Interview questions
Preparing for a data engineering interview at BCG? This page contains 21 real interview questions sourced from verified BCG interview experiences. Questions are sorted by frequency — the ones asked most often appear first.
BCG data engineering interviews typically focus on SQL, System Design/Architecture, and Spark/Big Data. The interview bar skews toward harder problems (13 hard vs. 3 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.
What is the difference between repartition and coalesce in Apache Spark?
Write an SQL query to find the second-highest salary from an employee table.
What strategies can you use to handle skewed data in Spark?
Design a Delta table layout for mixed workload: point lookups by user_id, range scans by date, and full partition scans. Compare partitioning vs. Z-ordering—when to use each, and the rewrite cost trade-off.
How would you model customer transaction data for both analytical and operational use cases?
Create a script to parse and transform a JSON file into a structured CSV.
Compare Redshift, BigQuery, and Snowflake in terms of cost, performance, and scalability.
Explain the difference between Star and Snowflake schemas. When would you choose one over the other?
Kafka Partitioning: How would you ensure even load distribution across Kafka partitions in a high-volume system?
Merge two dictionaries and remove keys with null values.
What are the key design principles for a cloud-based data warehouse?
What considerations are important when designing a dimensional model for a ridesharing app?
Explain how HDFS (Hadoop Distributed File System) stores data across nodes.
Explain how to schedule an automated task using Apache Airflow.
Describe how to monitor and log errors effectively in a real-time data pipeline.
Design a pipeline capable of processing 1TB of data per day.
Discuss trade-offs when designing a batch vs. real-time processing system.
Explain how serverless computing impacts modern data architecture.
How would you automate a data pipeline deployment using GitHub Actions or another CI/CD tool?
How would you design a real-time pipeline for generating daily retail sales reports?
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