Interview questions
Preparing for a data engineering interview at Meesho? This page contains 29 real interview questions sourced from verified Meesho interview experiences. Questions are sorted by frequency — the ones asked most often appear first.
Meesho data engineering interviews typically focus on Spark/Big Data, System Design/Architecture, and Behavioral. The interview bar skews toward harder problems (21 hard vs. 7 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.
Tell me about yourself and your experience.
Tell me about your family background
Explain the differences between Data Warehouse, Data Lake, and Delta Lake
Design a fault-tolerant Spark Streaming checkpoint strategy: what to persist, recovery semantics, and cost/scalability trade-offs with checkpoint frequency.
Given a streaming dataset from Kafka, how would you ingest the data in real-time using Spark?
Why do you think Meesho is the right fit for you?
Describe the ZS projects you worked on
Discuss the tech stacks and responsibilities at Morgan Stanley
Trapping Rain Water - calculate amount of water trapped between array elements
Design a Custom API that can query a backend server and return customer data such as the number of orders placed by a user based on their user ID
Design the data model for an ETL pipeline that ingests data from a database and loads it into Snowflake
Design an ETL pipeline using Kafka and Spark Streaming
Explain how spark.read.format("delta").load() works
Explain the architecture and role of the Hive Metastore in a data pipeline
Explain the architecture of Kafka
Explain the architecture of Spark Streaming
How do you store streaming data in Delta Lake and handle schema evolution?
How does Databricks create clusters for running Spark jobs?
How does Delta Lake store the transaction history in S3 buckets?
How would you manage the streaming data schema and handle schema evolution in Delta Lake?
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