Data Engineering System
dbt E-commerce Analytics Models
Created tested, documented analytics models with lineage so reporting can use consistent business definitions instead of raw source tables.
- Raw sources
- Staging
- Intermediate
- Marts
Problem
Raw e-commerce tables are hard to analyze directly and can create inconsistent definitions for orders, customers, revenue, and product performance.
Solution
Built a dbt transformation project using staging, intermediate, and mart layers to produce governed customer, order, product, and revenue models.
Results
Created tested, documented analytics models with lineage so reporting can use consistent business definitions instead of raw source tables.
Solution
The project converts raw source tables into documented, tested dbt models. Staging models standardize source fields, intermediate models hold reusable business logic, and marts serve reporting use cases.
Architecture
Raw source tables, dbt staging models, intermediate business logic, fact and dimension marts, and documented lineage.
Scalability Considerations
Use incremental models for large fact tables, partition by date where supported, and choose materializations based on query frequency and cost.
Engineering Decisions
- Separated cleaning logic from business logic to keep models maintainable.
- Used tests to validate keys, null handling, and model assumptions.
- Modeled marts around analytical questions instead of source-system structure.
Tradeoffs Considered
- Layered models improve maintainability, but too many layers can slow development and increase build time.
- Table materializations improve dashboard performance, but they increase storage and refresh cost.
- Incremental models reduce processing time, but they require strong unique keys and late-arriving data handling.
Results
- Produced analytics-ready customer, order, and product models.
- Improved metric consistency through centralized transformations.
- Added tests and lineage to make model behavior easier to review.
What I Would Do In Production
Add source freshness checks, exposures, CI validation on pull requests, semantic metric definitions, environment-specific deployment, and scheduled documentation generation.