Databricks moved engineering pay to the market's 90th percentile

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 34:30

   And, you know, I would also give credit for Marc Andreessen in one of the meetings, I think, early days, we said, he said, what's your biggest bottleneck? I think this was 2017. I said, hey, we can't hire engineers. We can't get the engineers from Facebook and Google. They're just too expensive. And he said, huh, so what's the, so Fang is the problem? And I said, yeah, Fang is the problem. And I said, okay, so you just need to add yourself to that list.

2. 34:53

   It needs to be FangDB. Huh. And that's it. And then he moved on. But after that meeting, we're like, yeah, okay, he kind of is right. We should actually just offer up and, you know, hire those people and become like one of those tech magnets. And it changed our mindset. Like we went back, we changed our compensation, we changed how we hire, just based on that one comment. I'm curious, like at a tactical level, I mean, that's very aspirational.

3. 35:15

   It sounds great. But like in changing the compensation or thinking bigger, Like, what did you then go do? We literally went back and moved the compensation for engineering to 90th percentile of the market. You know, we did the math and we saw how much is it going to cost us? Are we going to be able to do this? Is it going to be, is it something that we can afford as a company? What's the dilution going to be?

4. 35:36

   This was a long time ago. And actually, when we ran the calcs, we were surprised that actually we can do this and it's sustainable. And it was surprising to us. We're data companies. We crunched the data and all the numbers. What we found is that, you know, No, it's really your market cap divided by number of employees. That's how equity rich you are when you're paying someone equity. And these companies have a lot of employees.

5. 35:56

   So then it turned out, actually, we are actually richer than we think if you take our... market cap and divided by the number of employees. Because that's, you know, that's not how you're doing financially. That's not about your revenue growth, but that's about what you, you know, what could you offer someone in equity? And it turned out that actually it's absolutely within our reach to do it. So let's explore it a little bit.

6. 36:14

   And it started working. And then we were able to hire the people that we couldn't hire before. And they came in and they completely revamped what we were doing with the product. We just could increase the feature velocity and the innovation cycle at Databricks massively. And that actually helped us kind of slowly start separating from the pack. Because in 2017, if you had talked to us, we would have been one of the startups among hundreds of other ones out there.

7. 36:36

   So these small things kind of that they did to push us, think really, really big, go way bigger. And we did that, but we always kind of did it judiciously. We didn't want to kind of go crazy and burn down the bank account. We wanted to be careful about it. Databricks was founded in 2013? Yes. Is that right? And for the first couple of years, you weren't the CEO.