Senior Security Architect, Applied Field Engineering (afe)

Snowflake Snowflake · Data AI · CA-Menlo Park, United States · Solution Engineering

Senior Security Architect focused on building secure foundations for enterprise AI adoption, specifically agentic AI frameworks and leveraging Snowflake Cortex. The role involves architecting secure AI solutions, implementing governance, and ensuring data privacy for AI applications, while also contributing to modern security operations and influencing product roadmaps.

What you'd actually do

  1. Drive strategic engagements focused on the Security Architecture, ensuring robust foundations across Identity, Data, and Infrastructure for applications built on Snowflake.
  2. Support customer strategy for secure AI adoption, leveraging Snowflake Cortex to bring state-of-the-art LLMs directly to customer data within a secure environment.
  3. Deliver workshops and hands-on engagements to transition customers from legacy infrastructure to advanced SIEM Augmentation, Log Ingestion (Otel/Logs into Snowflake), and Cybersecurity Data Lake.
  4. Build repeatable reference architectures and frameworks for Identity (IAM), Data Governance, Row-Level Security, and Encryption to accelerate well-architected deployments.
  5. Guide customers through end-to-end journeys for BC/DR (Business Continuity/Disaster Recovery), including multi-region deployment patterns and measurable recovery outcomes.

Skills

Required

  • 5+ years of industry experience in Data, Security, Networking, Infrastructure or AI Engineering
  • Strong technical communications skills
  • Ability to deliver compelling demos, whiteboard sessions, and presentations
  • Ability to act as a trusted advisor and establish credibility with senior leadership and enterprise architects
  • Deep understanding of LLM security risks (e.g., prompt injection, data leakage) and mitigation strategies using tools like Cortex Guard
  • Expertise in governing Autonomous Agents, ensuring "Human-in-the-loop" controls and auditability for agent-driven actions
  • Mastery of techniques like Differential Privacy, Data Masking, and Secure Sandboxing to protect sensitive training and inference data
  • Proficiency in observability techniques including logging, monitoring, and distributed tracing on a platform level
  • Expertise in modern authentication/authorization protocols (OAuth 2.0, OpenID Connect) and implementing robust Role-Based Access Control (RBAC) models across cloud and on-premises environments
  • Hands-on expertise in securing cloud and hybrid network architectures, including micro-segmentation, zero-trust principles, and secure deployment of services like Apache NiFi
  • Hands-on expertise with SQL, Python, and APIs
  • Proficiency in securing the container lifecycle, from build (image scanning) to runtime (Pod Security Standards, network policies, service mesh), and managing secrets within Kubernetes
  • Strong background in aligning security programs with regulatory frameworks (e.g., SOC 2, HIPAA, GDPR) and managing security risk through continuous monitoring and auditing

Nice to have

  • Understanding of emerging AI regulations (e.g., EU AI Act) and their impact on enterprise data strategy

What the JD emphasized

  • secure AI adoption
  • Agentic AI frameworks
  • Cortex Guard
  • AI Observability
  • Generative AI Security
  • Agentic AI Governance
  • Data Privacy for AI

Other signals

  • AI security and trust foundations
  • Architect trusted Agentic AI frameworks
  • Deploy Cortex Guard and AI Observability
  • Ensure AI governance
  • Generative AI Security
  • Agentic AI Governance
  • Data Privacy for AI