314,552 interview questions from 6,000+ companies.
Approach for maintaining data quality and integrity across ETL pipelines.
Discuss the data integration tools you have used and how they fit into ETL, orchestration, and data quality workflows.
Preferred tools and patterns for data modeling and pipeline architecture in a modern data platform.
Use GROUP BY and SUM to rank the top 10 customers by total revenue from a single sales table.
Design a pipeline for a real-time operational dashboard, covering streaming ingestion, modeling, data quality, and dashboard serving.
Common pipeline issues when combining multiple data sources, including schema mismatch, data quality, orchestration, and duplicate handling.
Tests algorithmic thinking and practical handling of duplicates for reliable datasets.
Tests query optimization skills, including indexing, execution plans, and performance tuning.
Tests foundational understanding of storage architectures and their intended use cases.
Tests schema design tradeoffs including normalization, performance, and maintainability.
Tests data quality judgment and appropriate imputation or exclusion strategies for analytics.
Tests advanced database scaling design and implementation details for large datasets.
Tests systematic diagnosis of database performance issues using metrics, plans, and configuration checks.
Tests practical coding ability for data cleaning, validation, and transformation logic.
Tests ability to model data for query patterns, scalability, and maintainability.
