Data Engineering Interview Guide
Data engineering interviews assess the ability to design, build, and maintain systems that move and transform data reliably at scale.
Interview Topics
Data engineering interviews typically cover the following areas:
| Topic | Description |
|---|---|
| System Design | Designing pipelines for high-volume data ingestion (e.g., 50 TB/day from multiple sources) |
| SQL | Complex queries, window functions, query optimization |
| Coding | Data processing implementations in Python or Spark |
| Architecture | Schema design, data modeling, storage trade-offs |
Guide Contents
Data Pipelines
ETL/ELT pipeline design patterns, orchestration, and reliability.
Batch vs Streaming
Processing paradigm selection criteria and architectural patterns.
Data Warehousing
Star schemas, slowly changing dimensions, and analytical storage design.
Required Skills
| Skill Area | Technologies |
|---|---|
| Orchestration | Airflow, Dagster, Prefect |
| Distributed Processing | Apache Spark, Apache Flink |
| Data Modeling | Normalization, denormalization, dimensional modeling |
| Cloud Platforms | AWS, GCP, or Azure data services |
| SQL | Window functions, CTEs, query optimization |