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