Data Architecture Lead

Responsibilities

• Define and deploy architectural standards across analytics and data engineering teams, covering data modeling patterns in dbt, RBAC frameworks, and deployment workflows
• Build and deploy AI-powered tooling and skills, including agent frameworks and LLM-enabled automation
• Elevate the RBAC framework for the data platform, including designing row access and column masking policies at scale
• Partner with product and engineering teams on feature testing, writing product specifications, and developing proof-of-concepts
• Represent Snowflake's internal data capabilities externally by meeting with customers and sharing architectural patterns
• Bridge data engineering and analytics engineering to drive alignment and reduce redundancy
• Enable the team through documentation, knowledge sharing, and mentorship

Requirements

• 8+ years of experience in data engineering, analytics engineering, or data architecture
• Deep expertise in Snowflake, including advanced data modeling, performance optimization, and RBAC design
• Expert-level dbt skills, including macro development and CI/CD integration
• Hands-on experience with Airflow or similar orchestration platforms
• Strong proficiency with AI development tools, LLM-powered workflows, and agent frameworks
• Experience designing and implementing RBAC frameworks at scale
• Excellent written and verbal communication skills for technical specifications and stakeholder management
• Ability to drive cross-functional initiatives at a principal level

Preferred Qualifications

• Experience working at a technology company with a large-scale internal data platform
• Prior experience building internal developer tooling or platform-as-a-product initiatives
• Familiarity with Python for data pipeline development and automation
• Experience presenting technical content to external audiences

About the Company

Snowflake is a disruptive built-for-the-cloud architecture designed to push the limitations of conventional data warehousing, combining the power of data warehousing, the flexibility of big data platforms, and the elasticity of the cloud.