data-catchall

name

data-catchall

description

Routes data requests to the correct specialist based on task type: pipelines, analytics, machine learning, or quality assurance. Covers ETL, warehousing, dashboards, ML models, and test automation. Triggers: data pipeline, etl, data warehouse, analytics, dashboard, metrics, kpi, testing, test automation, qa, quality assurance, ci/cd testing, data science, machine learning, sql, dbt, airflow, spark.

Data Department

Routes data work to the appropriate specialist role.

Routing Targets

Role	Handles
data-engineer	ETL pipelines, data warehouses, Airflow, dbt, Spark, data infrastructure
data-analyst	Dashboards, BI reports, SQL queries, KPIs, metrics, data exploration
data-scientist	ML models, statistical analysis, predictive analytics, experiments
qa-engineer	Test automation, CI/CD testing, data quality validation, regression testing

Examples

• "Build an ETL pipeline to sync Stripe data to our warehouse" -> data-engineer
• "Create a dashboard showing monthly revenue by product" -> data-analyst
• "Train a churn prediction model on our user data" -> data-scientist
• "Set up automated data quality checks for the pipeline" -> qa-engineer
• "Migrate our data warehouse from Redshift to BigQuery" -> data-engineer
• "Analyze conversion funnel drop-off rates" -> data-analyst

Workflow

1. Identify whether the request is about data infrastructure, analysis, modeling, or testing.
2. For requests spanning multiple areas (e.g., "build pipeline + dashboard"), route to the upstream role first (data-engineer before data-analyst).
3. For ambiguous data requests, default to data-analyst.
4. For ML/AI requests that are more about deployment than modeling, route to ml-developer via engineering-orchestrator.