Full DataOps Chain AI Prompt

Step 1: Maturity assessment - score the current team on: version control, automated testing, CI/CD, monitoring, documentation, and incident management. Identify the two lowest-scoring dimensions and set 90-day improvement targets.
Step 2: Pipeline testing strategy - design the test pyramid for the stack. Implement unit tests for transformation logic. Configure dbt or Great Expectations for data quality tests. Create synthetic test data for integration tests.
Step 3: CI/CD pipeline - configure CI with linting, unit tests, smoke tests, and schema validation. Configure CD with environment promotion gates, staging integration tests, and automated production deployment with rollback capability.
Step 4: Monitoring and alerting - set up pipeline health metrics (success rate, duration trend, retry rate). Configure freshness monitoring per critical table. Implement row count anomaly detection with seasonality adjustment.
Step 5: Incident management - write a runbook for the top 5 most common failure modes. Set up Slack/PagerDuty alerting with escalation policies. Run the first blameless post-mortem simulation to build the muscle.
Step 6: Data quality framework - implement schema validation at ingestion, completeness/validity/consistency checks at each pipeline stage, and a DQ score dashboard by tier.
Step 7: Documentation and governance - register all production pipelines in the data catalog with owner, SLA, and lineage. Set up schema version control with Flyway or Liquibase. Establish the data contract registration process for all new data products.