#180 The rise of engineers in the data analytics space with Felipe Flores, host of Data Futurology

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As the data analytics space matures demands from the business for the products and services being generated has been increasing. There is a thirst and appetite for internal and external data and analytics outputs. However, we need visible platforms and the ability to orchestrate complex data pipelines to enable this growth and underpin the mechanism.

Cue the emergence of the engineer. We started with the data engineer and have welcomed more specialised roles including the Analytics Engineer, ML Engineer and AI Engineer. Heavily leveraging from the IT side, these roles have evolved and are becoming integral to the business. 

How exactly is the engineer in the data analytics space evolving? How is this enabling reliable accessible, performant systems that are monitored appropriately? Can we look at team structures? Are there ways we can implement or better use the modern data stack?

As an industry we’re maturing, we’re further specialising and we’re reacting to the demands of the business. We’re seizing the opportunity to create more value for our organisations!

That’s what Felipe will discuss in this week’s podcast episode.

Enjoy the show.

Thanks to our sponsor Talent Insights Group!

“It’s critical, and it’s something that we’ve been doing as an industry.”

— Felipe Flores, host of Data Futurology

Episode Highlights

So, there's definitely a big push there, where we will we as an industry will continue to learn from Cloud engineers.

Those have become more and more integral to the organization, more and more integral to the way that people work the processes.

You know, stand things up on the cloud, being able to take them down from a cloud engineer are some of the things that we've learned from Cloud engineering, is things like, treat your infrastructure as cattle, not as pets

When I first heard it, I loved it, I loved it. And the difference there is that, and it can be a bit morbid, in terms of an analogy, but it's, but it's a good one.

That's, that's what we want with our cloud infrastructure, something that, you know, ideally is infrastructure as code so, so very sort of Terraform heavy, something that we can stand up, use, break up, break down the lead, if we need to stand up multiple different copies, being able to do that in a repeatable and reliable way.

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