A new column was added without informing the data team, causing the pipeline to fail. How would you prevent such situations?

Preventing silent schema changes involves several practices. First, use a schema registry (e.g., Confluent Schema Registry) that enforces compatibility rules and requires explicit versioning. Second, implement automated schema validation tests that run before deployment, checking that new schemas are backward compatible with existing consumers. Third, establish a change‑management process: any schema change must be documented, reviewed, and communicated to all downstream teams via a shared channel or ticketing system. Fourth, design pipelines to be tolerant of optional columns by using nullable fields or default values. Finally, monitor pipeline health with alerts for schema mismatches and roll back quickly if a failure occurs. These steps reduce the risk of unnoticed schema changes and pipeline failures.