The most frequently asked bigquery questions in data engineering interviews.
Master bigquery for your next data engineering interview. These questions cover core concepts, advanced patterns, and real-world scenarios that interviewers test. This set leans toward senior-level depth (22 of 42 are tagged hard). Recurring themes are bigquery, partition, and snowflake — these patterns appear most often in real interviews and reward the deepest preparation. These questions have been reported across 32 companies including EY and Aarete. Average answer is around 2 minutes of reading — plan roughly 1 hour to work through the full set thoughtfully.
This collection contains 42 curated questions: 10 easy, 10 medium, and 22 hard. The distribution skews toward harder problems, reflecting the depth expected in senior-level interviews.
The most frequently tested areas in this set are bigquery (42), partition (24), snowflake (16), sql (15), optimization (11), and spark (10). 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.
Explain the differences between Data Warehouse, Data Lake, and Delta Lake
Can you explain the difference between OLTP and OLAP?
What is a Common Table Expression (CTE), and when would you use it?
How do you remove duplicate rows in BigQuery?
What is Snowflake's architecture, and why is it unique?
Have you worked on Data Warehousing projects?
Retrieve the most recent sale_timestamp for each product (Latest Transaction).
What is the difference between OLTP and OLAP?
Difference Between Internal and External Tables in BigQuery
Explain Common Table Expressions (CTEs) and their benefits.
Explain the use of the MERGE statement in SQL.
What is the difference between a clustered and non-clustered index?
What is a CTE (Common Table Expression)? What are its uses?
What is the difference between OLTP and OLAP?
Explain the difference between batch and streaming data processing in Data Fusion.
Describe a time you had to make a difficult decision with limited information.
Could you describe a specific cost optimization strategy you implemented in the cloud and its results?
Provide Data Pipeline for GCP Data Engineering
Explain your project and the technologies used so far.
What excites you about working at Google?
What is a Foreign Key?
Compare OLTP and OLAP systems in the context of financial transactions.
Compare Redshift, BigQuery, and Snowflake in terms of cost, performance, and scalability.
Connecting BigQuery with Linux
Design a daily ETL pipeline to ingest API data into BigQuery.
Explain BigQuery Architecture.
How can you automate data insertion into BigQuery using Python?
How do you interact with Google BigQuery using Python?
How to Use Dataflow with BigQuery
How would you optimize a query fetching sales data across multiple countries with billions of rows?
What are BigQuery Slots?
What are the benefits of BigQuery Warehouse?
What is BigQuery Cache?
What is UNNEST and provide a query example?
Write SQL query to replace specific patterns in a string column.
Write a query to find the median salary of employees in a table.
Business Role of Data Pipeline
Compare Native vs Cloud Database Systems.
Describe the architecture of an ETL pipeline you built in your previous project.
Design a data model for an e-commerce system tracking orders, shipments, and payments.
How to adapt the same pipeline to a cloud environment?
How would you design a real-time pipeline for generating daily retail sales reports?
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