Context
ParcelFlow, a logistics SaaS company, runs 180 Airflow-managed ETL/ELT pipelines on AWS to move operational data from PostgreSQL, Kafka, and S3 into Snowflake. Releases are slow and error-prone: pipeline changes are deployed manually, infrastructure changes are tracked separately by DevOps, and failed releases often require ad hoc rollback.
You are asked to design a delivery model and technical architecture that improves collaboration between data engineering and DevOps so pipeline changes can be shipped safely, repeatedly, and with clear ownership.
Scale Requirements
- Pipelines: 180 production DAGs, 40 daily code changes, 10 infrastructure changes/week
- Data volume: 12 TB/day batch + 80K events/sec streaming peak
- Deployment target: <15 minutes from merge to production for low-risk changes
- Availability: 99.9% for critical ingestion pipelines
- Recovery: Rollback or forward-fix within 30 minutes
- Environments: dev, staging, prod across 3 AWS accounts
Requirements
- Design a CI/CD process for Airflow DAGs, dbt models, and infrastructure-as-code with clear promotion gates.
- Define how data engineers and DevOps share ownership of deployment standards, secrets, IAM, networking, and runtime operations.
- Add automated validation for DAG syntax, unit tests, data contract checks, dbt tests, and environment-specific smoke tests.
- Support safe deployment patterns for schema changes, backfills, and streaming job updates without duplicate loads.
- Provide observability for deployment health, pipeline freshness, task failures, and infrastructure drift.
- Ensure rollbacks are deterministic and do not corrupt downstream tables or replay data incorrectly.
Constraints
- AWS-first stack; no migration away from Airflow or Snowflake in the next 12 months
- Team: 6 data engineers, 2 DevOps engineers, shared on-call rotation
- Compliance: SOC 2; production access must be audited and least-privilege
- Budget: incremental tooling spend capped at $8K/month
- Existing pipelines must continue running during the transition
