data-analytics-engineering

# Data Analytics Engineering Code-defined marts, metrics as APIs, contracts on critical interfaces, semantic layers only where they improve reuse or AI/BI consumption, and metadata systems that expose owners, lineage, quality, and governance to both humans and agents. Primary sources: `data/sources.json`. Refresh time-sensitive claims against official docs before giving definitive recommendations. ## When to Use - Choose or improve an analytics engineering stack (`dbt`, `SQLMesh`, `Coalesce`) - Define marts, grains, dimensions, facts, wide tables, or activity schemas - Design or migrate a semantic layer (`dbt Semantic Layer`, `Lightdash`, `Cube`, warehouse-native) - Add data contracts, metric governance, ownership, catalogs, and lineage - Build data quality checks, freshness monitoring, anomaly detection, and release gates - Prepare BI-ready models for dashboards, notebooks, APIs, or AI/NLQ analytics ## When NOT to Use - Lakehouse or ingestion architecture -> [data-lake-platform](../data-lake-platform/SKILL.md) - Product/event instrumentation, attribution, or identity resolution -> `marketing-product-analytics` - OLTP tuning, indexes, locks, or transactional database operations -> [data-sql-optimization](../data-sql-optimization/SKILL.md) - Metabase API automation -> [data-metabase](../data-metabase/SKILL.md) - ML feature engineering, experiments, or model evaluation -> [ai-ml-data-science](../ai-ml-data-science/SKILL.md) ## Triage Checklist Run through these before a...