Data engineering interview questions · hard
What architecture are you following in your current project, and why?
Briefly explain the architecture of Kafka.
Describe the data pipeline architecture you've worked with.
Explain the trade-offs between batch and real-time data processing. Provide examples of when each is appropriate.
Can you explain the trade-offs you made during the design process?
Designing Mixpanel - event-driven analytics platform
How did you ensure scalability and reliability in your design?
How would you design the schema for transactional data storage?
How would you handle massive data ingestion in a cloud environment?
Lakehouse vs. Warehouse
Mapper and Reducer design for solving Two-Sum
What's your approach to data versioning in a data lake?
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
Type or paste your answer to any of these questions and our AI Coach scores it, highlights gaps, and rewrites it at FAANG quality. Free to try.
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.