Lead Counsel, Data Privacy & HR Strategy

Meta; Austin (TX); On-site; USD 170,000 - 237,000; Full time

Job summary

Meta in Austin is seeking a Lead Counsel (Data Protection) to guide privacy efforts for HR and security issues. You will help advance our global privacy program, navigate complex regulatory obligations, and work with cross‑functional teams across the business.

The role requires a JD, US bar admission, 5+ years of experience, and strong communication and strategic thinking. You will engage with regulators and drive privacy initiatives with a practical, business‑oriented approach.

Qualifications

• JD or equivalent from an accredited institution.
• Active and in good standing bar license in at least one US state.
• 5+ years of legal experience as a practicing attorney.
• Working knowledge of global privacy laws, regulations and programs.
• Effective communication, legal writing and interpersonal skills.
• Ability to manage multiple projects with deadlines.

Responsibilities

• Support Meta's global privacy program and frameworks.
• Evaluate privacy laws' impact on HR and security processes.
• Develop subject-matter expertise in relevant areas.
• Advise legal, policy, and business teams on privacy matters including data subject rights and AI in the workplace.
• Engage with regulators on new privacy features and initiatives.

Skills

Privacy law knowledge

Cross-functional collaboration

Legal writing

Communication skills

Strategic thinking

Education

Juris Doctor (JD) or equivalent

Tools

AI tools

Regulatory compliance tools

Job description

Meta in Austin is seeking a Lead Counsel (Data Protection) to guide privacy efforts for HR and security issues. You will help advance our global privacy program, navigate complex regulatory obligations, and work with cross‑functional teams across the business.

The role requires a JD, US bar admission, 5+ years of experience, and strong communication and strategic thinking. You will engage with regulators and drive privacy initiatives with a practical, business‑oriented approach.