How Data Scientists Can Work Better With Engineers

Learning the right skills, owning investigative work, and collaborating on abstractions can go a long way

This was the best cover image for Data Engineering I could find, don’t @ me

“No update: the pipeline is broken, and we’re doing our best to fix it.”

It’s not realistic to ask Data Analysts and Scientists to own their entire production pipelines

Continuous Learning and Defining the ”Data Skill to Investment Matrix”

One of the best ways that data teams can help their engineers is by pushing themselves to learn new skills.

The Data Skill to Investment Matrix maps the usefulness of a new skill to how time-consuming it might be to become minimally proficient in it

“The Ticket Minimum” and Bug Communication

Data Analysts and Scientists should make it as easy and straightforward as possible for Data Engineering to fix broken pipelines or data issues.

Working Together to Create Abstractions

Data teams should work together to find areas where abstractions can improve workflows and communication.

dag-factory code is readable for team members who don’t speak Python, and enables more targeted asks to Data Engineering: “I don’t know which dependencies to add” is much better than “create this job for me”

The Basic Rule: Put Yourself In Their Shoes

growth @retool

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