Security Analytics Engineer L5

Netflix is an entertainment and technology company whose Consumer Security Foundations team builds shared data, tools, and infrastructure to protect customers and products. The Security Analytics Engineer will analyze consumer security risk, develop trusted metrics and dashboards, define risk portfolios, shape leadership reporting, and build scalable data pipelines in collaboration with security and product teams.

Responsibilities:

• Drive the analytics behind device and authentication risk, digging into signals like device trust, login/auth protocols, and access patterns to surface where and how risk shows up for our members
• Be a thought partner in shaping the strategy behind our consumer security risk metrics, deciding what the organization should measure and why, analyzing the underlying data to validate what's meaningful, and translating that into a dashboard that gives every security team a shared, current view of top risks
• Define and evolve our consumer product risk portfolio, using data to quantify and prioritize the ground-truth inventory of risk areas across our security organization, driving alignment across teams and with the broader company risk registry
• Influence how our organization's risk story is told at the leadership level, shaping the analysis and narrative behind quarterly risk reviews, monthly metrics reviews, and leadership-level cost-of-fraud reporting
• Partner strategically with security PMs and product teams to define what a comprehensive security metrics and controls story looks like for each product vertical, and drive its adoption
• Collaborate closely with threat-modeling partners to get ahead of emerging risks, using data to shape how they get measured and reported rather than reacting after the fact
• Build and maintain the data pipelines and metrics that power our analytics and dashboards, and design solutions other consumer security teams can build on, driving down one-off tooling across the organization

Requirements:

• • Domain expertise in consumer product security, such as fraud, account security, abuse/trust & safety, authentication, or privacy, and fluency in how security risk gets measured and reported
• • Strong engineering fundamentals, including well written, tested, maintainable code, sound data modeling, and data pipelines built to scale
• • Expert in analytics (SQL, Presto, Spark SQL, Python), with strong critical thinking and a demonstrated ability to turn raw data into clear, compelling narratives
• • Quick to pick up new tools and techniques, including using AI/LLM-based tools to work faster and smarter across analysis, engineering, and reporting
• • Comfort operating in ambiguity, building a data model, portfolio, or dashboard from a 0-to-1 state and iterating with cross-functional input
• • A strong bias for action, comfortable moving fast, making decisions with incomplete information, and driving ideas to production rather than waiting for perfect conditions