AWS Data Platform

Overview

This seeded case study models a cloud data platform for product and operations reporting. The platform ingests application events, third-party business data, and operational exports into governed S3 layers for analytics consumers.

Role

Nguyen's representative role is backend engineer responsible for ingestion workers, data contracts, access boundaries, and operational monitoring. The work focuses on turning raw source events into reliable, queryable data products.

Problem

Teams needed consistent reporting without coupling dashboards directly to production databases. Raw exports arrived with different shapes, retry behavior was inconsistent, and access rules were difficult to audit across analytics users.

Solution

The platform uses AppFlow and scheduled ingestion workers to land raw data in S3, then AWS Glue jobs promote validated records through Bronze, Silver, and Gold layers. Lake Formation policies separate producer, analyst, and service access, while Athena gives teams a direct query path over curated datasets.

Outcome

The seeded platform demonstrates a maintainable data flow with explicit lineage, retry-safe ingestion, governed access, and CloudWatch-based monitoring for failed jobs and delayed partitions.