dbt Analytics Engineer

Expert analytics engineering skill for dbt Core/Cloud — guides you through SQL-based data modeling with staging/intermediate/mart layers, dimensional modeling (Kimball methodology), incremental models, SCD Type 2 snapshots, comprehensive testing strategies, Jinja macros, dbt packages, and warehouse-specific optimization for Snowflake, BigQuery, and Redshift.

Gold

v1.0.00 activationsData & AnalyticsTechnologyadvanced

SupaScore

84.4

Research Quality (15%)

8.5

Prompt Engineering (25%)

8.5

Practical Utility (15%)

8.5

Completeness (10%)

8.5

User Satisfaction (20%)

8.2

Decision Usefulness (15%)

8.5

Best for

▸Building production-grade staging/intermediate/mart layered dbt projects with proper materialization strategies
▸Implementing incremental models and SCD Type 2 snapshots for large fact tables in Snowflake/BigQuery/Redshift
▸Designing Kimball dimensional models with proper grain definition and slowly changing dimension handling
▸Creating comprehensive dbt testing frameworks with data quality monitoring and custom Jinja macros
▸Optimizing warehouse-specific performance with clustering, partitioning, and incremental processing strategies

What you'll get

●Complete dbt model files with proper staging/intermediate/mart layering, including YAML schema definitions and comprehensive testing configurations
●Optimized incremental model implementations with proper merge strategies and performance tuning for specific warehouse platforms
●Dimensional model architectures with fact/dimension table designs, SCD handling, and business logic documentation

Not designed for ↓

×Basic SQL query writing or database administration tasks
×ETL tool configuration outside of dbt (Airflow, Fivetran, Stitch)
×Data visualization and dashboard creation
×Machine learning feature engineering or model deployment

Expects

Specific business requirements, existing dbt project structure, source data schemas, and target warehouse platform details.

Returns

Complete dbt model implementations with proper layering, YAML configurations, testing strategies, and warehouse-optimized SQL with detailed documentation.

Evidence Policy

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

dbtanalytics-engineeringdata-modelingdimensional-modelingkimballsnowflakebigqueryredshiftincremental-modelsscd-type-2jinjadbt-utilsdata-testingsql

Research Foundation: 9 sources (7 official docs, 1 books, 1 industry frameworks)

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

Version History

v1.0.02/15/2026

Initial release

Prerequisites

Use these skills first for best results.

SQL Query OptimizerPlatinum

Works well with

BigQuery Analytics ExpertGold Data Quality EngineerGold Data Warehouse ArchitectGold Snowflake Data Modeling ExpertGold SQL Performance Tuning ExpertPlatinum

Need more depth?

Specialist skills that go deeper in areas this skill touches.

Data Mesh ArchitectGold Real-Time Analytics ArchitectGold MLOps Platform EngineerGold

Common Workflows

Modern Data Stack Implementation

Complete modern analytics stack setup from warehouse design through dbt modeling to quality monitoring

Data Warehouse Architect→dbt-analytics-engineer→Data Quality Engineer

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