[00:00:00] ​Intro

[00:00:25] Phil: What’s up everyone. Today, we have the pleasure of sitting down with Danny Lambert, Director of Marketing Operations at dbt Labs.

[00:00:33] About Danny

[00:00:33] Phil: Danny started his career at an event solutions company, where he wore several marketing hats, including getting his first taste of marketing automation. He then worked in marketing ops at IZEA, a marketplace connecting brands with influencers, before a stint at maga.io, one of the leading martech and analytics agencies. After that, he moved into health tech at CareCloud, where he led demand gen and ABM, then transitioned to Rev.com, the popular transcription company, where he progressed from marketing ops to demand gen, and finally, Director of Integrated Marketing. Now, Danny is the Director of Marketing Operations at dbt Labs, creators of the most popular data transformation software used by data engineers at over 20,000 companies. Danny, thanks so much for being here. I’m pumped to chat.

[00:01:20] Danny: Thanks for having me, Phil. I’ve been listening to the show during my morning walks and commutes, now that we’re going back to the office a couple of days a week. I’ve really been enjoying it, so I’m excited to be here and share some insights with your audience.

[00:01:33] Phil: Awesome. We’ve got a packed episode today. We’ll keep it high-level for those unfamiliar with terms like data transformation and building a warehouse-first martech stack, but we’ll also dive deep for the more seasoned folks out there.

[00:03:46] Navigating the Disconnect Between Marketers and Data Teams
[00:03:46] Phil: The idea of working with data teams fascinates me. For many marketers, a warehouse-native approach to building a martech stack feels foreign. It was foreign to me just a few years ago—warehouse as a term felt like something from engineering meetings, not something marketers needed to think about. But times have changed. You’re at a very data-centric company, so I’m curious—what do you think are the misconceptions marketers have about working with data and data engineers?

[00:04:30] Danny: It feels very similar to working with product teams. Marketing often struggles to understand how to interface with them, especially when product-led growth (PLG) became big. There’s a disconnect in having a shared reality and understanding how product development impacts PLG efforts. Data teams can feel similarly siloed—hard to access, especially for non-technical marketers or ops people.

[00:05:09] I remember going through this myself at CareCloud. I was hearing a lot about data warehouses, but it all felt theoretical. I went to a Snowflake event and sat through it thinking, "I don’t get it." And I think that’s part of the issue. There’s a gap in practical understanding, which prevents adoption. You hear about it, but how do you actually apply it? Seeing theories laid out with schemas on slides doesn’t make it tangible.

[00:06:09] If you had a simple, practical guide showing how to set up ingestion or install dbt, it would be easier. But I think marketers often steer clear because there’s a lack of practical, accessible education around this. They don’t know where to start, and that can be intimidating.

[00:07:33] Overcoming Barriers to Data Literacy in Marketing

[00:07:33] Phil: What do you think are the biggest fears or reasons behind that hesitation? Like, you were motivated enough to go to a Snowflake event, but what stops other marketing teams from taking that first step?

[00:08:02] Danny: I think it’s because data feels foreign. For many marketers, data isn’t seen as their primary role—it’s supposed to assist them, not something they dive into deeply. The path to learning isn’t always clear. Some people take SQL courses on Codecademy, some have mentors, and others might just have colleagues they can ask for help. Everyone’s path is different, and that can be daunting if you don’t have a clear starting point.

[00:09:25] Phil: Yeah, and for a lot of marketing ops folks listening, they’re probably thinking, “We’re already swamped. Why should we care about data transformation?” What’s the biggest opportunity you see for marketers in taking that first step?

[00:10:03] Danny: For me, it was about reaching a pain point where I had no choice but to figure it out. At Rev.com, we had Segment set up and a multi-touch attribution tool, but it wasn’t giving us what we needed. The tool was cookie-cutter and couldn’t account for the nuances of our business. So, I thought, “Why can’t we do this ourselves?” We were already capturing the event stream, so I knew we could write a raw SQL query to get what we needed. Eventually, that’s what led me to implement dbt at Rev.

[00:12:42] Phil: That’s awesome. Shoutout to Julie Beynon too. We had her on the show early on, and she was the one who unlocked the world of data warehousing for me as well.

[00:13:00] Danny: Julie is fantastic. She’s really helped the marketing ops community level up in terms of understanding data.

[00:14:35] Unlocking the Full Potential of Data with dbt

[00:14:35] Phil: Let’s dive into dbt. It’s transforming how companies approach data and is well-known in the data world. Do you think dbt is also powerful for less technical teams, like marketing ops folks? How does it serve as a bridge for marketers looking to work more effectively with data?

[00:17:16] Danny: Absolutely. dbt changes the game by democratizing access to data. Before, only engineers could manage data pipelines. With dbt, there’s a role for everyone in the data lifecycle—whether you’re an analyst, an ops person, or a business leader. You can have read-only access to metrics or dig deeper into transformation models. This makes it easier for marketers to take ownership of their data and work faster without being dependent on data teams.

[00:21:00] Phil: That’s super interesting. I’m sure some marketing folks are thinking, “I’d love access to these tools, but the data team keeps it all behind closed doors.” How can marketers start collaborating with data engineers and make that case for access?

[00:23:00] Danny: The first step is understanding your data infrastructure. Do you have a cloud data warehouse? If so, it’s easier to get buy-in from the data team by proposing small, incremental steps. Start by asking for read-only access and build trust from there. You can show that you’re willing to take some work off their plate, which most teams appreciate.

[00:24:00] Danny: If you have dbt, it’s easier to have that conversation because you can request access with guardrails in place—like submitting PRs for the data team to review. It’s about incrementally taking work off their plate, showing that you’re ready to contribute without disrupting workflows. If you don’t have dbt, then it’s about understanding its value and communicating that to the team.

[00:25:00] If you’re in an organization without a warehouse, your starting point is different. You’ll need to build buy-in for a cloud data warehouse first. Fortunately, there are quick-start guides for warehouses like BigQuery, Snowflake, and Redshift. Start there, learn the basics, and gradually build up your skill set.

[00:26:00] Phil: Totally. And once you get the buy-in, where should marketers begin? What tools and workflows should they focus on?

[00:26:34] Building a Strong Foundation for Data Transformation

[00:26:34] Danny: If you’re just starting, you’ll need a few key tools. First, you’ll need an extraction tool like Stitch or Fivetran to sync your data from various platforms into your warehouse. Then, you’ll need a cloud data warehouse—BigQuery, Snowflake, Redshift, etc. There are free or low-cost options for all of these.

[00:27:00] Then comes dbt, which helps you transform the raw data into something useful. With dbt, you’re essentially just writing SQL to clean and join data. Once that’s done, the last piece is reverse ETL—tools like Census or Hightouch—so you can send your transformed data back into the tools your teams use, like CRMs or marketing automation platforms.

[00:27:39] If you don’t know SQL, I’d recommend brushing up on it. It’s a foundational skill, and dbt makes it easy by requiring only SQL knowledge to transform data. Once you’ve done it once, it gets faster and easier.

[00:28:00] Phil: What about teams that already have a Frankenstack—maybe a mess of systems and workflows in place? How should they approach migrating to a warehouse-first approach?

[00:28:35] Effective Migration Strategies for a Composable Tech Stack

[00:28:35] Danny: If your tech stack is mature but messy, you might want to consider a migration partner to help you. But for most teams, the key is getting your taxonomy right from the start. Work with your data team to ensure there’s a clear flow—from raw data to the final exposure. For us, that involves setting up models in dbt that follow a clean, logical structure.

[00:29:00] Then, prioritize the most important data products first. Start with your most used reports—like pipeline or customer 360 dashboards. Get buy-in by delivering high-value wins first, and work your way through the rest incrementally.

[00:30:00] Focus on building alignment within your organization. At dbt, we use functional pods—a marketing pod, a revenue pod, etc.—each with a data engineer, a data analyst, and other key players. This structure helps us prioritize cross-functional projects more effectively.

[00:31:00] Phil: That’s a great point. You can’t tackle everything at once. It’s about incremental progress and starting with the highest-impact areas.

[00:31:44] Danny: Exactly. The biggest challenge is deciding where to begin. But once you start, it’s a rinse-and-repeat process. You’ll see how much value it adds, and that makes future projects easier to push through.

[00:32:00] Phil: So, once you’ve succeeded in pitching the warehouse-first approach and going through the migration, what’s the real payoff? How does this fundamentally change marketing’s ability to deliver personalization or effective campaigns?

[00:33:00] Unlocking Flexibility Through a Warehouse-First Approach to Martech

[00:33:00] Danny: The main payoff is flexibility. Once you have your data in a warehouse and transformed, you’re no longer reliant on other teams. For example, with dbt, we’ve built out everything from our prospecting command center to our campaign reporting. These tools let us do things like real-time personalization, creating audiences based on data from across the business, and sending that audience data to any platform we need, like HubSpot or Pendo.

[00:34:00] Without this infrastructure, trying to personalize would be a nightmare. Imagine writing a SQL query that pulls customers who churned last year, have fewer than two active seats, and have visited our site in the past five days. With dbt and reverse ETL, that’s possible—and it’s all automated.

[00:34:50] It also changes how you approach big initiatives. For example, when we were building multi-touch attribution at Rev.com, none of the off-the-shelf vendors could handle our unique context. By using dbt to build our own model, we had the flexibility to do exactly what we needed.

[00:36:00] Phil: I love that. And it’s also about future-proofing, right? If a hot new tool or feature comes out, you don’t have to wait years for a package solution to integrate it.

[00:36:35] Danny: Exactly. You can move much faster. It’s about having control over your data so you can plug and play with whatever new technology comes out. It gives you a competitive advantage because you’re not waiting on a vendor’s timeline to adopt the latest tools.

[00:38:35] Phil: For sure. And you also mentioned earlier that for marketers collaborating with data teams, there are terms like reverse ETL, taxonomies, and data models that can feel overwhelming. What advice do you have for marketers diving into these technical areas for the first time?

[00:38:58] Building a Strong Foundation for Data Transformation

[00:38:58] Danny: Start by focusing on the basics. You’ll need to understand your event capture practices, how your data is modeled, and how it flows through your organization. If you’re new to this, start with foundational tools like Fivetran or Stitch for data extraction, then move to dbt for transformation.

[00:39:33] The most important thing is to work closely with your data team on taxonomy and structure. Make sure that from the raw data to the final product, everything is standardized. That’s where dbt shines. It helps you take that raw data, clean it up, and ensure everything is well-organized.

[00:40:00] Once you have the basics in place, you can use tools like Census or Hightouch to activate your data, sending it back to marketing platforms for personalized campaigns.

[00:40:33] Phil: That’s a great roadmap—simple but effective. And what about companies that already have some infrastructure in place but need to migrate to a more flexible, warehouse-first approach?

[00:43:15] Effective Migration Strategies for a Composable Tech Stack

[00:43:15] Danny: It depends on the scale of your setup. If your system is already large and mature, it might be worth hiring a migration partner to help. But for many companies, it’s about simplifying and getting the taxonomy right from the beginning.

[00:43:50] Work closely with your data team to define clear workflows—what happens from raw data to transformation to exposure. Then focus on the high-priority areas first. What’s the most-used report or dashboard in your organization? Start there, make it better, and move forward incrementally.

[00:44:25] Phil: Tackling everything at once can definitely be overwhelming. It’s about those small wins that build momentum.

[00:45:00] Danny: Exactly. Once you knock down that first major use case—like a pipeline or customer 360 dashboard—it becomes much easier to build on top of that.

[00:46:00] Phil: And in terms of flexibility, what other payoffs have you seen after adopting the warehouse-first approach?

[00:48:06] Unlocking Flexibility Through a Warehouse-First Approach to Martech

[00:48:06] Danny: Flexibility is by far the biggest benefit. We can personalize at a much more granular level, build custom reports, and even create new dashboards on the fly. For instance, we built a prospecting command center that tracks every touchpoint with a prospect across dark social, website visits, community engagement—you name it. That type of detail is invaluable for sales teams and would have been impossible with off-the-shelf tools.

[00:49:00] The same goes for our campaign reporting. Instead of just tracking how many ops a campaign generated in Salesforce, we can now track performance across web, email, and even cost data pulled directly from our accounting systems. It gives us a full picture of what’s happening, not just the final output.

[00:50:00] Phil: That’s next-level reporting. And like you said, this level of personalization would be impossible without the flexibility a warehouse-first approach offers.

[00:51:50] Danny: Yeah, the ability to control your own destiny when it comes to data is huge. You’re no longer waiting for vendors or external teams to deliver what you need.

[00:51:50] Phil: Yeah, the ability to control your own destiny when it comes to data is huge. You’re no longer waiting for vendors or external teams to deliver what you need.

[00:52:00] Danny: Exactly. The flexibility extends beyond just the tools you’re using. With a warehouse-first approach, you can easily integrate data into workflows and tools that weren’t originally designed to work together. It opens up so many possibilities.

[00:53:35] Building Trust and Skills for Data Access in Marketing Ops

[00:53:35] Phil: One thing that sticks out to me is how you’ve created this prospecting events log, which gives your sales team visibility into every touchpoint. That’s huge for personalization. It’s almost like you’re unlocking what multi-touch attribution tries to summarize into a few key points, but instead, you’re providing the raw data to personalize outreach.

[00:54:13] Danny: Exactly. And it’s not just the sales teams who benefit. This type of real-time data flow feeds into everything we do, from campaign reporting to customer success. We have complete visibility over the customer journey. That level of insight lets us act faster and more effectively.

[00:54:31] Phil: That makes so much sense, especially when you talk about how to support new tools like autonomous SDR platforms. Many of these tools are promising but limited in what they can do unless they have access to the right data. You’ve built a system that gives them that rich data stream, which makes them far more effective.

[00:55:22] Danny: Exactly. We’ve seen it firsthand. These new tools are powerful, but they need good data to work. Without that, their potential is capped. By feeding them high-quality data from our warehouse, we’re unlocking their full capabilities. It’s a game-changer.

[00:56:00] Phil: Let’s switch gears a bit. You’re a technical marketer, you’re into real estate, DIY projects, motorcycles—you’ve got a lot going on. One question we ask every guest is: How do you stay happy and balanced with everything you’re juggling at work and at home?

[00:56:15] Escapism as the Key to Work-Life Balance

[00:56:15] Danny: For me, it’s all about having an escape. I respect people who can focus on their job all day and then dive into reading or researching their field in the evening. But that’s not me. I need a break to recharge. Real estate, DIY projects, motorcycles—those are my outlets. They challenge me in different ways and give me a sense of accomplishment outside of work.

[00:56:52] I’ve always been an adrenaline junkie, so I need to get my heart rate up sometimes—whether that’s on a motorcycle or through Brazilian jiu-jitsu. It helps me reset mentally. And DIY projects slow me down. They force me to be intentional, take my time, and think things through. Each activity serves a different purpose, but they all help me recharge so I can stay focused at work.

[00:57:58] Phil: I love that you used the word “escape.” It gets a bad reputation sometimes, like it means you’re running away from something. But really, healthy escapism is just about giving yourself space to breathe and come back refreshed.

[00:58:33] Danny: Exactly. You need that balance. If you were on vacation all the time, you’d eventually need a break from that too. The key is finding activities that take you out of your daily routine and give you a mental reset. For me, that’s been crucial to maintaining focus and energy at work.

[00:59:00] Phil: Totally agree. I remember when we had Scott Brinker on the show, and he mentioned that after a full day of working in martech, he comes home and reads about martech. That’s not for everyone, but it’s what keeps him going.

[00:59:31] Danny: Yeah, some people thrive on that. But for me, I need those other outlets to stay balanced. Everyone’s different, and I think it’s about finding what works for you.

[01:00:00] Phil: Absolutely. Well, Danny, this has been such a great conversation. I really appreciate you taking the time to dive deep with us today. We’ll be sure to share the links to the dbt quick-start guides and other resources for anyone looking to explore this space. You’ve shared so much valuable insight, and I’m sure our listeners are going to get a lot out of it.

[01:00:25] Danny: Thanks for having me, Phil. It was a pleasure to be here.

[01:00:30] Phil: Awesome. Take care, Danny.