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As cliche as it may sound, getting close to the "business value" of data might be a good investment. Learning and building use cases, like a product recommendat
by soumyadeb 4y ago
As cliche as it may sound, getting close to the "business value" of data might be a good investment. Learning and building use cases, like a product recommendation pipeline for marketing OR a churn model for support to helping marketing ops migrate from GA (GA4) to an in-house analytics stack etc would help bring the data teams to the (much deserved) front row seat in the organization.
- panda888888 4y agoTo be honest, that sounds like analytics/BI or data science, not data engineering. Are you suggesting OP pivot their career into one of those roles?
- soumyadeb 4y agoWell, most practical ML algorithms (liner/logistic regressions etc) should be in the wheelhouse of most engineers. I read a comment somewhere "Its much easier to teach data-science to an data-engineer than vice versa" :) I think, if we data-engineers can step up the conversation from frameworks/databases to end-to-end use case, we will get more respect (and everything that comes with it like budget) from the rest of the org.
- mateo411 4y agoI think a good data engineer should incorporate some basic data analysis into their toolkit. This is what analytics/BI developers do all day everyday. Data Engineers need to do this whenever they create a new dataset. It's not hard to pick up this skillset, and this sort of analysis doesn't take a lot of time, and it will make you a really good Data Engineer.
- codeisawesome 4y agoWhat’s a good authoritative source that tracks use-cases across industries for data? I often find resources that are either too shallow and high-level (literally one-line) or a super deep dive on a singular use case.