Important 4 Data Engineering Skills that don’t get the proper hype.

First time I tried to shift from Data science and Machine learning to data engineering all my focus was on three main skills :

• Python : which helps you mostly in every step in the data engineering life cycle from source system through the ingestion steps till the final step which is the serving layer for ML, BI and Data Scientists.
• SQL: the most important skill in Data Engineering stack even more important than Python and it’s gonna last for many years since all the Data Warehouses support such as Snowflake, Azure Synapse, Aws Redshift and DataBricks.
• Data Engineering Concepts: such as DWH architectures, OLAP cubes, Star Schema, Snowflake Schema, difference between databases and data warehouses, Data lakes, Cloud tools, ETL vs ELT, DE tools such as Hadoop and hive, different types of dimensional modelling, etc.

But when I got my first job as a junior data engineer, I found myself stick with main 4 skills :

• First, SQL : more complex queries which have a lot of inner joins, nested queries, CTEs and ETL.

The second skill which I think it’s more important and I use it in day-to-day tasks is Linux.