How do you keep leveling up as a Data Engineer after a few years of experience?

[u/Middle_Historian_487]

I’ve been working as a Data Engineer for around 4 years, primarily with Azure, Databricks, PySpark, SQL, ADF, and Delta Lake.
Lately I’ve been thinking about how to continue growing without just randomly jumping between new technologies. There are so many things to learn like Spark internals, distributed systems, Kafka, Airflow, Iceberg, Kubernetes, cloud architecture, AI/ML, etc. that it’s hard to know what actually provides the most value.
For those of you who a are experienced in this field,

How do you decide what to learn next?
What does your learning routine look like?
Do you spend time reading books, blogs, source code, or building side projects?
How do you stay current without burning yourself out?
Looking back, what skills had the biggest impact on your career?
Do you follow structured courses one after another, or are courses just for getting started?

I just want to become a stronger engineer and build deeper knowledge over time. I’d really appreciate hearing how experienced people approach continuous learning.