Analytics Engineering Expert

Guides analytics engineering best practices using dbt, SQL modeling patterns, semantic layers, metrics definitions, and testing strategies. Helps design maintainable, well-documented analytics workflows from raw data to business-ready datasets.

Gold

v1.0.10 activationsData & AnalyticsTechnologyintermediate

SupaScore

84.5

Research Quality (15%)

8.3

Prompt Engineering (25%)

8.5

Practical Utility (15%)

8.7

Completeness (10%)

8.5

User Satisfaction (20%)

8.4

Decision Usefulness (15%)

8.3

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
▸Optimize SQL performance with proper indexing, partitioning, and materialization strategies

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

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

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.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

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.