20 Data Engineering Interview Questions You Should Know for Databricks & PySpark Roles

· Source: Data Engineering on Medium · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning · Depth: Advanced, quick

Summary

This article presents 20 essential data engineering interview questions specifically designed for Databricks, Spark, PySpark, and Azure Data Engineer roles. It highlights the evolving nature of these interviews, where a deep understanding of "under the hood" mechanics and the ability to troubleshoot real production problems are now critical, surpassing the sufficiency of basic SQL and PySpark transformations. The content immediately addresses the first question, "Explain the Architecture of Apache Spark," detailing its fundamental components: the Driver, Cluster Manager, Executors, and Tasks. It also illustrates their high-level flow within a typical Spark application, providing a foundational understanding for candidates.

Key takeaway

For Data Engineers interviewing for Databricks, Spark, or PySpark roles, you must move beyond basic SQL and transformation knowledge. Your preparation should prioritize understanding Apache Spark's architecture, internal mechanisms, and practical production troubleshooting scenarios. Focus on the "under the hood" details to demonstrate the depth required for modern roles, ensuring you can articulate how components like the Driver and Executors interact.

Key insights

Modern Data Engineering interviews demand deep "under the hood" knowledge and production troubleshooting skills.

In practice

Topics

Best for: Data Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Engineering on Medium.