System Design questions from Adidas data engineering interviews.
These system design questions are sourced from Adidas data engineering interviews. Each includes an expert-level answer. This set leans toward senior-level depth (10 of 12 are tagged hard). Recurring themes are spark, partition, and join — these patterns appear most often in real interviews and reward the deepest preparation. Average answer is around 3 minutes of reading — plan roughly 1 hour to work through the full set thoughtfully.
This collection contains 12 curated questions: 2 easy, and 10 hard. The distribution skews toward harder problems, reflecting the depth expected in senior-level interviews.
The most frequently tested areas in this set are spark (11), partition (10), join (7), optimization (5), window (5), and airflow (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. 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 how to implement schema validation for incoming data streams.
Propose a solution for monitoring and maintaining data quality across multiple regions.
Describe a system design to handle product launches with massive traffic spikes.
Describe how you would debug a failing ETL pipeline in production.
Describe how you'd design a system to track inventory and sales in real-time.
Design a data pipeline to collect, process, and visualize customer feedback from Adidas stores worldwide.
Design a database schema to store customer transactions, including attributes like region, product category, and timestamp.
How would you architect a recommendation system for Adidas's e-commerce platform?
How would you build a reusable ETL framework using Airflow?
How would you design a scalable data lake for Adidas's global e-commerce operations?
How would you design an architecture that supports both batch and real-time analytics for sales data?
How would you implement a near real-time data pipeline for analyzing user behavior on the Adidas mobile app?
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