There are 70% more open roles at companies in data engineering as compared to data science. As we train the next generation of data and machine learning practitioners, let’s place more emphasis on engineering skills.
This vibes with what I’ve been hearing on Practical AI lately. Organizations are facing big challenges when it comes to deploying, maintaining, and improving data processing tools and platforms in production settings. Big challenges produce big opportunities. And what does a data engineer do? According to this article:
Develops a robust and scalable set of data processing tools/platforms. Must be comfortable with SQL/NoSQL database wrangling and building/maintaining ETL pipelines.
If you have that skillset, you are in high demand today. And if you can adapt that skillset and be considered a ML engineer, you will be in high demand for a long, long time.