About the role
Define and drive governance approaches for the collection, access, use, sharing, retention, transformation, and protection of security-related data. Partner with engineering, security research, privacy, legal, compliance, and business teams to evaluate sensitive or novel data-use scenarios and translate
requirements into actionable technical guidance. Lead privacy and data-governance work for artificial intelligence, including model training, anonymization, de-identification, and differential privacy. Develop decision frameworks, standards, playbooks, certifications, and approval processes that make complex privacy
requirements clear, repeatable, and auditable. Identify systemic privacy and data-governance risks across products and programs, and drive resolution through technical analysis, stakeholder alignment, risk-based recommendations, and leadership escalation. Serve as a trusted advisor for high-impact or ambiguous privacy decisions, clearly communicating technical options, tradeoffs, dependencies, and customer or regulatory implications. Bachelor's Degree AND 6+ years experience in engineering, product/technical program management, data analysis, or product development 3+ years of experience managing cross-functional and/or cross-team projects Bachelor's Degree AND 12+ years experience engineering, product/technical program management, data analysis, or product development OR equivalent experience. 8+ years of experience managing cross-functional and/or cross-team projects. 1+ year(s) of experience reading and/or writing code (e.g., sample documentation, product demos). Experience translating privacy, legal, regulatory, or policy
requirements into technical and operational requirement Experience working with engineering, legal, compliance, research, security, or data-science stakeholders on complex technology programs Experience with privacy engineering, data protection, responsible artificial intelligence, cybersecurity data, or enterprise data governance. Working knowledge of data anonymization, de-identification, differential privacy, tokenization, synthetic data, or privacy-preserving machine-learning methods. Experience developing or operationalizing technical standards, governance frameworks, control
