Databricks Data Engineer Interview Questions & Practice

Databricks interviews go deep on Apache Spark internals — DataFrame transformations, shuffles, broadcast joins, Delta Lake and lakehouse patterns. Practice Spark-heavy challenges below in a real in-browser Spark session, then see live Databricks openings.

Practice challenges

• Optimize Small DataFrame Join with Broadcast (pyspark, easy, +50 XP)
• Customers With at Least One Order (sql, easy, +50 XP)
• Count Orders Per Customer Including Zero (sql, easy, +50 XP)
• Total Amount Spent Per Customer (With Zero) (sql, easy, +50 XP)
• Advanced Joins: Customers With No Orders (sql, easy, +50 XP)
• ROW_NUMBER: Assign Unique Rank to Each Employee (sql, easy, +50 XP)
• RANK vs DENSE_RANK: See the Difference Side by Side (sql, easy, +50 XP)
• SUM OVER PARTITION BY: Running Total Per Department (sql, easy, +50 XP)
• LAG: Show Previous Day's Sales Amount (sql, easy, +50 XP)
• LAG: Calculate Day-Over-Day Sales Change (sql, easy, +50 XP)
• LEAD: Show Next Day's Sales Amount (sql, easy, +50 XP)
• LAST_VALUE: Show Last Day's Sales on Every Row (sql, easy, +50 XP)
• ROW_NUMBER: Remove Duplicate Rows and Keep One Per Order (sql, easy, +50 XP)
• Customers Who Never Placed an Order (sql, easy, +50 XP)
• Find Customers Who Placed More Than 5 Orders (HAVING) (sql, easy, +50 XP)
• Monthly Revenue Report Using DATE_FORMAT (sql, easy, +50 XP)
• Find the Customer Who Placed the Most Orders (sql, easy, +50 XP)
• Find Products That Have Never Been Ordered (sql, easy, +50 XP)
• Fix the Broken Pipeline (pyspark, medium, +75 XP)
• Current and Previous Address Tracking with SCD Type 2 (sql, medium, +100 XP)

Live Databricks Data Engineering jobs

• Director, Americas Field Marketing at Databricks — United States
• Sr. Product Manager, Data Engineering — San Francisco, California
• Engineering Manager - Databricks SQL Control Plane — Mountain View, California
• Sr. Product Manager, Databricks AI — San Francisco, California
• Sr. Product Manager, Databricks AI — Seattle, Washington
• Sr. Product Manager, Databricks Repos — San Francisco, California

What does a Databricks Data Engineer interview cover?

Spark fundamentals (lazy evaluation, shuffles, partitioning), performance tuning (broadcast joins, AQE, skew), Delta Lake/lakehouse concepts, plus SQL and coding rounds.

How do I practice Spark for a Databricks interview without a cluster?

Run real PySpark in the browser on PySpark.in — the challenges below execute on an actual Spark session, no setup needed.

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