Sr. Systems Engineer
Datavations · New York
About this role
**About Datavations** Datavations is a leading New York-based data and AI software company focused on 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 internal teams and customers to interact with it. This is a hands-on role with significant architectural latitude. **What you’ll own** - **Attribute extraction engine**: LLM-based extraction at production scale, including configuration, quality gates, and cost profile. - **Transformation layer**: dbt models that turn extracted values into published attributes (standardization + an override model so human judgment can reliably beat the machine). Built on **ClickHouse** alongside the data platform team. - **Orchestration & reliability**: Dagster jobs, sensors, and schedules for taxonomy/attribute processing—run monitoring, retries, alerting, and recovery tooling. Reliability is part of this seat. - **Applications**: Internal and customer-facing screens (not just services). React/Next.js front end, backend services/APIs, application architecture, deployment, and CI/CD. - **Write path**: Every change validated, logged, and visible before shipping, with approval where it matters. - **Data quality & observability**: Automated testing on the data plus statistical detection of silent failures (outliers, drift, retailer update gaps, value flips between runs). - **Engineering standards**: Documentation, tests, runbooks, and review culture as the system/team scale. **Building with AI (specifically)** - **Proper AI systems**: 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 when prompts change, and measurable quality improvements. - **Prompts & rules as versioned data**: Stored, diffable, testable, auditable—with clear records of what changed and what it did. - **Tool-calling & MCP**: Safely exposing internal systems to agents (scoped, read-first, audited). - **Cost & latency as design constraints**: Model tiering, caching, batching, circuit breakers—knowing run cost before it runs. **What you’ll walk into** A modernization already in motion (not greenfield, not rescue). You’ll own the taxonomy and attribute systems end-to-end, while the warehouse, ingestion, and shared infrastructure are owned alongside you by the data platform team. There’s a detailed technical map, engaged leadership, a modern monorepo, and a new internal platform in its first release. **Requirements** - 5+ years in Cloud Infrastructure, SRE, or Platform Engineering - Strong expertise with **Terraform** / IaC - Experience with **CI/CD** (GitHub Actions, Jenkins, GitLab CI/CD, ArgoCD) - Strong expertise architecting solutions using **AWS** - Experience with **Python, SQL, dbt**, and modern orchestration tools (e.g., **Dagster, Airflow, Prefect**) - Proficiency with a columnar warehouse
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