Solutions Architect

Customer Engagement & Advisory

• Directly engage with customers individually or as part of a technical delivery team as a trusted advisor for thought leadership and solution approaches
• Extract business requirements from customers and translate them into a well-architected recommendation
• Develop and provide forward-looking guidance and future-state roadmaps
• Leverage known best practices and solutions to reduce risk to customer implementations
• Collaborate with Snowflake Go to Market teams and product teams to ensure customer alignment to Snowflake goals

Technical Leadership & Architecture

• Oversee and advise on the implementation of the customer’s Snowflake platform
• Demonstrate technical leadership through personally developed code, solution architectures, and implementation recommendations

AI/ML Solutions & ML Ops

• Design, prototype, and implement end-to-end AI/ML solutions on the Snowflake platform, ensuring scalability and performance
• Provide customers with standard practices and advice related to AI/ML workloads in Snowflake
• Define and advise on MLOps standard practices for model deployment, monitoring, and governance within the customer’s Snowflake environment

Product Adoption

• Drive the adoption of Snowflake’s AI product suite (e.g., Cortex, Streamlit in Snowflake, Snowflake Intelligence) within customer solutions
• Experience replatforming AI/ML workloads to Snowflake in line with business and operational requirements
• Coach and guide customers on how to best leverage Snowflake’s AI product suite to unlock new business value

Qualifications

• BA/BS in computer science, engineering, mathematics or related fields, or equivalent practical experience
• 3 years of technical consulting experience
• Experience in a lead role for analytics projects for large enterprises
• Experience implementing and operating Snowflake centric solutions
• Understanding of complete data analytics stack and workflow, from ETL to data platform design to BI and analytics tools
• Deep understanding of the complete AI/ML lifecycle, from data preparation and feature engineering to model training, deployment, and ongoing monitoring
• Familiarity with AI/ML and Transformation technologies
• Hands-on experience with modern MLOps tools and frameworks for deploying, managing, and governing machine learning models at scale
• Proven experience with developing generative AI and LLM use cases
• Familiarity with industry common BI and ETL solutions