**Data Types**: Structured (transactions, dimensions), semi-structured (logs, events). **Volume**: Hundreds of GB to multi-TB. **Sources**: Batch (Airflow), streaming (Kafka). **Stack**: Spark/Databricks, Snowflake/BigQuery, S3/GCS.
**Responsibilities**: ETL, schema evolution, data quality, serving for BI/ML....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like KPMG. The answer also includes follow-up discussion points that interviewers commonly explore.
Continue Reading the Full Answer
Unlock the complete expert answer with code examples, trade-offs, and pro tips - plus 1,863+ more.
Or upgrade to Platform Pro - $39
Engineers who used these answers got offers at
AmazonDatabricksSnowflakeGoogleMeta
According to DataEngPrep.tech, this is one of the most frequently asked General/Other interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.