AI is only as smart as the business meaning behind the data it reasons over. Today, that meaning lives in spreadsheets, tribal knowledge, and the heads of a handful of experts, which is exactly why AI agents get the right numbers but the wrong answers. We're looking for an AI Engineer (Data and Ontology) to close that gap: someone who wants to build the semantic foundation that makes our business data truly AI-ready. This is not a traditional data pipeline role, because AI needs explicit, governed business meaning, not institutional memory. You will own that meaning layer: defining the entities, relationships, canonical metrics, and business rules that turn raw data into something both humans and AI agents can reason over with confidence. You'll combine hands-on technical range (pipelines, infrastructure-as-code, CI/CD, modern data platforms, and LLM/agent tooling) with sharp business translation skills, turning implicit tribal knowledge into explicit, machine-readable definitions that agents can act on safely. You will also set the guardrails: what data can be combined, which aggregations are valid, and who can access what, so AI can operate on business data responsibly instead of guessing. If you want to help define what the AI-native data engineer looks like, this is your opportunity. At Amazon, you'll work alongside the latest AI and GenAI tools that are increasingly woven into how teams operate: from AI-powered capabilities that accelerate decision-making, to Generative AI that helps you focus on work that truly matters. You'll have opportunities and resources to develop AI fluency at your own pace, with continuous learning built into the culture. Key job responsibilities - Design and own data models and ontologies for core business domains: entities, relationships, canonical metrics, valid dimensions, and business rules, interpretable by both humans and AI agents. - Apply the right data modeling approach for each problem, capturing complex business relationships that AI needs to reason over correctly. - Encode definitions and rules as governed, machine-readable artifacts (ontologies, glossaries, concept maps, embeddings) that close the accuracy gap between AI agents and domain-specific questions. - Build and operate the pipelines, orchestration, infrastructure-as-code, and CI/CD that implement and serve your models in production, stable, performant, and testable. - Partner with business owners and analysts to extract implicit domain logic into explicit, auditable ontology definitions. - Build tooling to track data lineage, monitor data quality, and catch definition drift before it erodes AI or human trust. - Evaluate emerging AI tooling (agents, semantic search, embeddings) to make the team's data models increasingly AI-consumable. - Own enhancements that improve the team's data and ontology processes, resolving root causes rather than symptoms. - Participate in design and model reviews, and train teammates on how the semantic layer is built and consumed by AI. About the team We are Knowledge and Data Tech, part of Japan Store Tech within Amazon Japan's Retail Business. We own the knowledge and data foundation that AI and business decisions are built on, turning scattered, tribal knowledge into a governed, shared semantic layer that people and AI agents can trust. We're at the earliest stage of this shift, so you won't just execute a roadmap, you'll help shape it, with a front-row seat of AI engineering.