Interview questions
Preparing for a data engineering interview at Matrix? This page contains 15 real interview questions sourced from verified Matrix interview experiences. Questions are sorted by frequency — the ones asked most often appear first.
Matrix data engineering interviews typically focus on Spark/Big Data, General/Other, and Behavioral. The interview bar skews toward harder problems (7 hard vs. 4 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.
What are traits in Scala, and how are they different from classes?
Explain the differences between Data Warehouse, Data Lake, and Delta Lake
Why should we hire you for this role?
Design an anti-skew strategy for a join on a high-cardinality key with a long-tail distribution (e.g., a few keys hold 80% of rows). Cover salting, split-skew, AQE, and cost/operational trade-offs.
Calculate a 7-day moving average of clicks for each user_id
Calculate cumulative sales for each product in each store, ordered by sale_date
Difference between var, val, and def in Scala
Monads in Scala - define with Option example
SOLID Principles in Scala - describe with examples
Differentiate SORT BY, ORDER BY, DISTRIBUTE BY, and CLUSTER BY
Memory Management in Spark - executor, storage, shuffle memory
Salting Implementation - provide example
Spark Configurations for Large-Scale Jobs
Spark Execution Flow - describe
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