314,552 interview questions from 6,000+ companies.
Tests prioritization under pressure across multiple projects, including trade-off judgment, stakeholder communication, and ownership of outcomes.
Approach for maintaining data quality and integrity across ETL pipelines.
Tests ownership under ambiguity: how you prioritize, align stakeholders, and recover a project when the path forward is unclear.
Tests conflict resolution in a delivery context, including communication, influence without authority, and ability to preserve team trust while reaching a decision.
Approach for handling schema changes and data quality checks in a high-volume data lake pipeline.
Tests ownership and communication while debugging a complex software issue under ambiguity and stakeholder pressure.
Compare common sorting algorithms by best, average, and worst-case time complexity and explain when each is appropriate.
Compare ETL and ELT, and explain when ELT is the better pipeline pattern.
Explain how to profile, clean, and standardize missing or dirty data before analysis.
Approach for building near-real-time dashboard pipelines with streaming, orchestration, and data quality controls.
A structured approach to debugging production data pipelines, with focus on orchestration, data quality, idempotency, and safe backfills.
Explain INNER, LEFT, RIGHT, FULL OUTER, CROSS, and SELF JOINs with examples and when to use each.
Explain your preferred extraction and transformation stack, and the reasoning behind those tool choices.
Discuss practical experience using a data warehouse for analytics, including loading, transformation, orchestration, and data quality.
Redesign a slow Databricks Spark ETL pipeline to cut runtime from 3 hours to under 60 minutes without breaking data quality or SLAs.
Find the top 10 products by total sales revenue using joins, aggregation, and a CTE.
Use joins, a CTE, and aggregation to rank the top 5 products by non-returned revenue in the last 30 days.
Aggregate monthly sales totals by product category using JOINs, GROUP BY, and date formatting.
Use a CTE and ROW_NUMBER to return the top 2 products by revenue within each category from completed orders.
