Deloitte Data Engineer Interview Guide

Deloitte Data Engineer interviews reported here are practical and stack-specific rather than algorithm-heavy. Candidates describe SQL and Python exercises alongside PySpark or Spark discussions: examples include second-highest-salary queries, joins, window functions, MERGE logic, and explaining how Spark jobs behave. Be ready to narrate assumptions and trade-offs, since one candidate had to formulate SQL verbally without a visible schema.

Pipeline and architecture reasoning is equally prominent. Candidates report troubleshooting a slow Spark job, joining large tables, addressing the small-file problem, describing incremental loads and dimensional modeling, and designing a multi-source pipeline into a cloud warehouse. Cloud emphasis varied across accounts—GCP, Azure, AWS, Databricks, Airflow, and Snowflake all appeared—so prepare the platforms and project decisions most relevant to your background instead of assuming one standard stack.

Several accounts also include a managerial, HR, behavioral, or presentation conversation. Expect questions about your projects, prior experience, motivation, teamwork, ambiguous requirements, and how you communicate a recommendation. One candidate reported a presentation exercise based on a sparse brief. The exact sequence varies, but concise project stories that connect design choices to reliability, monitoring, cost, and data quality will help across the reported formats.

Timing evidence is limited. One candidate described a process lasting more than four months, while other accounts did not provide a complete timeline.