Senior Security Architect, Applied Field Engineering (afe)

Snowflake Snowflake · Data AI · NY-New York, United States · Solution Engineering

Senior Security Architect role focused on enabling secure AI adoption for enterprises using Snowflake's platform. The role involves designing secure architectures for agentic AI frameworks, implementing AI security measures like Cortex Guard and AI Observability, and ensuring data privacy and governance for AI applications. It also includes transitioning customers 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. Drive the creation of relevant technical content and tooling to showcase security best practices, accelerate new use case deployments, and adoption of new platform functionality.

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
  • 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.
  • Experience with Trust

What the JD emphasized

  • AI Security & Trust Foundations
  • trusted Agentic AI frameworks
  • secure AI adoption
  • AI governance
  • Generative AI Security
  • Agentic AI Governance
  • Data Privacy for AI
  • Platform Observability

Other signals

  • Architect trusted Agentic AI frameworks
  • Deploy Cortex Guard and AI Observability
  • Support customer strategy for secure AI adoption