Data Governance & Privacy Manager
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- Build a trustworthy data map and privacy-by-design foundation
- Enforce lifecycle policy and stewardship at scale
- Run AI governance, regulatory assurance and continuous monitoring
Benchmark: Apple AIML Privacy & Data Governance Engineering + AIML Compliance & Policies (current Apr-May 2026) Market refresh: data inventory/mapping + lineage, classification/ownership + quality, privacy-by-design, access/retention/data-use controls, policy + regulatory translation, AI governance, monitoring/audit evidence and cross-functional legal/engineering leadership.
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Build a trustworthy data map and privacy-by-design foundation
Data inventory, classification, ownership + lineage
Coverage: Data Governance / Data Quality, Governance & Lineage
Privacy engineering, minimization + data-flow risk
Coverage: Privacy & Data Protection / Security Fundamentals & Threat Modeling
Enforce lifecycle policy and stewardship at scale
Access, retention + approved data-use controls
Coverage: IAM / Data Protection / Data Governance
Data quality, metadata/catalog + stewardship workflows
Coverage: Data Quality, Governance & Lineage / Data Engineering
Run AI governance, regulatory assurance and continuous monitoring
AI/data regulation, risk assessments + policy translation
Coverage: AI & Agent Governance / Responsible AI / Data Governance
Monitoring, audit evidence + exceptions / remediation
Coverage: AI Evals & Monitoring / Compliance & Risk / Incident Response
Role-Readiness Proof / Capstone
Create and operate governance for an AI-enabled data product. Build system/data-flow maps and a catalog with classification, owners/stewards and lineage; define collection, purpose, usage, retention and access policies; run a DPIA/privacy risk assessment and AI-regulatory control mapping; implement least-privilege approvals, quality tests, monitoring and audit evidence; exercise an exception/data-incident workflow; deliver a governance dashboard and executive/legal/engineering review.
Proof Rubric
- Show evidence from data to control: complete inventory/lineage
- clear purpose and minimization
- accountable owners
- enforceable access and retention policies
- measurable data quality
- privacy-by-design tradeoffs
- traceability to applicable GDPR/CCPA/DMA/EU AI Act requirements
- AI risk assessment and monitoring
- audit-ready evidence
- exception/incident handling
- communicate residual risk and product/business impact