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