Is data science merging with data engineering?

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Editor’s note: The Towards Data Science podcast’s “Climbing the Data Science Ladder” series is hosted by Jeremie Harris. Jeremie helps run a data science mentorship startup calledSharpestMinds. You can listen to the podcast below:

There’s been a lot of talk about the future direction of data science, and for good reason. The space is finally coming into its own, and as the Wild West phase of the mid-2010s well and truly comes to an end, there’s keen interest among data professionals to stay ahead of the curve, and understand what their jobs are likely to look like 2, 5 and 10 years down the road.

And amid all the noise, one trend is clearly emerging, and has already materialized to a significant degree: as more and more of the data science lifecycle is automated or abstracted away, data professionals can afford to spend more time adding value to companies in more strategic ways. One way to do this is to invest your time deepening your subject matter expertise, and mastering the business side of the equation. Another is to double down on technical skills, and focus on owning more and more of the data stack —particularly including productionization and deployment stages.

My guest for today’s episode of the Towards Data Science podcast has been down both of these paths, first as a business-focused data scientist at Spotify, where he spent his time defining business metrics and evaluating products, and second as a data engineer at Better.com, where his focus has shifted towards productionization and engineering. During our chat, Kenny shared his insights about the relative merits of each approach, and the future of the field.

Here were some of my favourite take-homes from our conversation:

You can also follow Kenny on Twitter here, and me here.