Real interview questions asked at Citi. Practice the most frequently asked questions and land your next role.
Citi data engineering interviews test your ability across multiple domains. These questions are sourced from real Citi interview experiences and sorted by frequency. Practice the ones that matter most. This set leans toward senior-level depth (14 of 39 are tagged hard). Recurring themes are partition, spark, and optimization — these patterns appear most often in real interviews and reward the deepest preparation. Many of these questions also surface at BCG and Dunnhumby, 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 39 curated questions: 14 easy, 11 medium, and 14 hard. The balanced mix of difficulties makes this set suitable for engineers at any career stage.
The most frequently tested areas in this set are partition (20), spark (17), optimization (11), python (7), join (7), and airflow (7). 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.
What is the difference between repartition and coalesce in Apache Spark?
What is the difference between SparkSession and SparkContext in Spark?
What is the difference between partitioning and bucketing in Spark, and when would you use bucketing?
What strategies can you use to handle skewed data in Spark?
What is the difference between Managed and External tables in Hive/Spark?
What is a window function? Explain with an example.
Explain the concept of checkpointing in Spark and why it is important.
Agile methodologies used?
An existing job running longer suddenly: how to analyze the issue?
How is Oozie called?
Oozie workflow files (how many used)?
Shell commands for renaming a file?
Shell: change permissions?
Shell: command to check processes running in the background?
Using shell, how to find the difference between two files?
What type of wrapper is used, or which language is used?
Amazon Deequ usage and what sort of quality checks are done using it?
Given 1TB of a file, how to check word count?
Shell: how to run jobs/scripts in the background?
How to view Oozie jobs?
Oozie join condition?
Partitioning a table with card details and transactions?
Teradata to Hadoop migration and handling data with SCD Type 2?
What is a Kafka topic, and how do you choose the number of partitions for it?
What is the role of a partition in Kafka, and how does it impact scalability?
Describe how to pass data between tasks in Airflow using XComs.
Explain the concept of RDD, DataFrame, and Dataset in PySpark.
Explain the concept of consumer groups in Kafka. How do they affect message processing?
Explain the difference between TriggerDagRunOperator and ExternalTaskSensor in Airflow.
How do you ensure data quality and consistency across different stages of a data pipeline?
How do you handle failures in Airflow tasks, and what retry strategies can you use?
How do you optimize a join operation in Spark for large datasets?
How would you design a Kafka-based pipeline for processing streaming data in real-time?
Methods to avoid duplicates in PySpark/Scala?
Usage of UDFs?
What is a DAG in Apache Airflow, and how is it used for scheduling workflows?
Describe an end-to-end data pipeline project you worked on, highlighting your role and the technologies used.
Describe how Kafka ensures data durability and fault tolerance.
Introduce your recent project, explaining its goal, architecture, tools, and technologies.
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