314,552 interview questions from 6,000+ companies.
Approach for maintaining data quality and integrity across ETL pipelines.
Design the core pipeline infrastructure for a new project, with attention to orchestration, data quality, idempotency, and future scale.
Evaluate when a pipeline should use stream processing versus scheduled batch based on latency, cost, complexity, and data quality needs.
Practical approach for maintaining data quality across ML ETL pipelines, orchestration, and repeatable data processing.
Approach for designing an end-to-end data pipeline from ingestion through transformation, storage, and downstream consumption.
Explain SQL vs NoSQL trade-offs, including schema design, consistency, scaling, and query flexibility.
Design a streaming pipeline that can absorb late-arriving events while keeping aggregates correct and downstream tables stable.
Develop an ETL pipeline to process 10TB of daily sales data with strict data quality validations and orchestration requirements.
Tests schema design for slowly changing dimensions and time-based change tracking.
Tests dimensional modeling judgment and performance trade-off reasoning.
Tests performance troubleshooting and mitigation strategies for distributed query execution.
Tests SQL transformation skills for analytics-ready datasets.
Tests CDC understanding and ability to support incremental updates and synchronization.
Tests streaming architecture design, aggregation, and low-latency analytics considerations.
Tests query optimization techniques for large-scale analytical workloads.
Tests understanding of query planning, operators, and performance implications.
Tests data lake design choices for mixed data types and downstream usability.
Tests performance tuning and scalability knowledge for distributed processing.
Tests orchestration design and dependency management for production pipelines.
Tests end-to-end pipeline design for serving multiple business teams with reliable data.
27 total questions
