Technical screening questions
Ask for one example per question and listen for specifics.
- What causes a shuffle? A good answer: Wide transformations that redistribute data.
- How do you fix skew? A good answer: Salting, broadcast joins or repartitioning.
- Cache or persist? A good answer: Reuse across actions versus memory cost.
- How do you read the Spark UI? A good answer: Stages, tasks, spills and skewed tasks.
- How do you test Spark code? A good answer: Local sessions and small datasets.
Quick screening questions
Use these when you have only a few minutes.
- How do you diagnose data skew in a Spark job?
- What is the difference between a narrow and a wide transformation?
- How do you choose partition counts?
What separates the levels
Match the level to the requirement before you submit.
- Junior: writes transformations.
- Mid: owns jobs and tuning.
- Senior: designs processing architecture.
Red flags
Any of these is a reason to probe further.
- Only wrote SQL on Spark.
- Cannot read the Spark UI.
- No tuning examples.
Tools you should hear mentioned
An Apache Spark developer with real experience usually names several of these without prompting.
- Apache Spark
- PySpark or Scala
- Parquet
- Hive or Delta
- Airflow
- YARN or Kubernetes
Run the call well
Ask for the last project first, then dig into the second or third question. Take notes on versions, team size and outcomes, and record them with the submission so the vendor’s later technical interview does not surprise you.
Frequently asked questions
How long should an Apache Spark developer screening call take?
Fifteen to twenty minutes is enough to cover the project, three technical questions and availability, rate and work authorization.
Do recruiters need to know the technology?
Working vocabulary is enough. Use the sourcing guide for the role and listen for specifics instead of judging technical depth yourself.