Data & AnalyticsTechnologyPlatinum

Design efficient analytics workflows using dbt.

Analytics Engineering Expert

dbt, SQL, Metrics, Semantic Layers

intermediatev5.0

Best for

▸Design maintainable dbt project architecture with proper staging, intermediate, and marts layer organization
▸Implement incremental models with merge strategies for efficient large table updates
▸Build comprehensive testing strategy with schema tests, data tests, and business rule validation
▸Create semantic layer with centralized metric definitions using dbt Metrics or MetricFlow

What you'll get

▸Complete dbt project folder structure with staging, intermediate, and marts directories, including proper model dependencies and ref() usage
▸SQL model implementations with proper CTEs, window functions, and incremental logic, plus corresponding YAML configuration for tests and documentation
▸Comprehensive testing framework with schema tests, custom data tests, and metric validation queries with clear documentation

Expects

Specific analytics requirements including business metrics, source data structure, stakeholder needs, and transformation complexity details.

Returns

Complete dbt project structure with properly organized models, comprehensive testing framework, documented SQL transformations, and metric definitions following analytics engineering best practices.

What's inside

“- Apply software engineering discipline to analytics: version control, testing, CI/CD, and code review for every transformation - Design scalable dimensional models (Kimball methodology) that grow from dozens to hundreds of models while remaining comprehensible - Balance competing priorities: query ...”

Covers

What You Do DifferentlyMethodologyWatch For

Not designed for ↓

×Data engineering pipeline orchestration or infrastructure provisioning
×Building real-time streaming analytics or event processing systems
×Creating machine learning features or model training pipelines
×Designing data visualization dashboards or BI tool configurations

SupaScore

88.38▼

Research Quality (15%)

Prompt Engineering (25%)

Practical Utility (15%)

8.75

Completeness (10%)

User Satisfaction (20%)

8.75

Decision Usefulness (15%)

8.5

Evidence Policy

Standard: no explicit evidence policy.

analytics-engineeringdbtsql-modelingsemantic-layermetricsdata-testingdata-documentationincremental-modelsdata-transformationelt

Research Foundation: 8 sources (5 official docs, 2 industry frameworks, 1 community practice)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.