SQL questions from Adidas data engineering interviews.
These sql questions are sourced from Adidas data engineering interviews. Each includes an expert-level answer. This set leans toward the medium-difficulty band most real interviews actually live in (5 of 12). Recurring themes are join, partition, and snowflake — these patterns appear most often in real interviews and reward the deepest preparation. Average answer is around 1 minute of reading — plan roughly 1 hour to work through the full set thoughtfully.
This collection contains 12 curated questions: 3 easy, 5 medium, and 4 hard. The distribution skews toward harder problems, reflecting the depth expected in senior-level interviews.
The most frequently tested areas in this set are join (4), partition (4), snowflake (4), sql (3), optimization (2), and spark (2). 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.
Discuss a project where you balanced business goals with technical constraints.
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?
Design a star schema for retail analytics (e.g., Adidas). Explain the dimensional modeling choices, SCD strategy, and how you would scale this schema for global multi-currency, multi-region deployments. What are the refresh and storage cost implications?
Explain how partitioning and bucketing in Hive/Spark optimize queries. What are the trade-offs in bucket count, partition cardinality, and small-file problem? When does over-partitioning or over-bucketing become counterproductive?
Explain the differences between OLTP and OLAP databases and their relevance in Adidas's operations.
How would you create a materialized view for frequently accessed aggregated sales data?
How would you handle duplicate or corrupted data in a batch ETL job?
How would you optimize a query fetching sales data across multiple countries with billions of rows?
Tell us about a project where you optimized an existing process or pipeline. What was the impact?
What are the benefits of using a cloud data warehouse (e.g., Redshift, Snowflake) for analytics?
Write a query to calculate the total revenue generated by each product category.
Write a query to find the top 5 most-sold Adidas products in the last month.
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