The honest answer is that data engineering is not really a single-language field. SQL and Python are both essential, and they serve different purposes — which is why most working data engineers are fluent in both.
Why SQL comes first
SQL is the language of data. It is used to query databases, write transformation logic inside warehouses, validate that pipelines produced correct output, and design the data models that analysts and AI systems depend on. Data engineers who cannot write strong SQL create bottlenecks — everything they touch that involves data manipulation is slower and more fragile.
The SQL skills that matter for data engineering go deeper than SELECT and WHERE. Window functions, CTEs, aggregation patterns, and query optimisation are the areas that separate engineers who can build reliable systems from those who cannot. These take a few weeks of focused practice to develop properly.
Why Python is equally critical
Python handles what SQL cannot. It connects to external APIs, reads files from cloud storage, orchestrates complex workflows, integrates with messaging systems like Kafka, and enables the kind of custom processing logic that SQL expressions cannot express. Almost every data pipeline you build will have both SQL and Python in it.
The Python required for data engineering is not advanced. Variables, functions, error handling, Pandas for data manipulation, and database connectivity libraries cover the majority of what you will use. The learning curve is lower than most people expect, particularly if you already understand SQL — the concepts around data shape and transformation carry over.
What about Scala and Java?
Scala is used in some Spark environments — particularly older ones or those at companies with a Java background. Java appears occasionally in enterprise data engineering contexts. Neither is necessary to get started. Most modern data engineering is done in Python and SQL, and PySpark (Python API for Spark) has made Scala largely optional for Spark work. Learn SQL and Python first, thoroughly, and add Scala later if a specific role requires it.