Product Manager Technical - Business Data Technologies, Business Data Technologies

Data Intelligence Agent Product Manager

Amazon employees need faster access to relevant data for decision-making. Currently, finding the right datasets among thousands of options requires technical expertise, and converting business questions into actionable insights takes significant time. Many employees rely on data analysts for routine queries, creating bottlenecks that slow business decisions.

The Data Intelligence Agent is Amazon's AI-powered data intelligence platform that enables employees to ask business questions in natural language and receive accurate insights without SQL knowledge. Search is the intelligent discovery engine that powers the Data Intelligence Agent's dataset recommendations, understanding organizational context, user roles, and team patterns to surface relevant data sources. Together, Search and the Data Intelligence Agent transform how Amazon employees interact with data, moving from technical barriers to conversational intelligence that enables faster, data-driven decisions.

Key Job Responsibilities

We are seeking a Product Manager Technical to drive product strategy and execution for Search and Data Intelligence Agent platforms. You will own the product vision for how Amazon employees discover and interact with data, working across engineering, machine learning, and design teams to deliver AI-powered capabilities that scale across diverse business contexts.

In this role, you will help transform how Amazon employees access and use data for decision-making. Your work will directly contribute to faster business decisions, improved operational efficiency, and Amazon's continued innovation through better data utilization.

• Product Strategy: Own 18-month roadmap for Search and Data Intelligence Agent platforms, balancing user needs, technical feasibility, and business impact
• Search Intelligence: Drive requirements for dataset recommendations based on organizational context, user roles, and team usage patterns
• Conversational AI: Define user experience for natural language data queries, including multi-turn conversations and query refinement
• Performance Optimization: Collaborate with engineering teams to achieve sub-second response times through caching, optimization, and intelligent routing
• Cross-functional Leadership: Work with engineering, ML, design, and business teams to translate requirements into product specifications
• User Research: Conduct studies to understand data discovery workflows and feature priorities across different roles and teams
• Metrics & Analytics: Define success metrics including user adoption, query success rates, and time-to-insight improvements
• Stakeholder Management: Engage with business teams to understand use cases, gather feedback, and drive platform adoption