Data engineering interview questions · easy
CDC During Migration - explain approaches for real-time Change Data Capture
Describe a project you worked on, focusing on the data pipeline and your role.
Explain clustering with a real-time example.
Explain how to implement schema validation for incoming data streams.
Explain how you gather and define requirements for a complex data platform project.
Handle midstream schema changes gracefully.
How do you deploy from a development environment to QA and production?
How do you handle schema mismatches during merging?
How would you handle a schema change when new files arrive?
How would you handle data quality issues in a real-time ingestion pipeline?
Propose a solution for monitoring and maintaining data quality across multiple regions.
What are the implications of enabling schema auto-detection?
What would you do if a critical data pipeline failed during a holiday?
Which metrics are critical to monitor?
How do you handle exceptions in data ingestion?
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Data engineering system design focuses on: designing ETL/ELT pipelines, batch vs real-time processing trade-offs, data warehouse architecture (medallion/lakehouse), fault tolerance and exactly-once processing, schema evolution, and cost optimization at scale.
Data engineering system design focuses on data flow, storage formats, processing guarantees, and analytical query patterns. Software engineering system design focuses on request/response patterns, caching, load balancing, and microservices. Data engineers design for throughput and correctness; software engineers design for latency and availability.
Practice designing end-to-end pipelines: data ingestion, transformation, storage, and serving. For each design, discuss trade-offs around batch vs streaming, exactly-once vs at-least-once, cost vs performance, and schema evolution. Use real scenarios like 'Design Uber's surge pricing pipeline.'
The medallion (bronze/silver/gold) architecture organizes a data lakehouse into three layers: raw data landing (bronze), cleaned and validated data (silver), and business-ready aggregated data (gold). Interviewers ask about it because it's the dominant pattern at companies using Databricks, Delta Lake, or similar lakehouse platforms.