Data Architecture Lead

Snowflake • Menlo Park, CA

2d • Onsite

About The Position

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.
Founded by industry experts and backed by strategic investors, our disruptive built-for-the-cloud architecture was designed to push the limitations of conventional data warehousing. Our teams breed ambition, challenge ordinary thinking, and push the pace of innovation in service of the exploding demand for accessible data.
Snowflake is growing fast and we're scaling our team to help enable and accelerate our growth. We're passionate about our people, our customers, our values, and our culture! We're also looking for people with a growth mindset and the pragmatic insight to solve for today while building for the future. And as a Snowflake employee, you will be accountable for supporting and enabling diversity and belonging.
Snowflake started with a clear vision: make modern data warehousing effective, affordable, and accessible to all data users. Because traditional on-premises and cloud solutions struggle with this, Snowflake developed an innovative product with a new built-for-the-cloud architecture that combines the power of data warehousing, the flexibility of big data platforms, and the elasticity of the cloud at a fraction of the cost of traditional solutions.
We are looking for a Data Architecture Lead to join our Data, Analytics, and AI organization. This is a senior individual contributor role with principal-level scope. You will set the technical direction for how our data and analytics engineering teams build, govern, and evolve the data platform — and you'll be a hands-on practitioner who shapes what's possible.
This role an in office requirement - you must be able to work out of our Menlo Park office for a minimum of 3 days a week.

Requirements

• 8+ years of experience in data engineering, analytics engineering, or data architecture roles
• Deep expertise in Snowflake, including advanced data modeling, RBAC design, performance optimization, and the broader Snowflake feature set (Cortex, Dynamic Tables, Streams, Tasks, etc.)
• Expert-level dbt skills (dbt Core and/or dbt Platform), including macro development, testing frameworks, CI/CD integration, and large-scale project management
• Hands-on experience with Airflow or similar orchestration platforms for data pipeline management
• Strong proficiency with AI development tools, including experience with LLM-powered workflows, agent frameworks (e.g., Cortex Agents, Claude Code, Cortex Code), and prompt engineering for data use cases
• Experience designing and implementing RBAC frameworks at scale — including row access policies, masking policies, and role hierarchies in Snowflake or similar platforms
• Excellent written and verbal communication skills, with a track record of writing clear technical specs, architecture documents, and stakeholder-facing materials
• Ability to operate with principal-level scope: driving cross-functional initiatives, influencing without authority, and delivering outcomes across multiple teams

Nice To Haves

• Experience working at a technology company with a large-scale internal data platform
• Prior experience building or contributing to internal developer tooling, shared libraries, or platform-as-a-product initiatives
• Familiarity with Python for data pipeline development, scripting, and automation
• Experience presenting technical content to external audiences (customers, conferences, etc.)

Responsibilities

• Define and deploy architectural standards across analytics and data engineering teams, covering data modeling patterns in dbt, RBAC frameworks, deployment workflows, and code quality standards
• Build and deploy AI-powered tooling and skills — including agent frameworks, prompt-driven workflows, and LLM-enabled automation — for use across analytics and data engineering teams
• Elevate the RBAC framework for the data platform, including designing and implementing row access policies, column masking policies, and role-based access control patterns at scale in Snowflake
• Partner with Snowflake's product and engineering teams on feature testing and feedback — write meaningful product specifications, develop proof-of-concepts, and help push the Snowflake platform forward
• Represent Snowflake's internal data capabilities externally, meeting with customers to demonstrate what is possible on the platform and sharing real-world architectural patterns and learnings
• Act as a strategic lever across teams — bridging data engineering and analytics engineering to drive alignment, reduce redundancy, and raise the technical bar organization-wide
• Enable the team through documentation, knowledge sharing, and mentorship — setting standards that scale beyond your own direct contributions

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed
• Number of Employees: 1,001-5,000 employees