Data engineering interview questions
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.
Design a pipeline capable of processing 1TB of data per day.
Design a project architecture visually and explain key components.
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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.