Engineers Shouldn’t Write ETL
&gt; The fundamental flaw that prevents the Thinker and Doer model from living up to its recruiting hype is the assumption that there exists an army of soulless non-mediocre Doer engineers who eagerly implement the ideas and vision of data scientists.<p>There&#x27;s a large, active community of engineers who specialize in data, whose job is to technologically enable data scientists the means to perform their analyses. I know these people exist because I&#x27;m one of them, and I work with them, and I&#x27;ve met them at meetups and conferences. I don&#x27;t know why the author doesn&#x27;t think these types of engineers exist. Not all of us who code want to work with the web.<p>&gt; If you read the recruiting propaganda of data science and algorithm development departments in the valley, you might be convinced that the relationship between data scientists and engineers is highly collaborative, organic, and creative. Just like peas and carrots.<p>Almost every data team I&#x27;ve worked with is structured this way. I work daily with data scientists. I have a data scientist sitting to my right, two data scientists sitting across from me. Our teams are highly integrated and I can&#x27;t imagine it working any other way. If the teams the author is familiar with don&#x27;t operate in this manner, then I can see why he&#x27;d think the endeavor is hopeless.<p>I also disagree with the author&#x27;s conclusion. The data scientist&#x27;s job is to analyze and interpret data. They should not be spending any time thinking about how to get that data. They should not be concerned about where the data is coming from. The more time scientists have to spend thinking about ETL, the less time they have to do what their training is in, statistical analysis.