Snowflake Architect, Data Warehousing

As a Senior / Principal AI & Snowflake Architect, your role will involve leading the design and implementation of next-generation AI and agentic analytics solutions on the Snowflake Data Cloud. Your primary responsibility will be to architect, build, and optimize large-scale data and AI solutions across various stages, from data ingestion to monitoring and continuous improvement. You will collaborate closely with data engineering, application engineering, and product teams in a SaaS product environment to deliver scalable and cost-efficient AI capabilities for customers and internal users. Key Responsibilities: - Architect end-to-end solutions on Snowflake, focusing on maintaining Snowflake as the authoritative data platform for analytics and AI/ML workloads. - Design and optimize large-scale data models for analytical and AI/ML use cases. - Lead the design and implementation of ELT/ETL pipelines into Snowflake with a strong emphasis on performance, reliability, and cost. - Apply expertise in Snowflake architecture to meet SLAs, concurrency, and governance needs. - Design and implement scalable AI agents and workflows operating directly on Snowflake data. - Implement Natural Language Query (NLQ) capabilities over analytical data on Snowflake. - Architect and implement RAG solutions using Snowflake-native features for conversational analytics, ML model building, and personalized pipelines. - Design human-in-the-loop workflows for review, feedback, and governance around NLQ responses and AI-generated insights. End-to-End Solution Ownership: - Own solution design from data ingestion to monitoring of AI/ML and agentic workflows on Snowflake. - Define patterns for scalability, reliability, observability, and cost optimization across Snowflake and AI components. - Set and manage SLOs/SLAs for latency, throughput, and availability. - Implement monitoring, logging, and alerting for Snowflake workloads and AI/ML services. - Collaborate with data engineering to design robust data contracts and SLAs for AI features. - Partner with application engineering to integrate Snowflake-backed AI capabilities into user-facing products. 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 and API integration. - Experience in data governance, security, and compliance. - Skills in performance optimization and cost management. - Knowledge of Snowflake infrastructure management and hands-on Python for data engineering. - Familiarity with AI, LLMs, Snowflake AI features, and data lake frameworks. As a Senior / Principal AI & Snowflake Architect, your role will involve leading the design and implementation of next-generation AI and agentic analytics solutions on the Snowflake Data Cloud. Your primary responsibility will be to architect, build, and optimize large-scale data and AI solutions across various stages, from data ingestion to monitoring and continuous improvement. You will collaborate closely with data engineering, application engineering, and product teams in a SaaS product environment to deliver scalable and cost-efficient AI capabilities for customers and internal users. Key Responsibilities: - Architect end-to-end solutions on Snowflake, focusing on maintaining Snowflake as the authoritative data platform for analytics and AI/ML workloads. - Design and optimize large-scale data models for analytical and AI/ML use cases. - Lead the design and implementation of ELT/ETL pipelines into Snowflake with a strong emphasis on performance, reliability, and cost. - Apply expertise in Snowflake architecture to meet SLAs, concurrency, and governance needs. - Design and implement scalable AI agents and workflows operating directly on Snowflake data. - Implement Natural Language Query (NLQ) capabilities over analytical data on Snowflake. - Architect and implement RAG solutions using Snowflake-native features for conversational analytics, ML model building, and personalized pipelines. - Design human-in-the-loop workflows for review, feedback, and governance around NLQ responses and AI-generated insights. End-to-End Solution Ownership: - Own solution design from data ingestion to monitoring of AI/ML and agentic workflows on Snowflake. - Define patterns for scalability, reliability, observability, and cost optimization across Snowflake and AI components. - Set and manage SLOs/SLAs for latency, throughput, and availability. - Implement monitoring, logging, and alerting for Snowflake workloads and AI/ML services. - Collaborate with data engineering to design robust data contracts and SLAs for AI features. - Partner with application engineering to integrate Snowflake-backed AI capabilities into user-facing products. Required Skills & Qualifications: - Bachelor's/mast