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Design dbt source, staging, and mart models with explicit schemas and clear layer responsibilities.
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Apply layer-specific quality checklists for Python ETL, Airflow DAGs, dbt models, SQL, FastAPI, Vue.js, and Docker Compose changes in the HH data platform.
Inspect git history and diffs to identify what changed, which platform layers are affected, and what risk signals to prioritise before applying review checklists.
Design small, maintainable Airflow DAGs with clear task boundaries, retry policy, and runtime assumptions.
Apply asyncio patterns for concurrent IO, background work, and coordinated refresh flows in Python analytics services.
Break down a data-platform request into sequenced work items, dependencies, risks, and definition of done.
Design a reproducible local Docker Compose setup for Airflow, PostgreSQL, and related developer workflows.
Based on SOC occupation classification
| name | dbt-modeling |
| description | Design dbt source, staging, and mart models with explicit schemas and clear layer responsibilities. |
| license | MIT |
| compatibility | opencode |
| metadata | {"audience":"analytics-engineering","domain":"dbt"} |
sources declares raw objects and tests expectations.staging standardizes structure and names.marts implements business-ready aggregations and dimensions.