What’s up everyone, today we have the pleasure of sitting down with Danny Lambert, Director of Marketing Operations at dbt Labs.

Summary: Marketers often feel like they're battling a dragon when it comes to integrating data. We’re overwhelmed by technical jargon, stuck with outdated methods, and facing roadblocks from data teams. Danny walks us through his journey of cautiously entering the data world and the role dbt can play for marketing teams. By learning just enough SQL, knowing what tools you need to get started with and leaning on dbt’s tools, you can start small and gradually build a warehouse-first martech stack. The reward is more control over your data, flexibility to deploy personalized campaigns independently, and a competitive edge that no pre-packaged solution can match.

About Daniel

Danny started his career at an event solutions company where he wore several different marketing hats including getting his first taste of marketing automation
He then worked in marketing ops at IZEA, at marketplace that connects brands with influencers before having a short stint at McGaw.io one of the leading martech and analytics agencies
He then moved over to healtech at CareCloud where he led Demandgen and ABM
He then transitioned to Rev.com the popular transcription company where he started in marketing ops, then demand gen before being promoted to Director of Integrated Marketing
And today Dan is Director of Marketing Operations at dbt Labs, the creators of the most popular software for data transformation used by data engineers at more than 20k companies

Navigating the Disconnect Between Marketers and Data Teams

Many marketers struggle to engage with data teams because they feel worlds apart. Danny points out that it’s a lot like the early days of marketing’s relationship with product teams. Before product-led growth (PLG) became a buzzword, marketers and product teams operated in separate silos. It took a concerted effort to break that wall, and the same shift is needed with data. Marketers often find the mechanics of data engineering and warehousing intimidating, and for good reason—they weren’t trained for it. But it doesn’t have to be that way.

Danny recounts his time at CareCloud, where he was exposed to the concept of a data warehouse. The idea was gaining traction, and he attended a Snowflake event to grasp the essentials. After an hour of slides and schemas, he walked out just as confused as when he walked in. The issue wasn’t the information; it was the delivery. Marketers need to see things in action. Theoretical talks don’t cut it—practical, straightforward tutorials that walk you through the steps are what marketers crave. Installing tools like dbt and seeing data move can make it all click. It’s the difference between hearing about a new tool and actually feeling it work in your hands.

There’s also a major gap in educational resources that cater to marketers. As Danny highlights, marketing professionals who want to embrace data often get lost in the flood of courses and jargon-heavy materials. It’s a jungle out there—marketers want concise, actionable guidance, not a deep dive into tech theory. Without the right content, many opt to stay in their lane, using tools and methods they already know. It feels safer, especially when they’re under pressure to perform quickly.

Danny points out that this pressure to ramp up fast can discourage experimentation with a warehouse-first approach. New roles often come with tight timelines, and there’s a tendency to lean on old habits. Shifting to something like data warehousing means slowing down, learning the ropes, and building enough belief in the new approach to back it up internally. But if you’ve spent years doing things differently, it’s hard to develop the conviction needed to push for change. Confidence comes from exposure and understanding, but without that, the warehouse-first idea feels too foreign to champion.

Key takeaway: Marketers often shy away from data teams because they lack practical, accessible education and feel pressured to stick with familiar methods. Building confidence through hands-on learning and real-world examples is crucial for integrating data and marketing in a meaningful way.

Overcoming Barriers to Data Literacy in Marketing

Many marketers hesitate to engage deeply with data, often because they don’t see it as central to their roles. Danny explains that for most, data feels like a secondary tool—something meant to assist rather than dominate their day-to-day work. The challenge is that the pathway to becoming data-savvy isn’t straightforward. Even among those who’ve made the leap, each person’s journey looks different. Some take online courses, like those on Codecademy, learning SQL from scratch. Others find mentors who guide them through the maze of data management, or they happen to work in environments where they can lean on a data specialist nearby. But there’s no universal roadmap, which makes the process feel daunting.

Danny believes that the lack of a clear, predictable path to mastering data is one of the biggest hurdles marketers face. With so many options available—some technical, others more hands-on—marketers often struggle to identify which approach will actually get them the skills they need. For those with limited time, this uncertainty can be a dealbreaker. Without knowing if the investment will pay off, it’s easier to focus on other areas of marketing that feel more familiar and essential. Danny points out that while resources like Udemy are improving the situation, marketers still need a straightforward, reliable way to become proficient in data.

Another critical factor is the perceived opportunity cost. Marketers are often juggling multiple responsibilities, from staying up-to-date with industry trends to managing campaigns. For many, the idea of dedicating time to learning data—an area they may feel they have minimal expertise in—feels like too large a barrier. Why spend time learning about data warehousing when there are immediate, pressing marketing concepts to master? This fear of committing time and energy to an unfamiliar, complex area keeps many from taking the first step.

Danny emphasizes that while the accessibility of learning tools is improving, there’s still a significant gap. Even for those who want to upskill, the fear of the unknown and the lack of a guided pathway can make it feel like an insurmountable challenge. Until marketers can see a clear, accessible way to develop these skills, many will remain hesitant to dive into data, choosing to stick to familiar ground instead.

Key takeaway: Marketers often shy away from learning data skills due to a lack of accessible, consistent learning paths and the fear of time investment without guaranteed outcomes. Creating structured, easy-to-follow resources is crucial to making data literacy a viable option for busy professionals.

Unlocking the Full Potential of Data with dbt

Danny describes the transformation dbt brings to the data landscape, making it accessible not just to engineers but also to marketing ops and other non-engineering teams. In the past, accessing and manipulating data was a highly specialized skill, often requiring a marketer to rely heavily on a single engineer. As Danny puts it, you needed to build a relationship with this “one person in a closet somewhere” to get any insight or change implemented. This old approach made data access exclusive, slow, and frustrating for teams trying to move fast.

With dbt, Danny explains, the dynamics shift dramatically. It creates different roles and permission levels for everyone interacting with data, enabling a self-service model for marketers and operat...

143: Danny Lambert: A guide to data transformation and building a warehouse-first martech stack

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