Interview questions
Preparing for a data engineering interview at Carelon? This page contains 6 real interview questions sourced from verified Carelon interview experiences. Questions are sorted by frequency — the ones asked most often appear first.
Carelon data engineering interviews typically focus on Spark/Big Data, SQL, and Cloud/Tools. The interview bar skews toward harder problems (3 hard vs. 0 easy), suggesting emphasis on depth and system-level thinking.
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.
Design an end-to-end data pipeline using Glue, Lambda, EC2, S3, Redshift, and Athena.
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.
Explain how Spark processes a 500GB file, covering memory allocation, shuffles, and spillovers to disk.
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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