Real interview questions asked at Globant. Practice the most frequently asked questions and land your next role.
Globant data engineering interviews test your ability across multiple domains. These questions are sourced from real Globant interview experiences and sorted by frequency. Practice the ones that matter most. This set leans toward senior-level depth (7 of 13 are tagged hard). Recurring themes are spark, partition, and sql — these patterns appear most often in real interviews and reward the deepest preparation. Many of these questions also surface at Altimetrik and Chryselys, 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 13 curated questions: 4 easy, 2 medium, and 7 hard. The distribution skews toward harder problems, reflecting the depth expected in senior-level interviews.
The most frequently tested areas in this set are spark (9), partition (6), sql (6), optimization (4), and join (3). 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.
Tell me about yourself and your experience.
Explain the concept of checkpointing in Spark and why it is important.
How do you drop columns with null values in PySpark?
Explain Dynamic Partition Pruning error and how to fix it.
How do you convert 3 rows into one column in SQL?
How do you count occurrences in a column in SQL?
How do you keep a specific column on top in SQL?
Explain read and write modes in Spark.
How do you convert an array column to multiple columns in PySpark?
How does Adaptive Query Execution (AQE) work?
What is a serializer in Spark?
What is the difference between MapReduce and Spark?
What is the difference between head() and take() in PySpark?
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