dbt Analytics Engineering (AWS Redshift)

In Progress

dbt project for building analytics data models on AWS Redshift using staging, intermediate, and mart layers. Based on the Olist Brazilian e-commerce dataset (2016–2019), designed for self-service analytics with dimensional modelling.

Technologies

dbtAWS RedshiftSQLAWS GluePython

Problem

Raw source data needs structured transformation into analytics-ready dimensional models to enable BI teams and analysts to work independently without touching raw tables.

Approach

ELT pattern with dbt on Redshift: staging layer (source-conformed views) → intermediate layer (business logic) → marts layer (dimensional models for core and marketing schemas).

Result

In progress — Foundation structure and model architecture established; implementation ongoing.

Learnings

Dimensional modelling with star schemas for analytics efficiency; ELT patterns are cost-effective when the warehouse handles transformation; dbt best practices: modular models, SQL version control, documentation as a first-class artifact.

Relevance

Demonstrates analytics engineering, dimensional modelling, and modern SQL transformation workflows — a core competency for data-oriented roles.