Shopping Graph Data & AI Platforms

2024 — present

Overview

As a Software Engineer at Google Zurich, I lead the development of scalable data processing infrastructure, relational view layers, standardized metric aggregations, and LLM-powered automation frameworks within the Google Shopping Graph ecosystem.

Key Contributions

• Standardized Aggregations architecture — architected and led the Standardized Aggregations initiative across Google Shopping data platforms, establishing consistent, scalable, governance-approved aggregation pipelines; engineered custom User-Defined Aggregations and demand-weighted statistical rollups (Demand-Weighted Counts, Price Competitiveness views) over massive merchant datasets
• Autonomous production automation — designed and deployed LLM-powered triage agents, workflow orchestrators and staleness debuggers that automate root-cause analysis and drastically reduce manual on-call diagnostic time for production data pipelines
• AI tooling and context discovery — built reusable Model Context Protocol server frameworks and progressive context-discovery mechanisms, so AI coding agents can navigate complex developer ecosystems and internal APIs efficiently
• Relational view and schema architecture — unified data-access layers, schema definitions and relational view services for Shopping Graph APIs, streamlining cross-system integration and querying
• Data health and release governance — automated data-validation frameworks, large-scale historical backfilling utilities and CI/CD rollout tracking that safeguard data freshness and operational reliability across engineering teams