Data engineering interview questions · easy
What are Airflow Operators? Give examples.
Explain the difference between Azure Data Factory (ADF) and Databricks.
How do you handle data security and compliance in a cloud environment?
What is Azure Data Factory (ADF), and what are its main components?
What is the role of the Integration Runtime (IR) in ADF?
API calling with Airflow?
Airflow operators, hooks, and scheduler functionality?
Azure Functions vs. Logic Apps?
Can you explain your experience with Docker and Kubernetes?
Can you explain your experience with Jenkins in your project?
Cloud Composer Overview
Compare ADF vs. Databricks.
Data Factory vs. Databricks: When to use which?
Describe a real-world use case for using Step Functions with Lambda in a data workflow.
Describe a scenario where AWS Data Pipeline is preferred over Glue. Why?
Describe an AWS EC2 instance and how IAM roles/policies enhance security.
Describe how to secure sensitive data in cloud storage solutions.
Describe how to set up retries and timeout for tasks in Cloud Composer.
Describe how you deploy code to a production environment using Jenkins
Describe how you would use AWS Glue to schedule and manage Spark jobs.
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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.