Data engineering interview questions
How would you monitor and reduce disk-based queries (disk spilling)?
Lakehouse vs. Warehouse
Mapper and Reducer design for solving Two-Sum
Propose a solution for monitoring and maintaining data quality across multiple regions.
What are the best practices for logging and monitoring bad data?
What are the implications of enabling schema auto-detection?
What are the limitations of Assert Transformations in complex data flows?
What would you do if a critical data pipeline failed during a holiday?
What's your approach to data versioning in a data lake?
Which metrics are critical to monitor?
Architect a solution to handle notifications for millions of users with varying preferences.
Build a banking system architecture from scratch, highlighting critical workflows, scalability, and data management strategies.
Business Role of Data Pipeline
CAP Theorem
CI/CD implementation across environments (DEV, QA, UAT, PreProd, PROD)
Can Schema Evolution lead to data inconsistencies? If so, how do you manage them?
Compare Native vs Cloud Database Systems.
Data Volume in Pipelines and Scalability Solutions
Demonstrate system design principles applied to BI solutions.
Describe a data pipeline you built and optimized.
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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.