SQL vs Python for Data Engineers: What Interviewers Actually Ask

Every data engineer needs both SQL and Python. The real question interviewers are testing is: do you know when to use which tool?

SQL is the language of data warehouses, analytics, and declarative transformations. Python is the language of orchestration, custom logic, APIs, and machine learning pipelines.

In interviews, you'll typically face separate SQL and Python rounds — but the best candidates show fluency in both and can articulate trade-offs.

SQL interview rounds typically involve:

• Writing queries live — window functions, CTEs, self-joins, correlated subqueries
• Optimization — 'This query takes 45 minutes on 500M rows. How would you fix it?'
• Schema design — star schema vs snowflake, normalization trade-offs
• Platform-specific — BigQuery-specific features, Redshift sort/dist keys, Snowflake clustering

Companies like Amazon, Google, and Goldman Sachs have dedicated SQL rounds. These tend to be the most objective and highest-signal parts of the interview.

Python rounds for data engineers differ from software engineering Python rounds:

• PySpark — DataFrame operations, UDFs, broadcast variables, shuffle optimization
• Data manipulation — pandas operations, JSON parsing, file I/O
• Algorithms — Not LeetCode-hard, but basic data structures, string manipulation, and algorithmic thinking
• Pipeline code — Writing Airflow DAGs, API integrations, error handling patterns

FAANG companies increasingly test PySpark specifically, not just Python fundamentals.

Use this framework in interviews:

Use SQL when:

• Declarative transformations (aggregations, joins, filtering)
• Data warehouse operations
• Ad-hoc analysis and exploration
• dbt-style modular transformations

Use Python when:

• Complex business logic that's hard to express in SQL
• API integrations and external data sources
• ML feature engineering pipelines
• Custom data quality checks
• Orchestration and workflow management

The senior answer: 'I default to SQL for transformations because it's declarative, optimizable, and readable. I reach for Python when I need control flow, external integrations, or logic that would require ugly SQL hacks.'