This project is a portfolio case study around a problem I find especially interesting: building a solid data platform that can support analytics without becoming fragile or overly complex.
The goal
Create a workflow that takes data from multiple operational sources, lands it safely, transforms it with clear business logic, and makes it available in a format that analysts and stakeholders can trust.
Architecture
The platform was designed in layers:
- raw ingestion for reproducible source capture
- staging models for cleaning and standardisation
- core models for business entities and shared logic
- marts for reporting, dashboards, and product metrics
I also included orchestration, basic testing, and deployment-friendly structure so the system could be maintained like an engineering product rather than a one-off analytics script.
Principles behind the build
A good data platform should make the right thing easy. That means predictable schemas, documented models, version-controlled transformations, and visibility into failures before they become business problems.
For this reason, the case study focused on observability and maintainability just as much as on throughput or speed.
Outcome
The result is a strong example of how I think about data engineering: clean foundations, reusable modelling patterns, and a platform that helps teams answer questions faster without losing trust in the numbers.