data-engineering-best-practices
Data engineering architect, reviewer, and playbook for the modern data stack (Airflow, dbt, SQL warehouses, Spark, streaming pipelines, data quality, data modeling, orchestration, and schema management). Use when designing pipelines, modeling warehouse schemas, reviewing Airflow DAGs, architecting streaming pipelines, reviewing DE pull requests, writing dbt models, auditing data quality, writing SQL, building Spark jobs, designing data models, diagnosing pipeline failures, or managing schema evolution. Triggers on: pipeline design, DAG review, warehouse modeling, data contract, runbook, postmortem, streaming architecture, dbt, data quality, SQL review, Spark job, data modeling, schema management, orchestration, testing, incident.
development
