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.

What to emphasise for Databricks

Databricks works on the lakehouse platform they build, so it is worth going in strong on Apache Spark internals, Delta Lake table mechanics, and the trade-offs between batch and streaming — depth on Spark is the natural focus for a company whose product is Spark. Treat that as where to spend your last few days of revision, not as a script — the fundamentals below are what actually get tested, and a candidate who can write clean SQL and explain a pipeline end to end does well regardless of the company. Note that interview formats change frequently and vary by team and level, so use this as preparation guidance rather than a guarantee of what you will be asked.

What a Data Engineer interview usually covers

Most Data Engineering loops test four things, in some combination. First, SQL — almost always the deepest-tested skill, with joins, aggregation, window functions and top-N-per-group questions appearing again and again. Second, data modelling and pipeline design: how you would ingest a source, where you would clean it, how you would handle late or duplicate records, and how the tables would be laid out for consumption. Third, coding in Python or PySpark, usually a transformation rather than a puzzle. Fourth, a discussion of something you have actually built, where the interviewer probes your decisions and what you would change.

How to practise so it sticks

Reading solutions creates a false sense of readiness. Write the query yourself, run it, and check the output against the expected result — that feedback loop is what turns recognition into recall. Work in patterns rather than one-off puzzles: once you can write a top-N-per-group query from memory, a whole family of questions becomes routine. Then practise explaining your approach out loud before you type, because most interviews assess your reasoning as much as your final answer. Finish by rehearsing two or three projects in enough detail to discuss the trade-offs you made.

A four-week preparation plan

Week one: rebuild SQL fundamentals — joins, grouping, subqueries and CTEs — until they are automatic. Week two: window functions and the analytics patterns built on them (running totals, ranking, period-over-period, deduplication). Week three: PySpark — the DataFrame API, joins and shuffles, partitioning, and why a job is slow. Week four: system-style design questions and behavioural preparation, plus timed mixed practice so you are used to switching between question types. Throughout, keep a short list of the mistakes you actually make and re-test yourself on those, since that is where the marginal gains are.

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

• Senior Manager - Technical Solutions (Big Data / AI) — Bengaluru, India
• Senior Solutions Architect (EDW Enterprise Data Warehouse Migrations) — Amsterdam, Netherlands
• Senior Solutions Architect (EDW Enterprise Data Warehouse Migrations) — Milan, Italy
• Senior Solutions Architect (EDW Enterprise Data Warehouse Migrations) — Paris, France
• Senior Solutions Architect (EDW Enterprise Data Warehouse Migrations) — London, United Kingdom
• Senior Solutions Architect (EDW Enterprise Data Warehouse Migrations) — Berlin, Germany; Munich, Germany

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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