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Data Engineer Spotlight: Rob McGinley

 

We sat down with this month’s Data Engineer Spotlight to discuss his data journey, his best practices and advice, and how he sees the data field changing in the future. 

Meet Rob McGinley

Beginning his journey in a startup that was generating terabytes of data per day, Rob quickly realized that simply reporting analytics would not be enough, and so began his lifelong work in data. Now a Principal Engineer for SentinelOne and an adjunct professor at LaSalle University for Data Science, Rob has a lot to say about how the data field has changed and how we can keep up. 

“We started using Hadoop–one of the first users of it back in 2010, 2011,” he says of the startup Monetate. “I was a software engineer that just happened to also do all the data work. But the next big thing–I was CTO of a startup called Zodiac. We did predictive lifetime value and we were acquired by Nike, an all-data company, so it was all around data. I was head of consumer data at Nike, and then left after a few years and now I’m at SentinelOne heading up building out their data ecosystem.”

What’s your favorite part of working with data?

“I really like driving the business value through predictions. Once you get to the part where you can start saying, ‘If you do this, that’ll happen,’ or ‘If things continue the way they currently are you’ll see this,’ you can predict so much. At Zodiac, we work with a lot of e-commerce clients and we predicted when particular retailers would declare bankruptcy based on their lifetime value predictions. Data is powerful.”

What’s the biggest current challenge facing the data industry?

“Junior folks can…lack distance thinking, so they tend to just do things one way and expect it to just work. You still need to know about latency and networking, and how networking works in general. There’s a lot of different pieces that they’ll also need to help them in their careers.”

Do you have advice for aspiring data engineers? What’s something you think aspiring data engineers need to learn the most?

“Slow down. Think about the simplest solution first. Instead of trying to build large, complex things. Start very simple and then add on as it makes sense. I’m very much for a common sense approach to building systems because when you build something big, you don’t know what all the edge cases will be, so you’re gonna have this Frankenstein from the get-go that you’re trying to fix all the time. If you build small things and then compose them together, you’re more likely to have something much more reliable.

“Don’t jump to the shiny thing that you think is cool; a lot of folks want to do the complex thing because it sounds cool. But complex things aren’t always exactly good for your team, your business or anything else. Your own sanity, in some cases, when you’re waking up at night to fix these things.”

How do you predict the data field will change in the next five years?

“Data automation will…eventually set up a good foundation for starting things so you can focus on the real problems. [Automation] is never gonna be able to optimize everything, so you’ll need to know how to do that, but then instead of you doing all the work to set things up, you’re focusing on the real interesting problems.”

What are your thoughts on data automation?

“I think it’ll solve like a lot of the boilerplate stuff. I see it as a setting up a good foundation for starting things so that you can focus on the real problems. We’re never going to be able to optimize everything, so data engineers with need to know how to do that, but then, instead of doing all the correct work to set things up, you get to focus on the real interesting problems.”

Rob’s Best Practices

As an engineer who has spent his career with data, Rob is not only developing his own methods but also teaching future experts, and breaking his advice down into easy, actionable steps: slow down, ask questions, and keep learning. The ability to keep up with and give back to the data community is why Rob McGinley is this month’s Data Engineer Spotlight! Check out his Linkedin.

If you know a data engineer who deserves recognition, contact us with their name and email address to nominate them for our Data Engineer Spotlight. 

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