The Year Of The Data Engineer

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.

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