Description

Over a million selling partners and 30,000 Amazonians make business decisions through the analytics foundation this Principal Engineer will own – across a $670B+ revenue ecosystem. This role is for the central data and analytics platform powering Amazon's selling partner experience, providing consistent, standardized access to thousands of business metrics across hundreds of slices and time periods through its curated data layer and analytics query engine, a metric governance catalog, and cross-domain analytics experiences serving both selling partners and internal Amazonians.

We are seeking a Principal Engineer to architect and lead the next generation of this platform – redesigning core infrastructure to support fundamentally new workloads including real-time agentic analytics, AI-native programmatic interfaces, and multi-tier query optimization at dramatically lower cost, all while maintaining zero-disruption continuity for mission-critical production systems that underpin 10+ applications with 99.99% availability requirements.

This is not greenfield work on a blank canvas, nor is it maintenance of a stable system. It is both simultaneously: rebuilding the engine while the plane is flying. The platform carries significant technical debt – a monolithic architecture with 300K+ lines of code accumulated over 6+ years on Redshift/Presto-based infrastructure – and is actively being modernized to serve an ecosystem that is rapidly shifting toward AI-assisted decision-making. The architectural choices made here will define how AI agents access and reason about Amazon's selling partner business data for years to come.

Three technical challenges define this role:

1) 10x the throughput at 1/10th the cost – on a live platform. You will redesign core query infrastructure to achieve 100K+ QPS while reducing cost-per-query to a 10th of current cost. The architecture must span three fundamentally different performance tiers – sub-100ms operational queries, sub-2.5s strategic planning queries, and bulk model-training workloads – each with its own optimization profile. The migration must be invisible to every downstream client.

2) Teaching AI agents to reason about business data, not just query it. Amazon is investing heavily in agentic analytics, and those agents need infrastructure that doesn't exist yet. You will design MCP-compliant endpoints, decision-scoped analytics agents, and knowledge bases that give AI systems semantic understanding of thousands of business metrics – bridging structured metric definitions with LLM consumption patterns. These are foundational choices with ecosystem-wide consequences.

3) One source of truth where there are currently many. Selling partners see conflicting metrics across Vendor Central, Seller Central, and Selling Partner APIs, which erodes trust and makes AI recommendations unreliable. You will drive consolidation under Ripple as the single governed platform, navigating competing priorities across multiple Amazon VP-level organizations while designing schemas flexible enough for cross-domain use cases – including joining order details with returns, inventory movements, advertising performance, and customer feedback at transaction grain – that no single team can solve alone.

This role requires a PE who operates across the full spectrum: writing critical-path code for foundational infrastructure, setting engineering standards through exemplary practice across a 40+ engineer team spanning four locations, and influencing senior technical stakeholders across various organizations to shape cross-Amazon architectural direction. Depth and breadth, in the same role, at consequential scale.


Key job responsibilities

• Architecture Strategy and Technical Vision: Own the end-to-end architecture strategy and three-year technical vision for the product portfolio spanning Data Services and Analytics. Ensure these systems evolve coherently – that Ripple's infrastructure serves analytics consumption patterns, that metric governance integrates seamlessly with the data model, and that the full stack works cohesively for both human decision-makers and AI agents.
• Platform Redesign and Hands-On Development: Architect and build V2 platform, designing and writing critical-path code for the new query engine, materialization framework, and multi-tier infrastructure (rapid-operational <100ms, strategic planning <2.5s, model training at-rest). Prototype and validate architectural hypotheses before committing the broader team to a direction. Establish foundational code patterns that 40+ engineers across US, Canada, and India will extend.
• Cost and Performance Optimization at Scale: Drive the platform from its current cost and throughput profile to 100K+ QPS through storage-compute separation, intelligent materialization strategies, and query optimization across petabyte-scale data. Design smart optimization capabilities that use AI to analyze query patterns and automatically suggest materializations.
• AI-Native Infrastructure and Agentic Analytics: Design MCP-compliant endpoints making the query engine natively compatible with AI agents. Architect knowledge bases with automatic updates from the authoritative metadata catalog, enabling AI systems to autonomously discover and correctly interpret business metrics. Build the foundation for decision-scoped analytics agents that selling partners and third-party developers can integrate into their own agentic workflows.
• Platform Unification and Data Governance: Drive consolidation of Vendor Central, Seller Central, and Selling Partner API analytics under the Curated Data Layer as the single source of truth with consistent metric definitions. Expand the Curated Data Layer to support operational and transactional domain data, enabling cross-domain report generation at transaction grain. Establish metric catalog as the authoritative governance registry for all business metric definitions across the selling partner ecosystem.
• Cross-Organizational Technical Leadership: Influence senior technical stakeholders (upto VP level) across multiple Amazon organizations to drive architectural alignment on decisions with lasting ecosystem consequences. Represent the platforms perspective in cross-organizational forums where foundational infrastructure choices about AI agent data access are being shaped.
• Engineering Excellence and Team Development: Ensure overall code quality and operational excellence across a diverse and evolving codebase (300K+ lines across 25+ packages). Set engineering standards through direct engagement – reviewing, refactoring, and leading by example as an exemplary practitioner. Mentor and develop senior engineers, including supporting Sr. SDEs on their path toward PE-level impact. Elevate technical discourse through design reviews, architecture reviews, and knowledge sharing across the distributed team.
• Operational Excellence: Maintain 99.99% availability for production-critical systems with visibility into $670B+ GMS while simultaneously driving architectural modernization. Ensure zero-disruption continuity during infrastructure transitions, managing legacy constraints and federated development complexity where partner teams contribute features and applications across multiple locations and organizations.