Architect Data Engineer

About the role

Lead the architectural vision for a next-generation data layer designed specifically for Agentic AI. You will define high-performance schemas, orchestrate complex hybrid-database environments (Snowflake/Kinetica/NoSQL), and serve as the primary technical liaison for our customers.

Responsibilities

• System Architecture: Design the end-to-end blueprint for a modern data layer that seamlessly integrates structured, unstructured, and relational (Graph) data for AI agents.
• Schema & Ontology Design: Define multi-tenant schemas and Knowledge Graph ontologies that allow LLM agents to perform complex reasoning and cross-domain data retrieval.
• Database Administration & Governance: Oversee the health, security, and performance optimization of our data clusters (Snowflake/Kinetica), ensuring 99.9% availability for mission-critical AI workflows.
• Strategic Client Fronting: Act as the "Face of Engineering" for the customer. Lead discovery workshops, manage technical expectations, and align the architectural roadmap with their business objectives.
• Performance Engineering: Establish benchmarks for data latency and retrieval accuracy, ensuring the data layer can keep pace with the real-time demands of agentic execution.

Qualifications

• System Architecture: Design the end-to-end blueprint for a modern data layer that seamlessly integrates structured, unstructured, and relational (Graph) data for AI agents.
• Schema & Ontology Design: Define multi-tenant schemas and Knowledge Graph ontologies that allow LLM agents to perform complex reasoning and cross-domain data retrieval.
• Database Administration & Governance: Oversee the health, security, and performance optimization of our data clusters (Snowflake/Kinetica), ensuring 99.9% availability for mission-critical AI workflows.
• Strategic Client Fronting: Act as the "Face of Engineering" for the customer. Lead discovery workshops, manage technical expectations, and align the architectural roadmap with their business objectives.
• Performance Engineering: Establish benchmarks for data latency and retrieval accuracy, ensuring the data layer can keep pace with the real-time demands of agentic execution.
• The Hybrid Stack: Proven expertise in architecting for Snowflake (Data Cloud) and Kinetica (Real-time/Vector/OLAP).
• Knowledge Graph Mastery: Ability to design Property Graphs or RDF schemas that map enterprise entities into a machine-readable "World Model."
• Advanced ETL/ELT Strategy: Deep knowledge of data orchestration patterns (Change Data Capture, Streaming, and Batch) to ensure data freshness.
• Database Internals: Strong DBA skills—partitioning strategies, indexing, vacuuming, and resource scaling in cloud-native environments.
• Good To Have (The “Agentic” Edge): Semantic Layer Design: Experience with tools like Cube or dbt Semantic Layer to provide a consistent "Language" for AI agents to query.
• Security & Privacy: Knowledge of RBAC and Row-Level Security (RLS) within an AI context—ensuring agents only "see" what they are authorized to access.
• Tooling for Agents: Experience designing API-first data layers that agents can use as "Tools" (e.g., function calling).

Pay

TBD

Schedule

Remote