Data engineering is something I stumbled into likely similar to many engineers. Many of underlying concerns are fairly ubiquitous and In addition to fairly typical business intelligence reporting asks I had fairly early experience designing a data warehouse solution and some supporting transformation logic as an effort to enable very different operational and analytical needs to vary more independently. I was subsequently briefly on a team that owned analytics and therefore helped design a more complete extract-transform-load (ETL) system which populated a warehouse style database along with a supporting visualization layer.
I’ve since spent a fair amount of time in more of a supporting/consulting role, helping to verify the work of teams more actively involved in crafting solutions and assist in working through integrations with operational data stores and challenges that may arise.
Fundamentals of Data Engineering provides a high-level survey of the data engineering space. In my opinion it remained too high level and general (and some of the details or implications seemed suspsect) without really carving out a distinct discipline and therefore it seems like it would be most valuable for either an engineer very early on in their career, or someone such as an analyst that does not have a software engineering background. For someone with an engineering background the vast majority of the content is likely to overlap with information which is better covered elsewhere and those aspects that are unique to data engineering are not treated in-depth. It arguably does firmly establish fundamentals but instead provides the aforementioned high-level survey and therefore seems most poised to guide readers to know where to look for further information rather than being a primary source in and of itself (which is by no means a bad thing though it likely could have been done more succinctly).