3 Best Practices for Bridging the Gap Between Engineers and Analysts | by Madison Schott | Apr, 2024

Assigning code owners, hiring analytics engineers, and creating flywheels

Towards Data Science
Photo by Alex Radelich on Unsplash

As an analytics engineer, one of the most challenging problems I face is bridging the gap between engineering and analytics. Engineering and analytics are often siloed into their own teams, making cross-collaboration quite difficult.

Engineering pushes software and data changes that analytics knows nothing about. Analytics is then forced to pivot its work to accommodate these changes. Or worse, analytics must suggest a change that engineering needs to then fight into their tight schedule.

If the gap between the teams becomes too wide, it adds a lot of time, struggle, and data quality issues to the already piling amount of work. This is why it’s imperative to implement best practices for communicating with one another from the moment this issue is spotted.

In this article, I’ll discuss some of the most common problems I’ve faced and the best practices you can apply to solve them.

Often there is a lack of ownership of logic built out in code, especially between analytics and engineering. Who owns the code that generates…

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