Transform manual ETL routines into self-running pipelines
Most data teams spend more time babysitting pipelines than building models. Jobs fail silently at 3am. Schema changes upstream break everything downstream. Someone always ends up manually rerunning a stuck transformation while stakeholders wait for dashboards to refresh.
How the Data Workflows works
The Data Workflow Automator sits between your sources and your warehouse. It watches for new files or database changes, triggers transformation jobs when conditions are met, and validates that output matches expected schemas before marking a run complete. When something breaks, it isolates the failure point and notifies the right engineer with context about what went wrong.
Core capabilities:
- Detects new data arrivals across S3, databases, APIs, and file drops
- Triggers transformation jobs in Airflow, dbt, or custom scripts
- Validates row counts, schema integrity, and data freshness thresholds
- Creates incident tasks with failure logs when pipelines stall
Why you need the Data Workflows
Data engineering squads managing 20 or more scheduled jobs will see the biggest impact. If your team currently relies on Slack pings about failed DAGs or manual checks every morning, this agent replaces that overhead with proactive orchestration.
How the Data Workflows compares
The Data Quality Agent focuses on validating records after they land. The Data Workflow Automator handles the orchestration layer that gets data to that point. Pair them together for end-to-end coverage from ingestion through validation.
