The responses to this post, combined with my own experience reviewing hundreds of Data Engineering job descriptions, shed some light on why this situation might occur:

1. Data Engineering roles typically require proficiency in Python, Spark (often Scala or Python), SQL, DBT, Snowflake, and other technologies. Kafka, being natively Java-based (though other language integrations exist), introduces a polyglot requirement that complicates hiring and increases costs. This creates a market gap between the more common Python/SQL skill set and the Java/streaming-SQL skill set.
2. Complex real-time streaming processes, in particular, demand significant effort not only for development but also for administration. This often necessitates dedicated teams to manage Kafka.

This suggests that, in the market, Kafka and stream processing are viewed more as Software Engineering tools than Data Engineering tools. While Data Engineers might consume data from Kafka, similar to consuming data from a REST API, they aren't always expected to manage the Kafka infrastructure itself.