analytics-engineering

name	analytics-engineering
author	JM Labs (Javier Montaño)
description	dbt-style transformations, dimensional data modeling, testing strategies, and documentation generation for analytics platforms. Trigger: "analytics engineering", "dbt", "data modeling", "dimensional model", "star schema", "data marts".
allowed-tools	["Read","Glob","Grep","Bash","Agent"]

Analytics Engineering

Design and implement analytics engineering practices: dbt-style SQL transformations, dimensional data modeling, testing strategies, and automated documentation for data warehouses and lakehouses.

Guiding Principle

"Analytics engineering applies software engineering discipline to data transformation — version control, testing, documentation, and code review are non-negotiable."

Procedure

Step 1 — Data Modeling Design

1. Identify business processes and grain for each fact table
2. Design dimensional model: facts, dimensions, bridges, and degenerate dimensions
3. Apply naming conventions: fct_, dim_, stg_, int_ prefixes
4. Define slowly changing dimension strategy (SCD Type 1, 2, or 3) per dimension
5. Produce an ER diagram with grain, cardinality, and key relationships annotated

Step 2 — Transformation Layer Architecture

1. Design staging models: 1:1 with source, renaming, typing, basic cleaning
2. Design intermediate models: business logic, joins, calculations
3. Design mart models: final consumer-facing tables optimized for query patterns
4. Implement incremental models where data volume warrants it
5. Define materialization strategy per model: view, table, incremental, ephemeral

Step 3 — Testing & Quality

1. Implement schema tests: not_null, unique, accepted_values, relationships
2. Design custom data tests for business logic validation
3. Implement freshness checks on source tables
4. Define test severity levels: error (blocks pipeline) vs. warn (alerts only)
5. Build test coverage metrics targeting >80% of critical columns

Step 4 — Documentation & Lineage

1. Write model descriptions and column-level documentation
2. Generate and publish dbt docs site for data consumers
3. Implement column-level lineage through model references
4. Create a data dictionary with business definitions for key metrics
5. Establish a change management process for model modifications

Quality Criteria

• Every model has a description and column-level documentation for key fields
• Schema tests cover all primary keys (unique + not_null) and foreign keys (relationships)
• Incremental models handle late-arriving data correctly
• Data dictionary defines all business metrics with calculation logic

Anti-Patterns

• Models with hundreds of lines of SQL and no intermediate abstractions
• Testing only at the mart layer, missing quality issues in staging
• Materializing everything as tables when views would suffice
• Undocumented models that only the author understands