Snowflake Data Engineer Interview Questions

Automatic clustering lets Snowflake reorganize micro-partitions in the background based on the clustering key. Manual clustering means the engineer is more directly involved in deciding when and how to optimize table layout. I would prefer automation for most workloads, but I would still inspect the actual query patterns before relying on it blindly.

I would use Snowpipe or another ingestion path for landing data quickly, then use Streams and Tasks for incremental transformation. This keeps processing incremental instead of forcing full reloads. The main goal is to make ingestion durable, transformation repeatable, and latency predictable.

Streams capture change data so downstream jobs can process only new or modified rows. Tasks schedule or orchestrate SQL logic or procedural workflows. Together, they are useful for building incremental pipelines that are simpler than constantly reprocessing whole tables.

I would use a combination of change detection, merge logic, and effective dating columns to preserve history. When a tracked attribute changes, I would close out the old record and insert a new one instead of overwriting history. That preserves auditability and supports historical analysis.

Snowpipe is Snowflake's continuous data ingestion service for loading new files as they arrive. I would use it when I need low-latency ingestion from cloud storage without manually scheduling repeated bulk loads. It is a good fit for event-style or landing-zone-based pipelines.