Architect - Data Engineer

Quantiphi is an award-winning Applied AI and Big Data software and services company, driven by a deep desire to solve transformational problems at the heart of businesses. The Architect - Data Engineer role involves leading the architectural vision for a next-generation data layer designed for Agentic AI, defining high-performance schemas, and 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:

• 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)