Real interview questions asked at Carelon. Practice the most frequently asked questions and land your next role.
Carelon data engineering interviews test your ability across multiple domains. These questions are sourced from real Carelon interview experiences and sorted by frequency. Practice the ones that matter most. This set leans toward senior-level depth (5 of 12 are tagged hard). Recurring themes are partition, spark, and join — these patterns appear most often in real interviews and reward the deepest preparation. Average answer is around 1 minute of reading — plan roughly 1 hour to work through the full set thoughtfully.
This collection contains 12 curated questions: 3 easy, 4 medium, and 5 hard. The distribution skews toward harder problems, reflecting the depth expected in senior-level interviews.
The most frequently tested areas in this set are partition (8), spark (7), join (4), optimization (4), sql (4), and python (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.
Design an end-to-end data pipeline using Glue, Lambda, EC2, S3, Redshift, and Athena.
Discuss how versioning works in S3 and its use cases, such as data recovery and auditing.
What are the methods to copy files to S3 without using the bucket upload feature?
Test SQL skills using advanced window functions such as LAG, LEAD, and DENSE_RANK.
Time and cost comparisons for executing the same query in Snowflake and Spark.
Write a query to generate the specified output using advanced SQL skills with joins, aggregations, and window functions.
Discuss techniques such as partitioning, broadcast joins, and caching to enhance Spark job performance.
Explain how Spark processes a 500GB file, covering memory allocation, shuffles, and spillovers to disk.
Explain how to overwrite a file stored in S3 using PySpark.
What are the steps to execute a Python file with PySpark code on an EC2 environment?
Write PySpark code to save a DataFrame in Parquet format to an S3 bucket.
Write a complete PySpark program from import statements to the stop statement, covering transformations and actions.
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