Architect - Data Engineer

Quantiphi is an applied AI and Big Data software and services company focused on solving transformational business problems through machine learning, cloud, and data engineering. The Architect Data Engineer will lead the architectural vision for a next-generation data layer for Agentic AI, designing schemas and hybrid database environments while serving as the primary technical liaison for 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: Define performance and observability standards to ensure low-latency, accurate, reliable data retrieval for real-time agentic AI workloads

Requirements:

• • Experience Level:**10+ yrs
• • 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
• • 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)