Sr. Enterprise Data & AI Architect
Listed on 2026-05-16
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IT/Tech
AI Engineer (Applied/Software), Data Engineering, Cloud Computing
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.
Snowflake empowers enterprises and people to reach their full potential. Our culture is centered on impact, innovation, and collaboration
, creating a place where you can build big, move fast, and grow your career.
We’re looking for a Sr Enterprise Data & AI Architect to join a specialized team that guides our largest customers through complex data and AI transformations. Reporting to the Director of AFE Architecture
, this person will:
- Shape enterprise data and AI architectures across AWS, Azure, GCP, and the Snowflake Data Cloud
- Provide hands-on technical leadership in Enterprise Data Platforms, Enabling a Data Driven Organizational Culture, AI/ML
- Advise C‑level executives (e.g. CTO, CDO, VP Engineering) on strategic architecture decisions and investment choices.
This role blends deep hands on technical expertise with executive presence
. You should be able to design sophisticated AI/ML solutions and comfortably influence technology strategy at Fortune 500 organizations.
- Be part of a specialized team scaling thought leadership and driving direct complex, architecture‑level customer engagements (L400/L500) across the entire Snowflake sales organization
- Define and drive cloud integration strategies across major cloud providers and key ecosystem partners.
- Oversee and build proofs of concept, migrations, and modernization programs, providing hands‑on technical guidance.
- Create reusable assets (presentations, reference architectures, and implementation guides).
- Design and review modern AI/ML architectures on Snowflake, including data platforms, pipelines, and production applications.
- Guide customers in moving from legacy to cloud‑native, AI‑ready architectures.
- Architect solutions leveraging Snowflake Cortex, ML capabilities, and integrations with leading AI frameworks.
- Ensure secure, governed AI/ML deployments, including LLM safety, data privacy, and compliance.
- Define MLOps/LLMOps and RAG/agentic patterns for training, deployment, and monitoring at scale.
- Advise on build vs. buy decisions and cost models for training, inference, and managed AI services.
- Develop a deep understanding of competitive cloud data and AI platforms and how Snowflake differentiates.
- Build competitive playbooks, battle cards, and guidance for field teams.
- Partner with Product and Engineering to inform a roadmap based on customer and competitive insights.
- Act as a trusted advisor to senior technology and business executives on data and AI transformation.
- Lead high‑stakes customer reviews where architecture choices have major strategic and financial impact.
- Represent Snowflake at industry events, executive briefings, and strategic forums.
- Help customers shape AI investment strategies using ROI frameworks and TCO/business case analysis.
- Mentor, and develop a world‑class enterprise architecture practice across the broader solution engineering organizations
- Drive knowledge sharing and continuous learning within the team and across Sales, Customer Success, and Product.
- 15+ years in enterprise architecture, solutions engineering, or similar technical leadership roles, with increasing scope.
- Deep hands‑on AI/ML experience, including LLMs, ML pipelines, and AI application architectures.
- Expert‑level knowledge of cloud data platforms (e.g., Snowflake, Databricks, AWS, Azure, GCP)…
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