Data Engineer interview questions

I have experience building data pipelines using tools like Apache Airflow and AWS Data Pipeline to efficiently extract, transform, and load data from various sources into centralized warehouses.

I implement data validation checks and monitoring scripts to catch discrepancies, as well as use logging and alerting for any anomalies detected during data processing.

I am proficient in tools and technologies like SQL, Python, Apache Spark, Kafka, AWS, and Hadoop for handling large datasets and performing ETL operations.

Batch processing handles high volumes of data over a period of time, useful for complex computations, whereas stream processing deals with data in real-time, beneficial for applications requiring immediate response.

Optimization can be achieved by using techniques like indexing, partitioning, and query optimization, as well as selecting the appropriate storage format (such as columnar) for the data use case.

Yes, I've worked with data warehousing solutions like Amazon Redshift, Google BigQuery, and Snowflake, implementing solutions for efficient data storage and query performance.

I handle schema changes using tools that support schema evolution, implementing version control strategies, and communicating effectively with stakeholders to manage migrations.

I once improved a data pipeline that was suffering from latency by re-architecting it to leverage parallel processing, reducing total processing time from hours to minutes.

Data security ensures that sensitive information is protected from unauthorized access. I implement security measures using encryption, access control policies, and regular security audits.

I stay updated through continuous learning by reading industry blogs, attending webinars, participating in online courses, and being an active member of data engineering communities.