 ⇒ 00:11:12.039 Robert Tseng: Your entire, like, marketing budget, kind of how you… how should you efficiently allocate your budget?
85 00:11:12.040 ⇒ 00:11:16.660 Ruixi Wen: What bets to make. Just trying to help to basically be their experimentation.
86 00:11:16.660 ⇒ 00:11:24.819 Robert Tseng: kind of team, and help them to run, all types of different experiments across their organization.
87 00:11:25.000 ⇒ 00:11:39.369 Robert Tseng: Yeah, so I think, like, that’s the… and then I… I basically, like, price… price by outcome. So if we help the companies achieve X percentage growth, we should get a X percentage of that… of that revenue. And, like, that’s kind of how… how we… how we grow with our clients.
88 00:11:40.640 ⇒ 00:11:50.410 Ruixi Wen: Oh, I see, I see, that makes sense. Okay, thank you so much for clarifying that. Yeah. Yeah. I wonder what’s, like, the sales process look like for you guys, then? Like…
89 00:11:50.670 ⇒ 00:11:56.750 Robert Tseng: Yeah, I mean, it’s, it’s pretty, like, I mean, we do try, but…
90 00:11:56.750 ⇒ 00:12:07.880 Ruixi Wen: Yeah, I think the question is, like, I’m curious, like, for when it’s, like, kind of, like, service-oriented, like, how do you win the trust, like, during the sales process? Because, like, for a product, you can, like, let them test that, but the service, like…
91 00:12:07.880 ⇒ 00:12:17.840 Ruixi Wen: you cannot really… I don’t know, like, like, I guess the POCJ doesn’t work as well, because, like, it has, like, then you’re providing the service, and that should be charged for, so I’m curious, like, what’s the skills.
92 00:12:17.840 ⇒ 00:12:18.650 Ruixi Wen: Leslie.
93 00:12:18.650 ⇒ 00:12:22.790 Robert Tseng: No, that’s a great question. Thanks for bringing that up. So… I think…
94 00:12:23.380 ⇒ 00:12:31.639 Robert Tseng: So we do actually have some demos that, like, yeah, it helps to… when we’re selling to a not very…
95 00:12:31.670 ⇒ 00:12:44.190 Robert Tseng: educated buyer, it just helps for them to be able to, like, see and play with something. So we definitely have demos that we… so we have partnerships with various vendors, like Snowflake.
96 00:12:44.210 ⇒ 00:12:58.569 Robert Tseng: actual Omni amplitude… we have about, like, 10 core partners that we’ve built a bunch of demo instances with, and we basically co-sell with them into verticals that they don’t normally are able to reach by themselves. So that’s part of our go-to-market motion.
97 00:12:58.750 ⇒ 00:13:08.420 Robert Tseng: But then a lot of our deals kind of come through, like, referrals, and we have… we do have, outbound, but it’s less, like, of a volume play that you would see at a product company.
98 00:13:08.450 ⇒ 00:13:26.519 Robert Tseng: And you’re right, that, like, people work with us because they’re convinced that we could build something for them, but, like… and I actually think that barrier of trust is lower than, like, a product. Like, I… I think that if people can just… or if they’re convinced that you can help them
99 00:13:26.540 ⇒ 00:13:37.340 Robert Tseng: do the thing that they’re asking for, or, like, kind of you scope… you scope it down, then they’re willing to give you budget to go and try. So, yeah, I think that’s…
100 00:13:37.340 ⇒ 00:13:53.679 Robert Tseng: So I think that’s probably one of the biggest differences with products compared to selling for, like, a product. We… we… like, our… the volume we do is much lower. Our win rates are probably higher, like, we probably close, like, 20% of leads.
101 00:13:53.750 ⇒ 00:14:05.869 Robert Tseng: Which I feel like is just high compared to product companies. But yeah, it’s very relationship-oriented, and we just have to figure out other ways to build trust with the stakeholder before they sign a deal.
102 00:14:07.100 ⇒ 00:14:15.809 Ruixi Wen: Mmm, I see, that makes sense. Thank you so much for sharing, yeah. But how did you, fall into, like, this space and wanna, wanna work for this, and…
103 00:14:16.940 ⇒ 00:14:32.139 Robert Tseng: I guess… by 3… It’s been, like, 3 years ago, I left my last in-house role. I was leading data for a consumer brand in LA, it’s called Ruggable. They were doing about $400 million in revenue, I had a team of, like, 7 analysts under me.
104 00:14:32.140 ⇒ 00:14:37.390 Robert Tseng: And… yeah, I don’t know, I think I had the thought that if I could…
105 00:14:37.510 ⇒ 00:14:46.600 Robert Tseng: go off and just do, like, package my skills and try to do it for multiple, clients, so I was envisioning just doing it for e-com companies.
106 00:14:46.650 ⇒ 00:14:58.720 Robert Tseng: So those were my first clients. Along the way, I met my business partner, because I ended up bringing him onto a client that I was working on. He’s more of a traditional data engineer, was an early data engineer at WeWork.
107 00:14:58.890 ⇒ 00:15:03.689 Robert Tseng: And we ended up… it ended up being our biggest project together, so we thought.
108 00:15:03.690 ⇒ 00:15:27.819 Robert Tseng: well, why don’t we just try to combine forces, and then, kind of, we can… we can reach something bigger. And I think the bet that we were making was, we don’t just want to do the work we were doing before for many different companies, we want to do it in a very AI-native way. And so, I think that’s what really gets us excited. We’re… we’re really pushing… everyone on our team uses Cursor, and yeah, like, both technical and non-technical staff, like, we want to, like, re…
109 00:15:27.820 ⇒ 00:15:31.960 Robert Tseng: I think the way that consultants and freelancers kind of, like, work.
110 00:15:31.980 ⇒ 00:15:51.530 Robert Tseng: So yeah, I think that’s helped us to achieve, I mean, we’ve bootstrapped company, we’ve… we’ve grown to about 20… around 22 people at this point. Yeah, over the past… over the past two years of really, like, kind of working… working… working together. And yeah, so I think that’s… that’s kind of the… the journey that we’ve been on.
111 00:15:53.070 ⇒ 00:15:56.880 Ruixi Wen: Oh, I see, oh my god, that sounds crazy, like,
112 00:15:56.990 ⇒ 00:16:08.330 Ruixi Wen: Yeah, I think it makes a lot of sense, like, you’re already, like, in this space leading a team, and then you have, like, the product opportunities, and the AI wave, and you… you’re pivoting to here. I think that makes a lot of sense, yeah.
113 00:16:08.330 ⇒ 00:16:21.789 Robert Tseng: Yeah, I mean, I think AI has made my previous job, like, it’s irrelevant. Like, I… if… if I stayed where I was, I would… I would be so behind compared to where we are today, which has only been 2 years, you know.
114 00:16:22.700 ⇒ 00:16:24.520 Ruixi Wen: Yeah, that makes sense, yeah.
115 00:16:24.520 ⇒ 00:16:25.110 Robert Tseng: Yeah.
116 00:16:25.720 ⇒ 00:16:36.480 Robert Tseng: Well, I’m curious what… I mean, you’re weighing a couple different options, you’re thinking of going to product ops, like, you want to stay in GTM, like, what are you thinking in your career? Like, what are you… what kind of opportunity are you looking for next?
117 00:16:37.650 ⇒ 00:16:44.920 Ruixi Wen: Yes, I think, like, for me, I think I overall enjoy, like, the go-to-market side of things from my, previous company.
118 00:16:44.920 ⇒ 00:17:03.919 Ruixi Wen: And and I do… and I do, I think it’s just because of acquisition and all that. I think, like, 7 months’ experience was definitely, like… there’s a lot more for me to learn and grow into, like, especially the deal cycle was usually, like, 2-3 months. There were a lot of, like, deals in the pipeline just got cut short, so I didn’t get to, like, follow up and close some of the deals.
119 00:17:03.920 ⇒ 00:17:18.319 Ruixi Wen: So that was… that was kind of a bummer on that end, so on one side, I’m happy to, like, continue on that track. But also, like, because I was also working for a startup, I was, like, literally working with, like, product engineering team side by side, and I go on there weekly.
120 00:17:18.319 ⇒ 00:17:37.249 Ruixi Wen: meetings to see, like, the product updates, and I talk with customers and all that. And I found, like, myself, like, pretty much interesting, like, the product side, which, like, in my role before was, like, it’s more so, like, oh, these clients, they want us to build this feature, or they have this pinpoint, and probably we can, with our current
121 00:17:37.250 ⇒ 00:17:56.350 Ruixi Wen: product, we can just add a feature to help them to resolve that. And, that’s where, like, I fell into, I think, like, that part is, like, pretty, meaningful, but it’s, like, definitely requires you to be, like, really speak, like, all the language. You need to know the client’s pain points, and at the same time, you know, like, the language for product and engineering, especially, like.
122 00:17:56.880 ⇒ 00:17:57.330 Robert Tseng: Yep.
123 00:17:57.330 ⇒ 00:18:12.039 Ruixi Wen: product specs, you need to… you need to let them know, like, how… how this thing’s gonna be conducted. So I think that part was also, like, pretty much interesting, but I think I definitely need, like, more, because WB is not… not meant to be, like, a product-oriented program.
124 00:18:12.480 ⇒ 00:18:31.690 Ruixi Wen: Yeah, yeah, so I definitely need, like, more experience and time to grow into that role, but I think that’s, like, where I’m saying myself, I want to learn, like, how to really sell a product well, and also, like, how to, how to build, like, construct a product well to help, like, this product to, develop further. Yeah.
125 00:18:31.690 ⇒ 00:18:32.250 Robert Tseng: Yeah.
126 00:18:32.760 ⇒ 00:18:45.380 Robert Tseng: Yeah, tell me more about, like, kind of, like, the go-to-market initiatives you were running at, I guess, your previous company. I’m curious, like… I mean, go-to-market means a lot of things to different people, so I’m curious, like, what it looked like for you. Yeah.
127 00:18:45.670 ⇒ 00:18:59.039 Ruixi Wen: Yeah, so for us, like, I was doing, like, both, like, inbound and outbound, and before I, inbound, it’s kind of like what you guys are having right now, like, have referrals, like, from existing customers, or they just, like, book a demo from the website and stuff.
128 00:18:59.040 ⇒ 00:19:09.550 Ruixi Wen: the warm leads, and also on the outside, I was doing allbound. So before I joined, like, the company doesn’t have, like, all bounds, built out, so I helped them to build out, like, the whole Allbound pipeline.
129 00:19:09.550 ⇒ 00:19:24.269 Ruixi Wen: from 0 to 1, like, and by outbound, like, we… for us, like, it works perfectly well on LinkedIn, and the other thing, like, by cold calling. And then we also, like, go to, like, those conferences. I went to, like, Las Vegas DBT Coal last… last year.
130 00:19:24.270 ⇒ 00:19:25.429 Robert Tseng: Okay, nice.
131 00:19:25.790 ⇒ 00:19:44.220 Ruixi Wen: Yeah, that was, like, a pretty… a really, really crazy amounts for our quota because of that conference we showed up to. And and from there, like, I mostly was talking to… because our clients are mostly, like, data engineers, so I mostly talked to, like, VP of data engineers,
132 00:19:44.350 ⇒ 00:19:52.700 Ruixi Wen: unless it’s engineering manager from there. From, like, for us, it’s like, we talk to both, like, mid-market enterprises.
133 00:19:52.700 ⇒ 00:20:05.330 Ruixi Wen: E-commerce was definitely, like, also in our bucket. we try to get them in healthcare and stuff, and the other ones are large enterprises, like, Ford was our class… was our client. They have, like, a huge team, but it’s, like.
134 00:20:05.460 ⇒ 00:20:24.660 Ruixi Wen: their tech stackers is, like, pretty much, like, a legacy system, I would call it. Yeah. Yeah. Yeah, so I… I basically, like, like, I help with, like, the inbound with discovery calls, and I help, like, the outbound prospecting, and then converting to the discovery call, and the sitting with the sales engineer to go through, like, the whole.
135 00:20:24.660 ⇒ 00:20:33.970 Ruixi Wen: deal cycle, to POC stage, then. After POC stage, there’s some, like, infoaxing, which, I’m just, like, hands-off from there. Yeah, that’s, like, in general… Yeah.
136 00:20:33.970 ⇒ 00:20:46.699 Ruixi Wen: the go-to-market was like. And, the company, like, cost, like, their first million ARR last year, and I helped, like, to draw, like, 450,000 revenue in the pipeline, but when I was… when I left the company, like, we still have something.
137 00:20:46.700 ⇒ 00:20:48.539 Ruixi Wen: And the pipeline didn’t close, yeah.
138 00:20:49.180 ⇒ 00:20:56.039 Robert Tseng: Yeah. No, that’s great. I mean, congrats. I mean, wow, they got bought out by Snowflake at a million in revenue? That’s crazy.
139 00:20:57.950 ⇒ 00:21:01.069 Robert Tseng: Yeah, that’s a very early… that’s a very, like, fast…
140 00:21:01.070 ⇒ 00:21:02.160 Ruixi Wen: areas are you engaged?
141 00:21:02.160 ⇒ 00:21:02.710 Robert Tseng: Yeah.
142 00:21:03.040 ⇒ 00:21:04.140 Ruixi Wen: Yeah, yeah.
143 00:21:04.560 ⇒ 00:21:22.899 Robert Tseng: Yeah. No, I mean, that’s a lot of, like, kind of what we do. I mean, I know that your interest is in selling product, but I mean, I feel like your experience is very intriguing to me, so I’m… I mean, I’ll put… I’ll just give you a couple things to think about, but if you were to come and sell services, like, basically, I think…
144 00:21:23.110 ⇒ 00:21:24.270 Robert Tseng: You…
145 00:21:24.350 ⇒ 00:21:43.040 Robert Tseng: I think there’s more flexibility because, the go-to-market, like, feedback loop is much faster. You don’t have to wait for a product to be developed before you go take it to market. You can run even earlier experiments, which I’m… I’ve been pushing my team to do. As soon as we get a win from a client on, like, we did something that drove something, like, really cool results.
146 00:21:43.040 ⇒ 00:21:52.569 Robert Tseng: I want them to be packaging it and turning it into LinkedIn content, and then, like, there’s a whole sequence around that to try to see if we can get some traction with leads that way.
147 00:21:52.720 ⇒ 00:22:10.849 Robert Tseng: And so, I mean, I think other than that, like, the sequence of booking the demo, it’s booking a discovery call with us. After a call, you know, we basically go into proposal stage. If it’s a bigger client, mid-market enterprise, we typically do a paid discovery, so they will literally pay us, like, to do one to two months of work of just going around, talking to everybody in their company.
148 00:22:10.850 ⇒ 00:22:14.339 Robert Tseng: Figuring out, like, what, you know, what we need to do with them.
149 00:22:14.340 ⇒ 00:22:23.350 Robert Tseng: And then… and then we go into, like, a 3-6 month implementation, phase with them. So, yeah, you would be able to, like.
150 00:22:23.350 ⇒ 00:22:33.350 Robert Tseng: kind of… you would be a part of any… you’d be interfacing with our internal product team to work with our AI engineers to tell them to build features that you need on the outbound side.
151 00:22:33.620 ⇒ 00:22:50.439 Robert Tseng: you’d be working closely with our AI PM, or you could even take that role, because that engineer… those engineers need to know, like, what to build, especially if we’re building demos with our vendors, or, like, yeah, I think sometimes there’s… everybody ends up doing some
152 00:22:50.500 ⇒ 00:23:02.379 Robert Tseng: some form of client work that’s just, like, one of our requirements that everybody has to be participating in the consultancy in some way, so it is kind of a… that’s probably a unique part of our business.
153 00:23:02.380 ⇒ 00:23:18.710 Robert Tseng: And then, I mean, sounds like you’re very familiar with data engineers, so, like, we are hiring, like, technical talent to staff engineers on clients all the time. So, yeah, I mean, that’s… that’s kind of, like, you know, if you’re interested in kind of exploring that further, I’m happy to…
154 00:23:18.710 ⇒ 00:23:29.050 Robert Tseng: kind of follow up with you, give you, like, some idea of, like, scope that we could kind of pass your way. But yeah, for based on what you’re describing, I think some cross between
155 00:23:29.050 ⇒ 00:23:39.910 Robert Tseng: kind of, like, GTM systems and also, like, an AI PM for our internal AI team could be an interesting, like, role that I could discuss with the team for.
156 00:23:41.410 ⇒ 00:23:46.800 Ruixi Wen: Okay, amazing. I think that sounds great. I’m definitely, like, interested in, like, learning more about, like, you guys’ service and…
157 00:23:46.800 ⇒ 00:24:04.110 Ruixi Wen: how, like, this feedback could be, like, leading towards, too. But even if, like, it doesn’t fall into, the exact fit or something, but I think it’s, like, very nice, like, I got connected with you today, and really to learn about this. Like, thank you so much for, your generosity in offering the time and all that.
158 00:24:04.960 ⇒ 00:24:10.409 Robert Tseng: Yeah, of course. Yeah, I mean, I feel like I’m pretty well connected in this space, too, so if you see anybody
159 00:24:10.440 ⇒ 00:24:24.040 Robert Tseng: in my network that you want an intro to, feel free to let me know. Otherwise, yeah, I mean, I’ll send you some follow-ups, see if that’s something that you want to explore further. Yeah, I mean, I do think that your background
160 00:24:24.040 ⇒ 00:24:31.050 Robert Tseng: surprisingly fits well with what we do, so, I mean, I’d be interested in trying to see if we can work something out with you.
161 00:24:31.920 ⇒ 00:24:46.529 Ruixi Wen: Yes, yes, yeah, yeah, when I was, like, really looking to your guys’, like, website, I mean, I would, I mean, it stuck here on LinkedIn, and look through your posts, and I was like, wait, they, like, all these things they’re doing are, like, so similar, like, yeah, yeah, this, this is, like…
162 00:24:46.530 ⇒ 00:24:53.349 Ruixi Wen: Yeah. Yeah, I wonder, like, if linking’s the best platform to reach out to you, or you prefer email or phone number.
163 00:24:54.050 ⇒ 00:25:09.340 Robert Tseng: Yeah, no, I’ll give you my phone number. You can just text me directly. LinkedIn is pretty noisy. Obviously, we use it to… to do a lot of our outreach, so it just… the list just keeps getting buried. So… I mean, now that we’re… now that we’re connected, I’ll give you my number, and we should just text from there.
164 00:25:09.840 ⇒ 00:25:13.140 Ruixi Wen: Okay, that sounds great. Thank you so much, Brovert.
165 00:25:13.140 ⇒ 00:25:15.929 Robert Tseng: Yeah, thanks for your time, Miranda, and I hope to talk to you soon.
166 00:25:16.280 ⇒ 00:25:18.940 Ruixi Wen: Okay, same here. Okay, thank you. Yep.
167 00:25:18.940 ⇒ 00:25:19.610 Robert Tseng: Bye.