Data engineering interview questions · hard
Design a pipeline capable of processing 1TB of data per day.
Design a project architecture visually and explain key components.
Design a real-time data pipeline for clickstream events. How to ensure fault tolerance? Where to implement deduplication logic? How to efficiently store 1 billion+ rows?
Design a real-time message stream processing system
Design a scalable system for processing real-time sales data from multiple stores, storing it for analytics, and generating reports.
Design a schema for a retail store's sales data, explaining your choice of dimensions and facts
Design a system to handle 1M daily transactions with real-time analytics for Swiggy.
Design a working data pipeline to efficiently store, process, and report data.
Design an ETL pipeline to process real-time stock market data.
Design an End-to-End ETL Pipeline.
Design an e-commerce platform like Flipkart
Design the architecture for an Online Ticket Booking System
Designing a pipeline for real-time content engagement tracking
Develop a generic user profile system for Hotstar that accepts inputs from various teams, consolidates into a unified profile, and supports daily updates with aggregation methods.
Differentiate between Schema Enforcement and Schema Evolution.
Differentiating between pipeline parameters and global parameters
Discuss approaches for fault-tolerant data ingestion in real-time systems.
Discuss data replication strategies in Kafka for fault tolerance.
Discuss designing a data pipeline for a specific use case
Discuss the deployment process for real-time applications using CI/CD pipelines.
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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.