Data engineering interview questions · hard
How we manage dependencies and retries in data pipelines
How would you architect a recommendation system for Adidas's e-commerce platform?
How would you automate a data pipeline deployment using GitHub Actions or another CI/CD tool?
How would you build a monitoring dashboard for ETL job failures?
How would you build a pipeline that transforms semi-structured logs into a structured analytics layer?
How would you build a reusable ETL framework using Airflow?
How would you design a cost-effective data lake architecture on AWS or Azure?
How would you design a cost-effective, scalable, and efficient data pipeline for an e-commerce website?
How would you design a data archiving strategy in S3 using lifecycle policies?
How would you design a data ingestion framework for heterogeneous data sources?
How would you design a data pipeline to handle late-arriving data?
How would you design a data platform to handle real-time transaction data for a retail business?
How would you design a database to handle historical data storage for compliance purposes?
How would you design a logging framework to track errors across multiple services?
How would you design a real-time pipeline for generating daily retail sales reports?
How would you design a scalable data ingestion pipeline?
How would you design a scalable data lake for Adidas's global e-commerce operations?
How would you design a system to support personalized recommendations at scale?
How would you design an architecture that supports both batch and real-time analytics for sales data?
How would you design the architecture to handle high availability and scalability?
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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.