Interview questions
Preparing for a data engineering interview at S&P Global? This page contains 10 real interview questions sourced from verified S&P Global interview experiences. Questions are sorted by frequency — the ones asked most often appear first.
S&P Global data engineering interviews typically focus on Spark/Big Data, SQL, and Behavioral. There's a solid mix of fundamental and advanced questions, making it accessible for candidates at multiple experience levels.
Use the difficulty filters above to focus your preparation. For each question, attempt your own answer first, then compare with our expert solution. You can also practice these questions in our AI Mock Interview Coach for real-time feedback.
Convert complex SQL (CTEs, window functions, subqueries) to production-grade PySpark. Discuss when to use spark.sql() vs. DataFrame API, and the implications for testability, partitioning, and execution predictability.
What is the size of the teams I've worked with and how we handled sprints during the project?
Why are you considering leaving your current company?
Given the input string "AAABBBCCCDDDAAA," compress it to output "A3B3C3D3A3."
Explain the differences between Redshift and Snowflake, and how I've used them in previous projects.
Explain the scalability, performance, and cost-efficiency of both Redshift and Snowflake in different use cases.
Write a query to find the 5th highest salary in an employee table and calculate the number of employees whose salary is greater than that of their manager.
Explain how I handle performance optimizations, scheduling tasks, and monitoring DAGs in Airflow.
Provide specific examples of challenges faced with PySpark and SQL and solutions implemented.
How does data flow through the system? From ingestion to processing and storage?
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