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 We’re overwhelmed by technical jargon, stuck with outdated methods, and facing roadblocks from data teams.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 agenciesHe then moved over to healtech at CareCloud where he led Demandgen and ABMHe 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 MarketingAnd 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 companiesNavigating 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: M