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Data Engineer AI-Ready Data Foundation, Japan Store Tech
Job Location:
Monthly Salary:
Posted:
22 August 2026 (7 days ago)
Posted:
Application Deadline:
19 November 2026
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Department:
Job Summary
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. Were looking for a Data Engineer 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.
Youll 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 youll 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. Youll 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 teams data models increasingly AI-consumable.
- Own enhancements that improve the teams 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 Japans 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. Were at the earliest stage of this shift so you wont just execute a roadmap youll help shape it with a front-row seat as data engineering gets redefined by AI.
- 3 years of data engineering experience
- 3 years of data modeling experience
- 3 years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience
- Experience with AWS technologies like Redshift S3 AWS Glue EMR Kinesis FireHose Lambda and IAM roles and permissions
- Experience with non-relational databases / data stores (object storage document or key-value stores graph databases column-family databases)
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process including support for the interview or onboarding process please visit for more information. If the country/region youre applying in isnt listed please contact your Recruiting Partner.
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.
Youll 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 youll 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. Youll 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 teams data models increasingly AI-consumable.
- Own enhancements that improve the teams 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 Japans 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. Were at the earliest stage of this shift so you wont just execute a roadmap youll help shape it with a front-row seat as data engineering gets redefined by AI.
- 3 years of data engineering experience
- 3 years of data modeling experience
- 3 years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience
- Experience with AWS technologies like Redshift S3 AWS Glue EMR Kinesis FireHose Lambda and IAM roles and permissions
- Experience with non-relational databases / data stores (object storage document or key-value stores graph databases column-family databases)
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process including support for the interview or onboarding process please visit for more information. If the country/region youre applying in isnt listed please contact your Recruiting Partner.
Required Experience:
IC
About Company
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