Anti-abuse Senior Software Engineer, Product Security

Snowflake Snowflake · Data AI · WA-Bellevue, United States · Engineering

This role focuses on building and operating systems to protect the Snowflake platform from abuse, misuse, and fraud, leveraging security engineering, data analytics, and machine learning. The engineer will design and deploy anti-abuse controls, research new services, and assess risks within applications and features.

What you'd actually do

  1. Design and deploy the anti-abuse controls addressing risks such as Account Take Overs (ATO), data exfiltration, risk around code/data sharing, and other newest and highest future engineering challenges as Snowflake grows.
  2. Research, plan, and build anti-abuse architectures for Snowflake products and features
  3. Provide designs and reference implementations for new anti-abuse features
  4. Research new services, controls, or features that can help protect the product and our customers from abuse.

Skills

Required

  • 4+ years of experience with anti-abuse space, insider threats, detections, threat hunting and incident response.
  • Understanding of common abuse patterns (e.g., ATO, exfiltration, insider threats, , spam, fraud, privilege misuse).
  • Proficiency in Python, SQL, or a similar language for building detections and data pipelines.
  • In-depth knowledge of anti-abuse solutions, network security, and/or infrastructure security.
  • Experience performing source code reviews across various languages (e.g. Java, Go)
  • Ability to assess engineering designs and architecture diagrams for abuse risks
  • Ability to assess abuse risks within an application or feature
  • Experience communicating abuse risks and roadmaps
  • Experience designing and implementing anti-abuse solutions

Nice to have

  • Master's degree or PhD in Computer Science or related technical field.
  • Experience with cloud environments (AWS, GCP, Azure) and their security/abuse detection tooling.
  • Familiarity with ML-based detection systems, feature engineering, or anomaly detection methods.
  • Experience contributing to the security anti-abuse community such as presenting at conferences or meetups.

What the JD emphasized

  • anti-abuse space
  • common abuse patterns
  • anti-abuse solutions
  • assess engineering designs and architecture diagrams for abuse risks
  • assess abuse risks within an application or feature
  • designing and implementing anti-abuse solutions

Other signals

  • AI-native thinkers
  • treating AI as a high-trust collaborator
  • intersection of security engineering, data analytics, and machine learning
  • developing prevention controls, detections, and automation that mitigate abuse activity