DataEngineerII

What if AI could query your data warehouse and actually understand what the numbers mean, not just return rows? That's the infrastructure we're building, and we need a data engineer to help us scale it. We've built a semantic layer that sits between raw operational data and AI agents, encoding metric definitions, business logic, entity relationships, data lineage, and query routing into structured knowledge that large language models can consume and reason about. The foundation is in place. Now we need someone to deepen the data models, expand entity coverage, enrich the ontology with causal relationships, and build the pipeline infrastructure that keeps it all fresh and accurate at scale. In this role, you'll design and maintain the data infrastructure that powers AI-driven analytics for workforce Learning across Amazon's fulfillment network. That means building SQL pipelines in Redshift that process millions of daily records from nine upstream platforms, defining entity schemas with join keys, primary keys, and PII classifications, writing metric definitions with traceable formulas grounded in actual ETL logic, and modeling granularity levels that tell AI agents whether to query at the associate, site, or network level. You'll own the full stack from raw ingestion through transformation to semantic enrichment. You'll also work directly with business stakeholders to translate their domain expertise into structured metadata. When a Regional Learning Manager explains that "training compliance resets weekly on Sunday" or "this site type structurally can't meet that threshold," you'll encode that context into the semantic layer so AI agents handle it correctly without human intervention. Over time, you'll push this toward a world model: not just what metrics exist, but how they relate causally, what drives them, and what happens when they change. We're looking for someone who thinks about data infrastructure as more than pipelines and tables. You'll work with knowledge