Real interview questions asked at S&P Global. Practice the most frequently asked questions and land your next role.
S&P Global data engineering interviews test your ability across multiple domains. These questions are sourced from real S&P Global interview experiences and sorted by frequency. Practice the ones that matter most. This set leans toward fundamentals — 5 easy, 3 medium, and 2 hard questions. Recurring themes are partition, spark, and join — these patterns appear most often in real interviews and reward the deepest preparation. Many of these questions also surface at Datametica, so the preparation transfers across companies. Average answer is around 1 minute of reading — plan roughly 1 hour to work through the full set thoughtfully.
This collection contains 10 curated questions: 5 easy, 3 medium, and 2 hard. There's a strong foundation of fundamentals-focused questions — ideal for building confidence before tackling advanced topics.
The most frequently tested areas in this set are partition (3), spark (3), join (3), sql (2), window (2), and etl (2). Focusing on these topics will give you the highest return on your preparation time.
Start with the easy questions to warm up and solidify fundamentals. Medium-difficulty questions form the bulk of real interviews — spend the most time here and practice explaining your reasoning out loud. Hard questions often appear in senior and staff-level rounds; attempt them after you're comfortable with the basics. For each question, try answering before revealing the solution. Use our AI Mock Interview to simulate real interview conditions and get instant feedback on your responses.
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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