Retail & Operations Intelligence Platform
// data engineering / backend
A Python-based ELT data engineering platform simulating a multi-location retail business pipeline. The project processes raw transactional, inventory, and store data through a full medallion-style architecture (Bronze, Silver, Gold) using PostgreSQL.
Built ingestion pipelines to load raw CSV datasets into PostgreSQL, followed by in-database SQL transformations for cleaning, validation, deduplication, and aggregation. Implemented retry handling, quarantine logic for failed records, idempotent upserts, and pipeline run tracking to improve reliability and observability.
Developed orchestration workflows using Prefect to automate ingestion, transformation, and validation stages. Created data quality checks to detect invalid or inconsistent records before promoting datasets through the pipeline.
Exposed analytics through a FastAPI REST API with endpoints for daily sales metrics, inventory health alerts, and pipeline monitoring. Containerised the stack with Docker Compose, including PostgreSQL and pgAdmin for local development and testing.
- →End-to-end data engineering workflows
- →ELT pipeline design
- →Medallion architecture patterns (Bronze, Silver, Gold)
- →SQL-based data transformation and deduplication
- →Data quality validation and quarantine logic
- →API-driven analytics serving
- →Workflow orchestration with Prefect
- →Docker-based infrastructure with Docker Compose
- →Automated testing and CI-oriented development practices
Architecture & Engineering Evidence
Project artifacts, execution results, and technical evidence from the implementation.
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