Hard-level system design questions from real data engineering interviews.
These hard system design questions are selected from real interviews at top companies. Each question includes a detailed expert answer and pro tip to help you nail your interview.
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
Demonstrate system design principles applied to BI solutions.
Describe a data pipeline you built and optimized.
Describe a fault-tolerant distributed data processing system.
Describe a strategy for implementing a real-time content delivery monitoring system.
Describe a system design to handle product launches with massive traffic spikes.
Describe an end-to-end data pipeline project you worked on, highlighting your role and the technologies used.
Describe handling schema evolution in AWS Redshift without downtime.
Describe how Kafka ensures data durability and fault tolerance.
Describe how data is ingested, transformed, and served in a data pipeline.
Describe how to monitor and log errors effectively in a real-time data pipeline.
Describe how you would architect a pipeline to process real-time logs with schema evolution
Describe how you would debug a failing ETL pipeline in production.
Describe how you would design a data catalog for managing metadata
Describe how you'd design a system to track inventory and sales in real-time.
Describe strategies for monitoring, retries, idempotency, and validation in data pipelines.
Describe the architecture of an ETL pipeline you built in your previous project.
Describe the steps involved in optimizing an existing data transformation pipeline.
Describe your current project, including technologies, architecture, and responsibilities.
Describe your experience with large-scale data systems
Describe your monitoring strategy for this pipeline.
Describe your work with microservices.
Design a Data Warehouse for an e-commerce platform.
Design a data model for a ride-hailing app.
Design a data model for a ridesharing app
Design a data model for an e-commerce system tracking orders, shipments, and payments.
Design a data model for capturing watch sessions across multiple devices
Design a data model to track orders, payments, and shipping — handle changes in customer address
Design a data pipeline for real-time analytics of e-commerce transactions. Ensure to include data ingestion, processing, storage, and visualization components.
Design a data pipeline for streaming analytics.
Design a data pipeline from end to end - describe how data would be ingested, processed, stored, and queried.
Design a data pipeline to collect, process, and visualize customer feedback from Adidas stores worldwide.
Design a data pipeline to ingest and process clickstream data in near real-time
Design a data pipeline to ingest and process data from multiple sources (e.g., S3, Kinesis) to Redshift using Spark.
Design a data warehouse for 7-11 or 24x7 stores
Design a data warehouse for a grocery store.
Design a data warehouse schema to track orders, customers, delivery partners, and payments.
Design a database schema for tracking stock trades in real-time.
Design a database schema to store customer transactions, including attributes like region, product category, and timestamp.
Design a high-level system for a Netflix-like app.
Design a logging and monitoring solution for a mission-critical data pipeline.
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