Data engineering interview questions
How would you design the architecture to handle high availability and scalability?
How would you ensure data quality and integrity in a data pipeline? Discuss the steps you would take to validate and cleanse data.
How would you ensure the system can handle millions of concurrent users?
How would you fetch data from an external API, and what AWS services would you use to build a scalable data pipeline?
How would you fix a client's failing reporting pipeline suffering from performance bottlenecks?
How would you handle late-arriving data in a real-time stream processing pipeline?
How would you handle schema changes in a production ETL pipeline?
How would you handle schema evolution in a real-time data system?
How would you implement a near real-time data pipeline for analyzing user behavior on the Adidas mobile app?
How would you implement data governance and security in your design?
How would you manage a disagreement within your team about an ETL pipeline design?
How would you manage schema evolution in your data lake?
How would you process Excel files with multiple sheets? Design the data pipeline.
How would you schedule a recurring pipeline in Data Fusion?
How would you set up an alert system to monitor your ETL pipeline for failures or performance issues?
How would you set up end-to-end tracing for a complex pipeline?
How would you use monitoring tools to detect and resolve pipeline failures proactively?
Identify potential bottlenecks in your pipeline design and propose solutions to mitigate them.
Introduce your recent project, explaining its goal, architecture, tools, and technologies.
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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.