We discuss the dynamics & challenges behind building out your data engineering function with Taylor Murphy (Head of Product & Data @ Meltano). Taylor also shares his recommendations for collaborating in multi-stakeholder environments, the relationship between the data team & engineers, why you should run your data team like a product team, gaining buy-in around the ROI of data, and what it’s like being a company’s first data eng hire. Featuring guest co-host/community member John Wang (Director of Engineering @ Petal)!
Taylor Murphy (@tayloramurphy) is the Head of Product and Data of Meltano, an open source data platform that enables collaboration, efficiency, and visibility. Taylor has been deeply involved in leading and building data-informed teams his entire career. At Concert Genetics he scaled the Data Operations team to enable the management of hundreds of thousands of genetic tests and millions of claims records. At GitLab, he was the first data hire where he focused on building and scaling the data organization as the company headed towards its IPO. Taylor has been involved with Meltano since its inception, acting as the primary customer with whom the team engaged to understand the needs of modern data professionals.
"What next? I put this in a dashboard. What are you gonna do with this? I think a lot of people are like, ‘I wanna be more data-informed and I wanna build up a robust data organization.’ And that's where they stop. They think, ‘Okay, I'm gonna get the data, and then I'm gonna make a decision.’ And that's not good enough because there's always gonna be more work to do than you can accomplish. And every time you deliver a dashboard, they're gonna go, ‘Oh, this is great. What if we did this, this, and this?’ And that's fun for data people and you enjoy that, but you kind of wanna help them think through these things.”
- Taylor Murphy
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- Taylor’s early days at GitLab as its first data eng hire (2:23)
- How Meltano evolved out of a GitLab business intelligence project (4:21)
- Using data eng to discover problems & iterate solutions (6:01)
- Why having data teams under finance can be detrimental to product strategy (7:22)
- The importance of data eng representation at the VP level (10:01)
- The ideal time to build your data eng team (12:57)
- Communicating the ROI of data eng functions (14:53)
- How to gain stakeholder buy-in (17:04)
- Prioritizing qualitative data in a solution’s early stages (18:54)
- Taylor’s recommendations for the early-stages of building your data org (20:05)
- Roadblocks when building data teams – and solutions for success (22:42)
- The “run your data team like a product team” thesis (25:52)
- Best practices for applying product team principles to your data eng functions (27:57)
- The hand-off between data engineering & the broader engineering org (29:08)
- Navigating company politics from a data eng perspective (33:02)
- Prioritization conversations between data eng & other stakeholders (36:25)
- Rapid-Fire Questions (38:31)
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