Data engineering interview questions · hard
What are the performance considerations when integrating Logic Apps with ADF?
What are the pricing models for queries in Athena?
What are the pros and cons of using a data lake on AWS, GCP, or Azure?
What is Azure Data Lake Storage (ADLS) Gen2, and how does it differ from Blob Storage?
What is your experience with cloud technologies?
What techniques do you use to balance compute costs and performance in cloud-based data solutions?
What types of queries would not be efficient in Athena?
Which AWS services do you use for data ingestion and processing?
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Learn the platform used by your target companies. AWS is most common overall (Glue, Redshift, S3, Kinesis). GCP is preferred by Google and startups (BigQuery, Dataflow, Pub/Sub). Azure is dominant in enterprise (Synapse, Data Factory). Learn one deeply and understand the equivalents on others.
Core tools: SQL, Python, Spark, Airflow (or equivalent orchestrator), one cloud platform. Increasingly important: dbt, Kafka, Terraform, Docker/Kubernetes, Delta Lake or Apache Iceberg, a data observability tool. The specific stack varies by company.
Yes. Apache Airflow is the most widely used orchestration tool and questions about DAG design, task dependencies, XComs, operators, and failure handling are common. If the company uses a different orchestrator, expect similar questions adapted to their tool.