20+ enterprise clients moved between two SaaS platforms with zero data loss and zero production incidents
The Problem
OneTrust acquired Convercent. The compliance records of 20+ enterprise customers had to move from one SaaS platform to the other. The two platforms used different data models. Any lost or corrupted record would become a compliance incident for the customer.
Constraints
- Two different schemas, with 100+ custom fields and 50+ data types to map
- Strict privacy rules on every record
- Millions of compliance records to ingest
- Every environment had to be signed off before the next one: DEV, QA1, QA2, then PROD
What I Did
I owned the migration end to end, from design to the production cut-over.
- Built a mapping engine that transforms records between the two schemas, driven by configuration instead of one-off scripts
- Architected Django REST endpoints on Kubernetes with idempotent writes, schema validation and transactional rollback
- Ran phased validation across DEV, QA1, QA2 and PROD
- Added parallel batch processing for throughput, without losing ordering guarantees
- Produced reconciliation reports that compare source and target counts per client and per data type
Result
20+
Clients Migrated
0
Data Loss Incidents
0
Production Incidents
4
Environments, Phased
Every client moved with zero data loss and zero production incidents. Because the mapping was configuration-driven, each new client was a config change, not a new project.
Stack
Python
Django REST Framework
PostgreSQL
Kubernetes
AWS
Datadog
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