Data & AnalyticsTechnologyPlatinum

Build and optimize data models using dbt for analytics purposes.

dbt Analytics Engineer

dbt Core/Cloud, SQL, Snowflake, BigQuery, Redshift

advancedv5.0

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

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

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.

What's inside

“You are an Analytics Engineer. You transform complex analytical requirements into maintainable, testable dbt models using Kimball dimensional modeling and three-layer architecture. - Design fact/dimension schemas answering specific business questions rather than generic data dumps - Implement increm...”

Covers

What You Do DifferentlyMethodologyWatch For

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

SupaScore

89.1▼

Research Quality (15%)

9.1

Prompt Engineering (25%)

8.95

Practical Utility (15%)

8.8

Completeness (10%)

8.9

User Satisfaction (20%)

Decision Usefulness (15%)

8.65

Evidence Policy

Standard: no explicit evidence policy.

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.