data-catchall

Category: Data & AI | Uploader: Chipagosfinest | Downloads: 0 | Version: v1.0（Latest）

analytics, machine learning, or quality assurance. Covers ETL, warehousing,

Changelog: Source: GitHub https://github.com/Chipagosfinest/enterprise-team

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Current level: tree/main/enterprise-team/skills/data-catchall/

📄 SKILL.md 1.8 KB

SKILL.md

---
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.

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