Technical screening questions
Ask for one example per question and listen for specifics.
- How do you partition data in S3? A good answer: By query pattern, with file sizing in mind.
- Glue or EMR? A good answer: Managed serverless ETL versus cluster control.
- How do you handle late data? A good answer: Reprocessing windows and idempotent writes.
- How do you secure a data lake? A good answer: IAM, Lake Formation and encryption.
- How do you cut Redshift cost? A good answer: Sizing, distribution keys and workload management.
Quick screening questions
Use these when you have only a few minutes.
- How would you design an S3-based data lake with Glue and Athena?
- How do you handle schema changes in a pipeline?
- How do you control cost on Redshift or Athena?
What separates the levels
Match the level to the requirement before you submit.
- Junior: maintains jobs.
- Mid: builds pipelines.
- Senior: designs the data platform.
Red flags
Any of these is a reason to probe further.
- Only console clicks.
- No orchestration experience.
- Cannot explain partitioning.
Tools you should hear mentioned
An AWS data engineer with real experience usually names several of these without prompting.
- AWS Glue
- S3
- Redshift or Athena
- Lambda and Step Functions
- Airflow
- Terraform
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 AWS data engineer 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.