Easy-level sql questions from real data engineering interviews.
These easy sql questions are selected from real interviews at top companies. Each question includes a detailed expert answer and pro tip to help you nail your interview. This set leans toward fundamentals — 35 easy, 0 medium, and 0 hard questions. Recurring themes are sql, bigquery, and snowflake — these patterns appear most often in real interviews and reward the deepest preparation. These questions have been reported across 33 companies including Incedo and Presidio. Average answer is around 1 minute of reading — plan roughly 1 hour to work through the full set thoughtfully.
This collection contains 35 curated questions: 35 easy. There's a strong foundation of fundamentals-focused questions — ideal for building confidence before tackling advanced topics.
The most frequently tested areas in this set are sql (8), bigquery (8), snowflake (5), spark (4), python (4), and etl (3). 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. 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 a Data Lake and a Data Warehouse.
Explain the concept of ACID properties in the context of databases.
Explain Common Table Expressions (CTEs) and their benefits.
Explain the difference between UNION and UNION ALL.
What is the difference between a clustered and non-clustered index?
What is the difference between DELETE and TRUNCATE?
What is a CTE (Common Table Expression)? What are its uses?
Aggregate surface areas and calculate cumulative surface area using the LAG function.
Are you comfortable with the variable pay structure, and what are your expectations for the base salary?
CSV Without Column Names/Schema - how to read
Can CASE statements be used in an UPDATE query?
Can you chain multiple triggers for a single pipeline?
Compare Redshift, BigQuery, and Snowflake in terms of cost, performance, and scalability.
Converting SCD0 to SCD3
Describe a cross-team data project where you had to align architectural boundaries, ownership, and SLAs. How did you handle conflicting priorities, technical debt, and the scalability of communication as the number of stakeholders grew?
Walk through a production incident where data freshness or correctness was at risk. How did you balance immediate mitigation vs. root-cause remediation? What architectural changes would prevent recurrence, and what are the cost vs. reliability trade-offs?
Given a dataset, perform transformations: Filter rows where sales > 1000, Add a new column calculating a 10% discount on sales, Group data by region and calculate total revenue.
How do you count occurrences in a column in SQL?
How do you interact with Google BigQuery using Python?
How do you keep a specific column on top in SQL?
How to view Oozie jobs?
How would you clean the data by filtering out records with null values in user_id?
How would you handle null values in a dataset, especially in a single column?
Identify consecutive numbers in a column (at least 3 consecutive).
Merge two dictionaries and remove keys with null values.
Strategies for working with busy team leads?
Tasks where the candidate faced failure and lessons learned.
Tell us about a project where you optimized an existing process or pipeline. What was the impact?
Use cases for internal staging in Snowflake?
Using Airflow to trigger and manage ETL jobs?
What are Assert Transformations, and where are they used?
What is BigQuery Cache?
Write SQL query to replace specific patterns in a string column.
Write a query to find minimum age.
Write a query to find the median salary of employees in a table.
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