Capgemini’s interview process for a Snowflake Data Engineer is multi-stage and focuses on both technical expertise (especially in Snowflake and related cloud technologies) and soft skills like collaboration and problem-solving.

The process is designed to assess your end-to-end understanding of data engineering pipelines, cloud integration, and your ability to fit into Capgemini’s collaborative work culture.

Differences between ETL and ELT in Snowflake.

Data ingestion strategies and managing streaming vs. batch processing.

Working with external tables, stages, and data sharing in Snowflake.

Hands-on exercises: writing SQL queries, loading/transformation scripts.

Performance optimization strategies (clustering, caching, scaling).

Data governance, lineage, and pipeline monitoring with tools like Airflow.

Integration scenarios: connecting Snowflake with AWS Glue, Lambda, S3, DBT, etc.

Troubleshooting data quality and consistency issues in distributed pipelines.