Drimva

Mecka AI

Privacy Counsel and Data Protection Officer

$150K–$180K

Toronto GTA +1 · Hybrid · Full-time · Posted Oct 31, 2026

Location
Toronto GTA
New York
Experience
5 to 8+ years; experience at both a top tier law firm and in-house.
Education
Law degree.

About the job

Owns Mecka AI’s global privacy programme and serves as its independent Data Protection Officer for video-data capture and processing across markets.

What you'll do

  • Maintain the privacy programme, including consent standards, records, assessments, policies, training, and cross-border transfer and registration frameworks.
  • Independently advise on and monitor data protection compliance; serve as a contact for data subjects and regulators and report material privacy risks.
  • Partner with technical and operational teams on privacy controls, review new capture models and deliveries, and lead incident responses and outside counsel management.

What they ask for

  • Law degree and admission to practice in at least one jurisdiction.
  • 5 to 8+ years of privacy and data protection practice, with experience both at a top tier law firm and in-house at a high-growth technology, marketplace, AI, or data company.
  • Demonstrated ability to perform the DPO function independently and objectively, including communicating with senior management and regulators.
  • Hands-on experience advising on data protection regimes in several countries at once, including consent, cross-border transfers, impact assessments, breach response, regulator registration, and outside counsel management.

Nice to have

  • Experience with video, image, biometric, location, or machine-learning training data; or in-house experience at a marketplace, delivery, AI, data, or gig platform operating across emerging markets.
  • Privacy certification such as CIPP/E, CIPP/A, or CIPM; SOC 2 compliance or audit-readiness experience; or regulatory engagement experience.

Pay

$150K – $180K (base salary)

Tools

AI, SOC 2

AI focus

Privacy counsel for data capture and processing used to train and validate humanoid and embodied AI systems.

Model-written summary · the original posting is authoritative

First seen Nov 1, 2026 · Last verified Nov 6, 2026 (UTC)