Lead Consultant - Snowflake and Cortex AI Engineer

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Skills you'll use

Who can apply

• Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field, or equivalent professional experience.
• Experience: 6–10+ years in Data Engineering, MLOps, or ML Platform roles, with a strong record of architecting and deploying ML solutions at scale. At least 3+ years building advanced data science or large-scale analytics solutions specifically on Snowflake.
• Snowflake Expertise: Deep expertise in Snowflake, including Snowpark (Python & Scala), Snowpark Container Services, Dynamic Tables, Streams, Tasks, Snowpipe, Snowflake Model Registry, UDFs/UDTFs/Stored Procedures, event tables, and performance optimization. Ability to architect Snowflake-native ML platforms without depending on external compute engines.
• Cortex AI Proficiency: Strong proficiency with Snowflake Cortex AI services, including Cortex LLM functions, Cortex Analyst, Cortex Search, Cortex Fine-Tuning, and embedding generation. Solid grasp of the process involved in developing and deploying production-grade AI applications fully within the Snowflake ecosystem.
• Programming: Proficiency in Python and SQL, with familiarity in TypeScript/JavaScript or a systems language such as Go or Rust. Deep experience with TDD, CI/CD pipelines, and code quality standards in a Snowflake-centric workflow.
• CI/CD & Infrastructure: Experience with CI/CD tools like GitHub Actions or Azure DevOps, containerization with Docker for Snowpark Containers, and Snowflake infrastructure-as-code approaches using Terraform, Schemachange, or SnowCLI.
• ML Tools: Practical knowledge of Snowflake Model Registry, experiment tracking, and model serving, supported by experience with external tools like MLflow or SageMaker for hybrid scenarios.
• AI/Agent Tools: Hands-on experience with AI coding tools such as Claude Code, GitHub Copilot, Cursor, or equivalent, alongside Cortex AI functioning as the main LLM serving platform. Working understanding of how LLMs reason about code and strong prompt engineering rigor.
• Security & Compliance: Solid understanding of healthcare data privacy and security within Snowflake, including RBAC, row access policies, dynamic data masking, network policies, secrets management, and audit controls.
• Systems Thinking & Architecture: Practical experience designing scalable interactions across traditional ML infrastructure and AI systems, with a preference for Snowflake-native solutions that minimize data movement and strengthen governance.

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