Context
FinSight, a B2B analytics company, runs nightly ETL pipelines that ingest transactional data from PostgreSQL and S3 into Snowflake. The team uses GitHub Actions, Terraform, Docker, and Apache Airflow 2.x for deployments, but recent releases have failed intermittently, causing missed SLAs and broken downstream dashboards.
You are asked to design a practical troubleshooting and deployment-hardening approach for pipeline deployment failures across code, infrastructure, orchestration, and data quality layers.
Scale Requirements
- Pipelines: 120 Airflow DAGs across batch ETL and ELT workloads
- Deployment frequency: 15-20 production releases per week
- Data volume: 8 TB/day across 2,500 source tables and files
- SLA: Critical DAGs must start within 10 minutes of deployment
- Recovery target: Failed deployment rollback or mitigation within 15 minutes
- Retention: Deployment logs, task logs, and audit events retained for 90 days
Requirements
- Design a deployment troubleshooting workflow that isolates failures in CI/CD, container build, IaC changes, Airflow DAG parsing, secrets/configuration, and warehouse connectivity.
- Define pre-deployment validation checks for DAG syntax, dependency conflicts, dbt/SQL model validation, and Terraform plans.
- Propose a safe rollout strategy for Airflow DAGs and ETL code, including rollback and partial deployment handling.
- Add mechanisms to detect whether a deployment failure is caused by infrastructure drift, schema changes, bad credentials, or data quality rule regressions.
- Specify logging, metrics, and alerting needed for fast root-cause analysis.
- Ensure deployments are idempotent and do not trigger duplicate loads or corrupt target tables.
Constraints
- AWS-based stack only: ECS, ECR, MWAA or self-managed Airflow, S3, CloudWatch, Snowflake
- Small team: 3 data engineers and 1 platform engineer
- Budget increase limited to $8K/month
- SOX-style auditability required for production changes
- Production deployments allowed only during a 2-hour nightly release window
