Privacy Engineer
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- Translate privacy requirements into data flows, risks and controls
- Engineer privacy-preserving systems and verify privacy risk
- Build scalable privacy infrastructure for AI and distributed systems
Benchmark: OpenAI + Anthropic + Apple/Google privacy-engineering roles (Sep 2026): embed privacy into architecture and SDLC; discover, classify and map data; enforce access, purpose, retention/deletion and audit controls; threat-model privacy risks; implement privacy-enhancing technologies; translate legal and policy requirements into scalable engineering controls. Mastery ladder: 2 job-market competencies per level; 2 direct VIDEO bridges per competency (12 total).
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Translate privacy requirements into data flows, risks and controls
Privacy-by-design, data mapping + lifecycle requirements
Coverage: Data Governance / Privacy / Data Modeling / Risk Assessment
Access, purpose limitation, retention + audit controls
Coverage: IAM / Data Governance / Compliance Automation / Auditability
Engineer privacy-preserving systems and verify privacy risk
Privacy threat modeling, reviews + adversarial analysis
Coverage: Privacy Threat Modeling / Risk / Secure SDLC / Product Security
Differential privacy + federated learning
Coverage: Privacy-Enhancing Technologies / ML Privacy / Private Analytics
Build scalable privacy infrastructure for AI and distributed systems
Secure computation: MPC + homomorphic encryption
Coverage: Cryptography / Distributed Systems / Privacy-Preserving Compute
Policy-as-code, observability + privacy incident response
Coverage: Policy Enforcement / Platform Engineering / Audit / Incident Response
Role-Readiness Proof / Capstone
Engineer a privacy-preserving feature/platform for an AI product: data-flow inventory and classification, purpose/retention/deletion rules, privacy threat model and review, consent/access/policy-enforcement service, audit logs and lifecycle automation, a private analytics path using differential privacy or federated learning, and one advanced PET design using MPC or FHE. Add observability, an incident playbook and evidence of control effectiveness.
Proof Rubric
- Minimize data
- make purpose and ownership explicit
- translate policy into executable controls
- prove retention, deletion and access behavior
- quantify privacy/utility tradeoffs
- threat-model privacy harms distinctly from security
- choose PETs for realistic use cases
- make controls auditable and resilient
- handle incidents and exceptions without weakening defaults