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Description

The future of ecommerce product data is being rewritten with Generative AI—and Amazon's Catalog Data Quality organization is leading a fundamental transformation in how we manage product information at scale. Our team is re-envisioning how Amazon measures, validates, and improves product data using foundation models, multimodal LLMs, and autonomous agent systems to transform the way customers discover, compare, and purchase products.

As a Sr. Technical Program Manager, you will own the technical execution and strategy for data quality initiatives that ensure Amazon's catalog is complete, correct, and consistent across billions of products and variations. You will translate business requirements into detailed technical specifications and coordinate the design, development, testing, and deployment of GenAI-powered quality capabilities across multiple engineering teams.

You will work closely with applied scientists, software development managers, engineers, and product managers to define how models are selected and deployed for various quality measurement and improvement tasks. You will establish robust technical requirements around accuracy, performance, and quality metrics, create implementation plans that span multiple systems, and ensure seamless integration of quality pipelines into Amazon's catalog infrastructure.

This role offers a rare opportunity to shape the technical foundation of Amazon's next-generation catalog systems while working at the intersection of GenAI innovation and massive-scale distributed systems. If you're excited by the challenge of deploying autonomous AI agents to improve data quality across billions of products and variations and deliver a better customer experience, this is the role for you.

Key job responsibilities
Define technical requirements and system architectures for quality measurement, detection, and correction pipelines across billions of products
Develop quality strategies that determine optimal GenAI model selection to maximize data completeness, correctness, and consistency
Drive cross-team execution across Selling Partner Experiences, Category teams, Relationship Processing, and engineering teams
Create implementation plans and resource estimates for catalog quality initiatives
Establish visibility mechanisms and dashboards that provide stakeholders clear insight into quality metrics, defect resolution rates, and delivery timelines
Coordinate technical escalations, facilitate resolution of design disagreements, and communicate revised plans when risks materialize
Partner with applied scientists to define how LLMs and autonomous agents are deployed for quality improvement at scale

A day in the life
Review quality metrics dashboards and identify trends across priority product categories
Lead cross-functional working sessions with Category teams, Applied Science, and engineers on GenAI model tuning and quality improvements
Attend daily stand-ups and track progress on quality capability rollouts
Collaborate with engineering managers to estimate effort and align on implementation timelines
Prepare executive briefings on program status, quality improvements, and customer experience gains
Facilitate technical escalation meetings to resolve cross-team design disagreements
Review technical design documents and ensure alignment with customer experience priorities
Update program trackers with milestone completions and communicate status to stakeholders
Sync with Selling Partner Experiences teams on seller feedback patterns and recommendation effectiveness

About the team
The Catalog Data Quality team (COMPASS) is the foundational infrastructure team within Catalog System Services (CSS) that builds the measurement, validation, feedback, and learning systems essential for maintaining Amazon's catalog quality at unprecedented scale. We are the navigational framework that ensures every enrichment, every data ingestion, and every seller contribution moves Amazon's catalog toward complete, correct, and consistent product information for hundreds of millions of customers worldwide.

Our Mission: Build the measurement, validation, feedback, and learning infrastructure that guides Amazon's catalog toward sustained excellence—empowering teams and selling partners with the tools, transparency, and intelligence needed to deliver trustworthy product information at unprecedented scale.

What We Do:

Measure catalog quality across hundreds of millions of products, providing metrics and insights that drive improvement
Validate enrichments and contributions before they enter the catalog, preventing defects at the source
Power feedback systems that give selling partners and internal teams rapid, transparent, actionable insights
Build learning infrastructure that enables continuous improvement through intelligent feedback loops and GenAI-driven insights
Provide federated frameworks that other teams leverage to build their own quality innovations
Our Technology Stack: We leverage GenAI technologies including Large Language Models (LLMs), multimodal AI systems, and autonomous agent architectures deployed on AWS infrastructure. Our systems span the full quality lifecycle: measurement, detection, AI-powered correction, quality evaluation guardrails, and seller communication platforms.

Our Impact:

For Customers: Accurate, complete, and consistent product information that increases purchase confidence
For Selling Partners: Transparency, self-service tools, and actionable recommendations to improve their catalog contributions
For Amazon: Preventing catalog defects, enabling better product discoverability, reducing operational costs, and driving revenue through higher-quality shopping experiences
Our Culture: We maintain a high bar for operational and software engineering excellence while fostering a collaborative and supportive environment. We work closely with applied scientists, engineers, product managers, category experts, and business stakeholders across Selling Partner Experiences, Category teams, and Relationship Processing teams. We partner closely with AI-driven enrichment teams to create a complete quality ecosystem—while they enrich, we ensure those enrichments are validated, measured, and continuously improved.