I found a pattern: if we assume database=>warehouse=>ETL=>Viz=>AI/ML is the ladder of data science pyramid, it seems that the closer to the bottom, the less variant of options are used. For example, Company D only uses SQL server for data storage while almost everything in AI/ML.So my takeaway from your sharing is that:1. it takes more efforts, time and qualifications to be a data scientist than a data engineer;2. the data engineering plays an important role in a company, because of its nature of elementalness.

Grad student @ Boston University, aspiring data scientist and sports analyst. Buy me a coffee now(https://www.buymeacoffee.com/MemphisMeng)!