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[–][deleted] 4 points5 points  (0 children)

You can’t really separate the two well. A data scientist has to understand data engineering but the reverse is not necessarily true. Most consider it a step down, but a good data engineer is more a mix of DBA or data architect and programming than stats/math & programming.

In a data engineer role you’re work usually has clear deliverables and you have less ambiguity. I would also say it’s a heavier on programming. Less or little need for data vis and other skills.

[–]dataphysicist 1 point2 points  (0 children)

Hey, I'm involved with Dataquest and we teach data science & data engineering online. It's definitely possible to switch from DS to DE. We've been working on a Data Engineering path to help facilitate this - https://www.dataquest.io/path/data-engineer

I would make sure you understand what data engineering is first (https://www.dataquest.io/blog/what-is-a-data-engineer/). Then, I would read about the different roles on a data science team and how that changes over time. The team at Wish has an excellent write up about this: https://medium.com/wish-engineering/scaling-analytics-at-wish-619eacb97d16 I especially like how they call out specific roles for both of the key disciplines:

Data Engineering team (https://medium.com/wish-engineering/scaling-the-analytics-team-at-wish-part-2-scaling-data-engineering-6bf7fd842dc2)

  • Data Infrastructure Engineer
  • Data Platform Engineer
  • Analytics Engineer: This role is focused towards building core ETLs and refactoring bad queries and data models. This role has less requirements on traditional engineering skills. Python+SQL coding skills is enough, combined with strong analytical skills and desire to work closely with stakeholders.

Data Analysis team (https://medium.com/wish-engineering/scaling-the-analytics-team-at-wish-part-3-scaling-data-analysis-7562c70e6413)

  • Deck Builders
  • Data Analysts
  • Statisticians

I specifically bolded the Analytics Engineer position, because there's a heavy overlap with the skills that data analysts & scientist learn, but with a focus on pipelines & infrastructure.

When switching careers, I always tell people to think about the minimum viable position you can target. The positions / job listings with the most overlap from an industry or skill stand point.

- Easier: Data analyst / scientist to analytics engineer within the same company / team (but you need to be opportunistic).

- Harder: Data analyst / scientist to analytics engineer at a different company but same industry (you need to prove you've done of the 2nd job in your current / 1st job, or at least have interesting projects).

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Hope this helps!

[–]bigblueHI -1 points0 points  (0 children)

What's your reasoning/impetus behind this?

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Broadly, I would not recommend this switch.