Analytics Engineering: Transformation Pipeline Design & Data Modeling

Generic, brand-neutral engineering capability; deep, sourced playbooks live in references/ and knowledge/. [DOC]

Generic, brand-neutral engineering capability; sourced playbooks in references//knowledge/. [DOC]

TL;DR

Analytics engineering defines how raw data is transformed into reliable, documented, and tested analytical models — source-to-target mapping, modeling patterns, transformation frameworks, testing, and documentation. This skill produces analytics engineering documentation that enables teams to build maintainable, trustworthy data transformation pipelines [EXPLICIT]

When to Use

• Designing source-to-target data transformation pipelines
• Selecting data modeling patterns (star schema, OBT, activity schema)
• Setting up dbt or similar transformation framework projects
• Defining testing strategies and data contracts for analytical models
• Planning documentation and data discovery infrastructure
• Optimizing warehouse performance and controlling compute costs

When NOT to Use

• Data ingestion and orchestration pipelines → use data-engineering skill
• Dashboard design and KPI frameworks → use bi-architecture skill
• ML feature engineering and model serving → use data-science-architecture skill
• Data profiling and anomaly detection → use data-quality skill

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Sub-capabilities (resource map)

Deep, evidence-tagged playbooks — open the one the task needs (ICM Layer 3, on-demand). [INFERENCE]

Procedure

1. Resolve the sub-capability; open the matching references/ playbook. [EXPLICIT]
2. Apply its decision tables; pick the strategy explicitly. [EXPLICIT]
3. Validate against the Quality Criteria and tag every claim. [EXPLICIT]

Quality Criteria

• Sub-capability resolved to one playbook. [INFERENCE]
• Claims evidence-tagged. [EXPLICIT]

Contract

• Aceptación: capability resolved to its reference playbook, applied, validated, evidence-tagged. [EXPLICIT]
• Límites: · Focuses on transformation and modeling, not data ingestion · Does not design BI consumption layer (dashboards, KPIs) · Does not address data quality monitoring beyond trans. [EXPLICIT]
• Casos borde: Legacy Stored Procedures Migration: Map existing logic to dbt models, preserve business rules, run parallel validation. Expect 20-30% of stored procedure logic to be obsolete o. [EXPLICIT]
• Supuestos: · Data warehouse or lakehouse is provisioned and accessible · Source data is being ingested (or ingestion is designed in parallel) · Team has SQL proficiency and familiarity with t. [SUPUESTO]
• Trade-off: Decision Enables Constrains Threshold --- --- --- --- Star Schema Fast queries, intuitive for BI, clear grain More joins, ETL complexity Multiple consumptio. [EXPLICIT]

Packet

Capas del packet, cargables bajo demanda (disciplina ICM: una capa por vez, nunca todas juntas): references/ guías de profundidad (cargar UNA por etapa) · knowledge/ cuerpo de conocimiento · prompts/ prompts listos · examples/ salida de ejemplo · agents/ subagentes del packet · assets/ recursos estáticos.