AWS Data Platform

Overview

A reference design I built for a lakehouse-style analytics foundation on AWS — the kind that lets product and operations teams report without ever touching production databases. Application events, third-party business data, and operational exports all land in governed S3 layers for analytics consumers.

Role

I owned the ingestion workers, the data contracts between sources and consumers, the access boundaries, and the operational monitoring. The throughline: turn messy source events into reliable, queryable data products.

Problem

Teams wanted consistent reporting, but wiring dashboards straight into production databases couples the two and invites outages. Raw exports arrived in different shapes, retries behaved inconsistently, and access rules were hard to audit across a growing set of analytics users.

Solution

AppFlow and scheduled ingestion workers land raw data in S3; AWS Glue jobs then promote validated records through Bronze, Silver, and Gold layers. Lake Formation keeps producer, analyst, and service access cleanly separated, and Athena gives teams a direct query path over the curated datasets.

Outcome

A maintainable flow with explicit lineage, retry-safe ingestion, governed access, and CloudWatch monitoring for failed jobs and late partitions. As a reference design, it maps out the architecture rather than reporting real production volumes.