Snowflake Architect, Data Warehousing

Role Overview

We are looking for a Senior / Principal AI & Snowflake Architect to lead the design and implementation of next-generation AI and agentic analytics solutions on top of our Snowflake Data Cloud. Snowflake is our core data and analytics platform and will remain our primary system of recording and computing for data, analytics, and AI workloads.

In this role, you will be the hands-on technical leader who architects, builds, and optimizes large-scale data and AI solutionsfrom data ingestion and modeling, through orchestration and deployment, to monitoring and continuous improvement. You will partner closely with data engineering, application engineering, and product teams in a large SaaS product environment to deliver production-grade, scalable, and cost-efficient AI capabilities for our customers and internal users.

Key Responsibilities

Snowflake-Centric Data & AI Architecture-

• Architect and deliver end-to-end solutions on Snowflake, ensuring Snowflake remains the authoritative data platform and primary compute layer for analytics and AI/ML workloads.
• Design and optimize large-scale data models (dimensional, data vault, wide tables, feature tables) for analytical and AI/ML use cases.
• Lead the design and implementation of high-throughput ELT/ETL pipelines into Snowflake using native ingestion and transformation capabilities (e.g., Snowpipe, COPY INTO, Streams & Tasks), with a strong focus on performance, reliability, and cost.
• Apply deep expertise in Snowflake architecture (compute, storage, security, data sharing, multi-cluster, workload isolation) to meet SLAs, concurrency, and governance needs.
• Design and implement scalable AI agents and agentic workflows that operate directly on Snowflake data, avoiding parallel/fragmented AI stacks that bypass Snowflake.
• Build and evolve Natural Language Query (NLQ) capabilities over analytical data on Snowflake, including semantic layers, prompt engineering, and guardrails.
• Architect and implement RAG (Retrieval-Augmented Generation) solutions using Snowflake-native features, integrating:
• Snowflake Cortex (e.g., Cortex Analyst, Cortex Search, Cortex Code) for conversational analytics, semantic search, and code generation.
• Snowflake AI, Snowpark, and Snowflake Notebooks for building, training, and deploying ML models and custom logic close to the data.
• Embeddings-based search, recommendation, and personalization pipelines backed by Snowflake as the vector and feature store.
• Design human-in-the-loop workflows for review, feedback, and governance around NLQ responses, recommendations, and AI-generated insights, ensuring transparency, auditability, and continuous improvement.
  
  

End-to-End Solution Ownership-

• Own solution design from data ingestion through modeling, orchestration, deployment, and monitoring of AI/ML and agentic workflows on Snowflake.
• Define and implement patterns for production-grade scalability, reliability, observability, and cost optimization across Snowflake and AI components.
• Set and manage SLOs/SLAs for latency, throughput, and availability.
• Implement monitoring, logging, alerting, and tracing for Snowflake workloads and AI/ML services.
• Collaborate with data engineering to design robust data contracts, SLAs, and lineage for AI features and NLQ/RAG systems.
• Partner with application engineering and product teams to integrate Snowflake-backed AI capabilities into user-facing SaaS products and internal tools.
• Champion responsible and safe use of AI, including bias detection, content safety, and user controls/guardrails in AI agents and NLQ/RAG systems.
  
  

Required Skills & Qualifications

• Bachelor's/master's in computer science, IT, or related field.
• 8+ years of Snowflake Data Cloud experience.
• Deep understanding of Snowflake architecture and ecosystem.
• Strong grasp of data warehousing concepts and diverse data structures.
• Expertise in Snowflake native load utilities.
• API integration and automation experience.
• Data governance, security, and compliance background.
• Performance optimization and cost management skills.
• Experience with Snowflake infrastructure management.
• Excellent communication and stakeholder management.
• Innovative, AI-driven mindset.
• Hands-on Python for data engineering/automation.
• Knowledge of AI, LLMs, and Snowflake AI features (e.g., Cortex).
• Experience with data lake frameworks and serverless technologies.