Snowflake Data Engineer Interview Questions

I would use a view for logic reuse without storing results, a materialized view when the same query is expensive and frequently reused, and a table when I need persisted transformed data or full control over refresh timing. The choice depends on query cost, freshness needs, and maintenance overhead. There is no single default that wins in every case.

I would inspect file format settings, stage access, permissions, data type mismatches, and load errors in the return output. Load failures are often caused by malformed files or unexpected schema differences rather than a platform problem. Once I know the exact error class, the fix usually becomes clear.

I would look at partition pruning, clustering, predicates, and whether the query is written in a way that blocks optimization. If functions wrap filter columns or joins are not selective, Snowflake may have to scan too much. The best fix is often to rewrite the query so the optimizer can eliminate irrelevant micro-partitions.

I would centralize policy, standardize roles, and make ownership visible so teams can self-serve safely. Governance should not slow people down unnecessarily, but it must make sensitive data access auditable and controlled. The best systems make the secure path the easiest path.

I would connect my answer to building reliable, scalable data infrastructure where performance and governance both matter. A strong response should show that I am excited by the lakehouse-style problems Snowflake solves and the engineering discipline needed to make data easy to use safely at scale.