In this episode of Full Throttle Leaders, host Michael Falato interviews Josh Lillie, a go‑to‑market leader on the Databricks startups team. Josh explains that Databricks is a data and AI company whose mission is to democratize AI for organizations of any size, from pre‑seed startups to Fortune‑10 enterprises. He describes how his team works with hundreds of startups across verticals—fintech, health‑tech, manufacturing, and especially B2B SaaS platforms—helping founders and technical co‑founders build products on Databricks and craft effective go‑to‑market strategies. Josh shares his personal career journey: six years at Microsoft in business development and digital sales, then four years at Databricks, highlighting his experience in account‑based marketing and digital sales models. He offers a key career lesson about the importance of saying “yes” to opportunities while recognizing the need to find an inflection point to avoid overextension. The conversation also covers the strategic use of LinkedIn for personal branding, credibility, and founder outreach. Finally, Josh invites founders, early‑career professionals, and anyone interested in AI or Databricks to connect via email or LinkedIn for advice on go‑to‑market, AI, or career development.
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Hello everybody, Michael Falato here from Full Throttle Leaders, the podcast that we've been talking about with go-to-market strategists, um, top-of-funnel folks, AI folks. And we got a really great guest for us today, Josh from Databricks. How you doing, Josh?
I'm good, Michael. Thanks for having me.
No worries. Tell me your role at Databricks and what Databricks does.
Yeah, first of all, Databricks is a data and AI company and our goal is to help organizations both big and small, anywhere from early-stage startups to Fortune 10 companies democratize data and AI. Essentially, our goal is to make AI usable within these enterprises. My role is helping support the Databricks startups team on the go-to-market side. Working hand in hand with our founders, helping bring founders into the Databricks ecosystem and build, you know, products, technologies, softwares on top of Databricks.
And how many startups do you work with now?
So we work with hundreds of startups that range anywhere from pre-seed to Series B, ranging anywhere from fintech companies, health tech, manufacturing tech, etc. And a lot of these startups are essentially working to disrupt their industry verticals, and they're trying to figure out how to, you know, build agents and agentic solutions to essentially disrupt the enterprises that they're selling into.
And who do you mostly deal with at these companies?
We're generally working with the founders and co-founders of these companies.
So they're like, what, 10 to 50 employees, or?
Yeah, it really ranges. I mean, now with AI, you're seeing, you know, 1 to 2 person companies come out with, you know, full-fledged products and they're finding product market fit super early on. You know, I think AI is really democratizing the way that, you know, a lot of these companies are able to build and able to build in the most efficient way as well. But it really ranges. I mean, sometimes, you know, you have, you know, these engineering-heavy or research-heavy teams that are, you know, trying to figure out, you know, how to solve, you know, the world's hardest problems. And so we're working mostly with technical teams to help figure out, you know, what that looks like and, you know, how to help them better go to market as well.
Is it a particular industry? Is it like marketing tech, biotech? Like, who do you work with? Who do you, or who's the best ideal customer profile?
Yeah, you know, it's interesting. I mean, right now we're seeing a lot of companies that are disrupting traditional SaaS. So there's a lot of companies right now that are, you know, for example, in go-to-market that are trying to figure out, you know, how to build and compete with the next Salesforce as an example. So a lot of software, mostly B2B, platform and software companies, but it really ranges. So we also see a lot in financial services as well. So a lot of fintech companies that are, you know, trying to figure out how to, you know, disrupt the banking industry and, you know, how does AI, you know, influence the financial services market. You know, you'd be surprised that a lot of these regulated industries are actually, you know, more ahead than a lot of people think in terms of adopting AI. And a lot of the startups that we're working with are, you know, trying to solve the challenge of, you know, selling into these, you know, large financial services or healthcare enterprises.
And how long have you been at Databricks, and like, what is your journey to get there?
Yeah, so I've been at Databricks now for 4 years, uh, this month in April. Um, I was at Microsoft for about 6 years before that in similar roles. I was started my career at Microsoft as a business development rep back when, you know, Azure and public cloud was just becoming a thing back in 2016. And a lot of these conversations were, you know, how to take an on-prem data center that was sitting in someone's closet and, you know, move that to public cloud. And a lot of it was, you know, just helping get people comfortable with the idea that, you know, their data is safe and secure and somewhere that it's not, you know, sitting where they can actually physically see it. So that was a lot of my conversations back at Microsoft, and then I was fortunate to, you know, continue to do well in various sales roles and go-to-market roles. My last role at Microsoft was leading go-to-market strategy for their small, medium, and commercial business unit, where I spent time, you know, trying to figure out the customer acquisition strategy for their digital sales team. So Microsoft was investing in these digital sales centers across the globe, and, you know, they're hiring a lot of these inside sales representatives that were focused on new customer acquisition. And so my role is trying to figure out how to, you know, combine account-based marketing as a lead funnel with our digital sales model, and then you go to market in the most efficient way possible, you know, primarily focused on, you know, Azure, their modern workplace solutions, and then Dynamics 365 as well.
If you could basically say in the last 6 to 12 months you've learned something that's incredibly helpful— strategy, tech, advice— or something that burns you and you're like, I want to tell folks on this podcast, like, you know, I did this, it wasn't a good idea, but I learned from it. So either learning from a good thing or a bad thing.
Yeah, I'll say one, you know, maybe more personal one from a career perspective is, you know, just saying yes to everything. I think a lot of, you know, my career has really come from saying yes to as many opportunities as possible, and then you'll start to see kind of a snowball effect of you know, good things happening, more opportunities coming from here. I think it kind of goes to the old saying of, you know, the harder you work, the luckier you get. And part of that is just saying yes to things that come up. You know, you're, um, in a role, whatever it might be, and, you know, you get a new opportunity, say yes to it and just see what comes from it. I think the flip side of that is when you start saying yes to too many things, Yeah. you start to naturally spread yourself you know, too thin. And that also isn't a good thing either, right? Because if you try to do, you know, a great job at everything, you're going to do a mediocre job at a lot of things. So I would say that you have to kind of find the inflection point of, you know, saying yes to things to a point and then trying to figure out how to say no.
Yeah, it's one of those things I did in the last 5 years where I was like, okay, I've been in sales for 20-something years, 25 years. I could build a go-to-market strategy for a team, which I did as a VP of sales. But why wouldn't I just do it for a bunch of companies and make $60,000, $70,000 a month versus $200,000 a year? So that's kind of the inflection point I think everybody should look at. Like, I've worked in the space. What's my shingle? What's my company going to be? Because, you know, someone like you and me, after a certain amount of years, you're like, I could probably do at least some of this. better, faster, cheaper. And if it's even a service, not even a whole, you know, like a whole platform and IP, people need your help. So that would be the one advice that I would give to someone like yourself. You know, think about 10 years from now, 5 years from now, like what's, what's next? How do you build up your thing? So tell me about who's on your LinkedIn and like what is LinkedIn since you're at Microsoft? What has it helped you kind of basically build out your whole strategy at all, or like what type of tools are working well for you on your own GTM?
Yeah, I mean, first of all, LinkedIn is great. I mean, from our perspective, you know, we use LinkedIn, you know, both as a way to, I think, you know, personally brand, you know, what we're doing and, you know, the conversations that we're having and, you know, essentially act as, you know, the face and of, you know, in this example, Databricks, right? So acting as the face of Databricks. for, you know, this particular segment that we're in and help, you know, provide that credibility for founders and co-founders of these startups. But it's also just great for things like this, right? You know, conversations that you and I are having as well, you know, wouldn't happen without tools like LinkedIn.
Without LinkedIn and Sean Greene.
Yeah, exactly.
Yeah. So tell us like who should get a hold of you and why they should get a hold of you. And maybe if you want to, I know we have your email address on your, on your video there, but yeah, is there any other thing that you want people to know before we sign off?
You know, I mean, email and LinkedIn is great. I mean, you know, from always happy to have a conversation with, with anyone. Um, you know, have a lot of conversations with early in career folks about, you know, getting into, you know, tech sales or tech go-to-market more broadly, and happy to give my point of view to the extent that people find it valuable. But, you know, any founders as well, always happy to help with founders on the go-to-market side, you know, talk all things AI, talk all things Databricks. So happy to help.
Is there a particular, like, for your specific specific approach where you got to go Series A, uh, seed round, and Series B, but you can't go past this revenue or this stage? Or do you have any—
We'll work with, we'll work with all, all startups that, um, you know, we, we try to find startups that, you know, can see value in something like Databricks, right? So a lot of times that are startups that, you know, they're just getting to market with a stitched-together product, you know, built on, you know, a bunch of cloud-native services or open-source tools. You know, there's a lot of value in consolidating that on top of Databricks. So we don't necessarily really look at, you know, revenue size or stage. Again, we're pre-seed to Series B is where we spend most of our time right now. But, you know, really startups across the board, across all industry segments and verticals.
Great. Well, thanks for your time. It's great to get to know you, and I look forward to keeping in touch.
