Books

Books can help to go deeper on a subject and really think some material through, try to go for actual exercises/projects over books though! Start with one that matches the work you want to do next. Several of the paid books are often available through a public library or O'Reilly access from an employer so this is a handy list to keep if you land a job!

Free to read online

Think Python by Allen B. Downey: a clear introduction to programming in Python, good for filling gaps in the basics.
Automate the Boring Stuff with Python by Al Sweigart: practical scripts for files, spreadsheets, and web pages.

Paid

Fundamentals of Data Engineering by Joe Reis and Matt Housley: an overview of the full data engineering lifecycle. Good first book after the track.
Designing Data-Intensive Applications by Martin Kleppmann: how databases, replication, and distributed systems work. Read it after the book above.
Fundamentals of Analytics Engineering by teacher Lasse Benninga and colleagues: For those that are interested in the career path of the Analytics Engineer, heavily focused on providing the business with quality datasets and reports using dbt and less focused on technical DE work like Spark/Kafka etc.

Honorable mention:

The Data Warehouse Toolkit by Ralph Kimball and Margy Ross: the reference for dimensional modeling (facts, dimensions, slowly changing dimensions). Originally released in 1996, this is considered the bible of Data Warehouse modeling. Less relevant for Data Engineers that are more concerned on the technical side (building pipelines, APIs etc) and more relevant for those that work mainly in the data warehouse with dbt and SQL, so not relevant for all DE's.

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