Ernst & Young U.S. LLP Data Engineer Interview Questions
The questions to prepare for a Ernst & Young U.S. LLP Data Engineer interview. Questions from real interview reports rank first. Updated daily.
Find missing employee numbers in a fixed sequence using PostgreSQL generate_series and a left join.
Explain normalization, why it improves data integrity, and how to model many-to-one relationships in operational databases.
Explain how RANK() and DENSE_RANK() handle ties differently in ordered SQL results such as leaderboards.
Find the second-highest distinct non-NULL salary using PostgreSQL window-function logic.
Use SQL aggregation and Python validation to explain how a workplace data problem was solved and measured.
LalamoveCompare Python lists and tuples, including mutability, performance, and when each should be used.
Tests understanding of Python list operations and their impact on data structures.
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A structured approach to debugging production data pipelines, with focus on orchestration, data quality, idempotency, and safe backfills.
Design the core pipeline infrastructure for a new project, with attention to orchestration, data quality, idempotency, and future scale.
Approach for maintaining data quality and integrity across ETL pipelines.