"Algorithm" has become a default answer whenever:
they don't know how it works;
or, they don't care to explain it
Algos are everywhere and their ever-presence fades into the background - largely unnoticed but impacting everything we do / see / feel.
When many algorithms coordinate together they facilitate "machine learning" - a feat of computer science and statistics capable of predicting the future with extraordinary success rates.
Regression is incredible, but most ML is deployed toward classification and clustering problems.
These revolve around figuring out organizational structures in conjunction with a data-driven understanding of what Class a given datapoint "belongs" in.
Classification and clustering together form a powerful framework to understand and influence the universe.
The data that is fed into these ML models is inarguably more important vs ANY of the models.
How does the data get to the model?
What is the source?
What impels the 'correct' data into the model... and while we are at it..
How do we know what the correct data is?
Data engineers are the folks building the infinite pipeline of data from a growing number of APIs, Feeds, Streams and other data sources.
Some of the data lives in silos on-premise. Other data are unified in the cloud. Most are unintentionally executed a hybrid multi-cloud strategy with lots of gaps in lineage.
Data engineers harness data, move it efficiently to a target and "set the table" for data analysts, data scientists, programmers, developers and soon EVERYONE who touches a 1 and/or 0... and that is most of us by 2030.
Learning data engineering has been incredibly rewarding.
And humbling.
The landscape is constantly changing and capabilities are being extended that require study to implement.
We keep laying down more pipe, in better locations, using more advanced techniques.
The future requires infrastructure today.
All infrastructure is digitizing and data engineering is pivotal to our future as a species.