Alex Woodie points out that data science also requires data engineers:
The shortage of data scientists – those triple-threat types who possess advanced statistics, business, and coding skills – has been well-documented over the years. But increasingly, businesses are facing a shortage of another key individual on the big data team who’s critical to achieving success – the data engineer.
Data engineers are experts in designing, building, and maintaining the data-based systems in support of an organization’s analytical and transactional operations. While they don’t boast the quantitative skills that a data scientist would use to, say, build a complex machine learning model, data engineers do much of the other work required to support that data science workload, such as:
Building data pipelines to collect data and move it into storage;
Preparing the data as part of an ETL or ELT process;
Stitching the data together with scripting languages;
Working with the DBA to construct data stores;
Ensuring the data is ready for use;
Using frameworks and microservices to serve data.
Read the whole thing. My experience is that most shops looking to hire a data scientist really need to get data engineers first; otherwise, you’re wasting that high-priced data scientist’s time. The plus side is that if you’re already a database developer, getting into data engineering is much easier than mastering statistics or neural networks.