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Approach for maintaining data quality and integrity across ETL pipelines.
Tests leadership through execution: ownership, prioritization, and stakeholder alignment on a meaningful project with measurable outcomes.
Tests how you mentor junior teammates through structured feedback, communication, and ownership for both growth and team outcomes.
Tests ownership and prioritization in managing code quality and technical debt without sacrificing delivery.
Explain how you use IaC to provision and manage pipeline infrastructure consistently across environments.
Key security considerations for a cloud data pipeline, from ingestion through storage, orchestration, and monitoring.
Compare star and snowflake schemas for warehouse design, including trade-offs in normalization, query simplicity, and analytics performance.
Tests ownership in diagnosing and fixing a slow data pipeline, with emphasis on root-cause analysis, communication, and measurable impact.
Set up CI CD and automated testing for data pipelines so changes ship faster with fewer production issues.
Tests your ability to choose the right architecture patterns for streaming and batch workloads.
Tests your ability to build reliable, scalable data pipelines and manage them in production.
Tests your understanding of modern lakehouse capabilities and when to choose Databricks for ITJ data platforms.
