Summary
The responsibilities of a data scientist and a data engineer often overlap and occasionally come to cross purposes. Despite these challenges it is possible for the two roles to work together effectively and produce valuable business outcomes. In this episode Will McGinnis discusses the opinions that he has gained from experience on how data teams can play to their strengths to the benefit of all.
Preamble
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- Your host is Tobias Macey and today I’m interviewing Will McGinnis about the relationship and boundaries between data engineers and data scientists
Interview
- Introduction
- How did you get involved in the area of data management?
- The terms “Data Scientist” and “Data Engineer” are fluid and seem to have a different meaning for everyone who uses them. Can you share how you define those terms?
- What parallels do you see between the relationships of data engineers and data scientists and those of developers and systems administrators?
- Is there a particular size of organization or problem that serves as a tipping point for when you start to separate the two roles into the responsibilities of more than one person or team?
- What are the benefits of splitting the responsibilities of data engineering and data science?
- What are the disadvantages?
- What are some strategies to ensure successful interaction between data engineers and data scientists?
- How do you view these roles evolving as they become more prevalent across companies and industries?
Contact Info
- Website
- wdm0006 on GitHub
- @willmcginniser on Twitter
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- Blog Post: Tendencies of Data Engineers and Data Scientists
- Predikto
- Categorical Encoders
- DevOps
- SciKit-Learn
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA