Sr Platform Engineer
Datavations · New York
About this role
**Sr Platform Engineer — Datavations** **About Datavations** Datavations is a leading New York-based data and AI software company serving the $2.3T 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—helping manufacturers make sharper decisions about pricing, distribution, and shelf presence. **About the role** Product taxonomy and attributes power every insight Datavations delivers. We’re looking for a senior 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 - **Attribute extraction engine**: LLM-based extraction at production scale, including configuration model, quality gates, and cost profile. - **Transformation layer**: dbt models that turn extracted values into published attributes—standardization and an override model that lets human judgment reliably beat the machine. Built on **ClickHouse** with the data platform team. - **Orchestration & reliability**: Dagster jobs, sensors, and schedules for taxonomy and attribute processing, including run monitoring, retries, alerting, and recovery tooling. Reliability is not separate here—it’s part of this seat. - **Applications**: Internal and customer-facing screens and services (not just backend). Includes internal platform for taxonomy/attributes and customer-facing views. **React/Next.js**, backend services/APIs, application architecture, deployment, and CI/CD. - **Write path**: Every change validated, logged, and visible before it ships, with approval where it matters. - **Data quality & observability**: Automated testing on the data plus statistical detection of quiet failures (outliers, drift, retailers that stop updating, values that flip between runs). - **Engineering standards**: Documentation, tests, runbooks, and review culture as the system and team scale. --- ## Building with AI (specifically) - **AI that’s core, not bolted on**: An LLM extraction engine already running at scale, a rules engine producing confidence scores per item, and an in-app assistant that answers questions in natural language and proposes changes for review. - **Evaluation before assertion**: Golden sets, regression suites triggered by prompt changes, and a defensible way to answer “did that make it better?” - **Prompts & rules as versioned data**: Stored, diffable, testable, auditable—with clear records of what changed and what it did. - **Tool-calling & MCP**: Exposing internal systems to agents safely (scoped, read-first, audited). - **Cost & 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 and not a rescue. You’ll own the taxonomy and attribute systems end to end, with the warehouse, ingestion, and shared infrastructure owned alongside you by the data platform team. --- ## Requirements - 5+ years in **Cloud Infrastructure**, **SRE**, or **Platform Engineering** - Strong expertise with **Terraform** and **IaC** - Experience with **CI/CD** (GitHub Actions, Jenkins, GitLab CI/CD, Ar
Listing freshness
CronJobs last confirmed this listing 2h ago. If its source stops confirming the opening for seven days, this page is removed from active inventory.