Sr. Full-Stack Engineer, Data Systems
Datavations · New York
About this role
**About Datavations** Datavations is a leading New York-based data and AI software company specializing in the $2.3 trillion building materials industry. We provide building materials and home improvement manufacturers real-time, store-level visibility into pricing, assortment, and inventory across major retailers—including Home Depot, Lowe’s, and Menards—so manufacturers can make sharper decisions about pricing, distribution, and shelf presence. **About the role** Product taxonomy and attributes are the foundation of every insight Datavations delivers. We’re looking for an engineer to own this domain end-to-end—driving both the data systems that organize the market and the applications that enable teams and customers to interact with it. This is a hands-on role with significant architectural latitude for someone who wants full ownership of a system that already powers the business. **What you’ll own** - **The attribute extraction engine**: LLM-based extraction at production scale, including its configuration model, quality gates, and cost profile. - **The transformation layer for this domain**: dbt models that turn extracted values into published attributes (standardization) and an override model that lets human judgment reliably beat the machine. Built on our **ClickHouse** warehouse alongside the data platform team. - **Orchestration and reliability**: Dagster jobs, sensors, and schedules for taxonomy and attribute processing, including run monitoring, retries, alerting, and recovery tooling. Reliability is not a separate team here—**it’s this seat**. - **The applications**: internal and customer-facing. You own the screens people use (not just the services behind them): the internal platform for taxonomy/attributes and customer-facing views. **React/Next.js**, backend services/APIs, application architecture, deployment, and CI/CD. - **The write path**: every change validated, logged, and visible before it ships, with approval where it matters. - **Data quality and observability**: automated testing on the data itself, plus statistical detection of quiet failures (outliers, drift, retailers that stop updating, values that flip between runs). - **Engineering standards for the domain**: documentation, tests, runbooks, and review culture as the system and team scale. **Building with AI (specifically)** - AI is core, not bolted on: an LLM extraction engine running at scale, a rules engine producing a confidence score per item, and an in-app assistant that answers natural-language questions and proposes changes for review. - **Evaluation before assertion**: golden sets, regression suites when prompts change, and defensible answers to “did that make it better?” Quality you can measure. - **Prompts and rules as versioned data**: stored, diffable, testable, auditable—with a clear record of what changed and what it did. - **Tool-calling and MCP**: exposing internal systems to agents safely (scoped, read-first, audited). - **Cost and latency as design constraints**: model tiering, caching, batching, circuit breakers—knowing what a run costs before it runs. **What you’ll walk into** A modernization already in motion (not greenfield, not rescue): a production system that has grown fast with the business, a detailed technical map, engaged leadership, a modern monorepo, and a new internal platform in its first release—with full air cover to do it properly. You own the taxonomy and attribute systems end to end; the warehouse, ingestion, and sha
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