Schema changes are inevitable, but performing them safely in production is one of the most challenging aspects of backend engineering. As systems grow, migrations must handle dependency ordering, rollback strategies, long-running operations, and coordination across services without causing downtime or data corruption.

In this session, I'll present the architecture and implementation of a migration platform built in Python for large-scale MongoDB applications. The system uses MongoEngine for data modeling, directed acyclic graphs (DAGs) to manage migration dependencies, and AI-assisted tooling to generate and validate migration steps.

We'll explore:
- Designing migration workflows for safety and reliability
- Representing migrations as DAGs to enforce execution order
- Handling failures, retries, and rollback scenarios
- Using AI to reduce manual migration authoring effort
-Operational considerations: observability, auditing, and deployment strategies
-Lessons learned from running migrations in production environments