Challenge

Legacy data infrastructure managed through shell scripts, cron jobs, and manual monitoring. No centralized visibility into data workflows; failures discovered by business users (“my report is broken”). Dependency management handled via schedules alone; frequent cascading failures. Oncall rotations burned out; no effective runbooks.

Approach

Technical Architecture:

• Apache Airflow as orchestration backbone (High Availability setup with 3 schedulers)
• Containerized operators for isolation and reproducibility
• Standardized DAG patterns/templates for common use cases
• Integrations: Snowflake, S3, Slack, PagerDuty for full observability

Migration Strategy:

1. Pilot phase: Migrate 20% of workflows (2 weeks)
2. Core platform setup: Establish production infrastructure (3 weeks)
3. Wave migration: Migrate remaining 80% (8 weeks, 3 teams)
4. Legacy cleanup: Decommission old infrastructure (2 weeks)

Process Improvements:

• DAG code reviews for quality gate
• Monitoring/alerting: Task failures → Slack → PagerDuty
• Clear runbook database tied to each DAG
• Team training: Hands-on workshop for pipeline owners

Team

• Size: 7 people (1 PM, 2 platform engineers, 2 implementers, 1 DBA, 1 analyst)
• Duration: 14 weeks
• Wave participants: 3 teams, 8 pipeline owners trained

Results

Delivery Metrics

• On-time: ✓ Completed in 14 weeks (planned 14 weeks)
• Budget: $380K actual vs $365K planned (+4%)
• Scope: 100% - All critical workflows migrated

Technical Impact

• Workflow visibility: 100% (was 0% - no centralized view)
• Mean Time to Resolution (MTTR): 45min → 8min (82% improvement)
• Pipeline reliability: 94% → 99.2% on-time completion
• Dependency enforcement: Manual schedule deps → automated graph
• Retry capability: Reduced manual remediation by 60%

Operational Impact

• Oncall burden: 3 incidents/week → 0.3 incidents/week (90% reduction)
• Runbook effectiveness: 100% of failures now have documented remediation
• Knowledge transfer: 8 pipeline owners trained; can now maintain own DAGs
• Code reuse: 40% of DAGs created from standardized templates

Business Impact

• No more report delays: 100% of critical data flows on-time
• Data freshness: Improved for 180+ downstream dashboards
• Team morale: Oncall satisfaction survey improved from 2.1/5 → 4.3/5
• Technical debt: Eliminated $250K/year in legacy support costs

Key Decisions

1. Airflow over custom orchestration - Open source; large community; ownership benefits
2. Wave migration approach - Reduced risk; allowed team to learn and iterate
3. Containerized operators - Isolated from infrastructure; easier to test and maintain
4. Mandatory code review - Prevented DAG antipatterns; improved quality upfront