Digital Knowledge Graph
Amazon Digital Pricing Research · Software Development Engineer II
Overview
I conceived Amazon’s Digital Pricing Knowledge Graph, wrote its PR/FAQ, pitched the idea to leadership and secured funding for its development.
I then led its architecture and development, building a federated graph-based platform for product selection, segmentation, A/B experimentation and catalog recommendations across Amazon’s digital businesses.
Key contributions
- Graph architecture: Designed the graph model and built the platform using Java, Gremlin, a domain-specific language and Amazon Neptune, supporting 50M+ vertices and adoption by 5+ global business units.
- Federated data ownership and access: Designed a federated system that let teams independently stream data into the shared graph while retaining ownership of their data. Built a custom DSL layer that added properties to graph entities to support granular access controls.
- Boolean rule-engine optimization: Integrated a custom Boolean rule engine into the graph model, achieving a 99.999% reduction in graph size and increasing product-segmentation capacity 1,000×.
- Constant-time metadata selection: Built a metadata-selection framework covering 10M+ products, enabling constant-time selection and reducing selection workflows from 24 hours to under 15 minutes, while supporting flexible, dynamic pricing strategies.
- Multi-tenant ingestion: Developed a high-throughput, low-latency multi-tenant microservice ingesting data from different digital businesses. It handled terabytes of catalog data and powered millions of pricing computations per hour.