What if AI could actually understand your data -- not just query rows, but reason about what the numbers mean? That's the infrastructure we're building, and we need a data engineer to help us scale it.
Join a tight-knit team of engineers building the data and application stack that powers AI-driven analytics for training operations across one of the largest fulfillment networks in the world. You'll design cloud data pipelines that process millions of daily records, ship full-stack web applications that business users touch every day, and help build the semantic layer that lets AI agents reason over operational data. We work directly with the leaders who run the operation -- translating their expertise into pipelines, tables, and tools that make the business faster, smarter, and more measurable.
If you love SQL, care about software craft, and want to be part of shaping what AI-native data infrastructure looks like at real enterprise scale, keep reading.
In this role you will:
- Design, build, and operate production SQL pipelines in a cloud data warehouse
- Model entity schemas, define metric formulas, and own data lineage end to end
- Ship features across backend services and frontend UI for the tools our team builds
- Contribute to a growing semantic layer that encodes business context in a form AI agents can consume
- Partner directly with business stakeholders to turn their operational expertise into structured, machine-readable definitions
- Own the health, alerting, and access controls for what you ship
Key job responsibilities
- Design, build, and operate production SQL data pipelines in a cloud data warehouse, processing large volumes of daily operational data from multiple upstream sources with strong reliability, freshness, and observability
- Model entity schemas, define metric formulas, and own data lineage end to end, from raw source through transformation to consumption, so downstream consumers and AI agents can trust every number
- Contribute to full-stack web applications used by business teams, shipping features across both backend services and frontend UI to make data captureable, reviewable, and actionable at the source
- Extend the team's semantic layer (version-controlled definitions of metrics, entities, relationships, and business context) so operational knowledge lives in code and can be consumed by AI agents and LLM-based tools
- Partner directly with business stakeholders to translate their domain expertise and institutional knowledge into structured, machine-readable definitions and tables
- Own the operational health of what you build: production alerts, on-call, ticket intake, access controls (including row-level security), and pipeline observability
- Continuously look for opportunities to reduce manual analysis by moving the platform toward natural language interfaces, richer entity relationships, and more autonomous investigation
- Explore emerging techniques in semantic data modeling, knowledge representation, retrieval, and prompt engineering to keep improving how AI systems reason over our data
- Write the design docs, scope documents, and runbooks that make the team's work reviewable, maintainable, and durable
