ETL Developer Interview Questions & Practice Simulator

Know at least one traditional ETL tool (Informatica, SSIS, Talend) and modern data engineering tools (Airflow, dbt, Spark). Cloud-native ETL services (AWS Glue, Azure Data Factory) are increasingly important. The specific tool matters less than understanding ETL patterns that apply across platforms and demonstrating adaptability to learn new tools.

Advanced SQL skills are fundamental for ETL development. Master complex joins, window functions, CTEs, and performance optimization. Understand how different databases handle SQL and optimization strategies. Most ETL work involves significant SQL, whether in transformation logic or source/target queries. Strong SQL skills are non-negotiable for ETL roles.

Data modeling understanding helps you design effective ETL processes. Know dimensional modeling concepts—facts, dimensions, slowly changing dimensions—especially for data warehouse work. Understanding source and target schemas helps you design efficient transformations. Data modeling skills distinguish ETL developers who can design solutions from those who just implement them.

Real-time and streaming ETL skills are increasingly valued. Understand the difference between batch and streaming paradigms. Know tools like Kafka, Spark Streaming, or cloud streaming services. Even if a role is batch-focused, discussing real-time considerations shows forward-thinking skills as organizations move toward more real-time data processing.