Director of Enterprise Data Privacy Engineering

Think Customer First

Make privacy a customer value proposition, ensuring trust and transparency in all data products. Embed privacy-by-design into enterprise platforms and customer-facing applications.

Requirements

• Bachelors or masters degree in computer science, Engineering, or related field.
• 10+ years of progressive experience in data privacy, cybersecurity, or data protection, with enterprise-scale architecture expertise.
• Proven leadership in AI-driven privacy engineering, automation, and data engineering solutions for customer consent and privacy compliance.

Technical Skills

• Hands-on experience with AI security tools, DLP solutions, and privacy-by-design principles.
• Strong programming skills (e.g., Python, Java) and proficiency with data processing frameworks (e.g., Apache Spark, Hadoop).
• Expertise in cloud platforms (AWS, Azure, GCP) and data storage technologies (SQL, NoSQL, Big Data).
• Working knowledge of sensitive data management technologies (e.g., BigID) and data loss prevention techniques.

Regulatory Knowledge

• Deep understanding of global privacy regulations (GDPR, CCPA) and cyber frameworks (NIST, ISO 27001), with a proven track record of implementing robust compliance solutions.

Leadership and Communication

• Outstanding leadership, communication, and stakeholder management skills.
• Ability to influence senior executives and drive privacy as a business enabler across the organization.

Experience and Certifications

• Demonstrated experience in technical leadership roles developing applications for consent management and customer data privacy.
• Industry certifications such as CIPP/E, CIPM, CISSP, IAPP or equivalent.
• Active industry participation, publications, or memberships related to data privacy and customer trust.

Designing AI-First Privacy Architecture

Design AI-first privacy architecture that automates compliance, risk prediction, and secure data engineering.

• Develop privacy accelerators - frameworks, APIs, and SDKs - that integrate seamlessly into product development workflows.
• Ensure ethical AI practices and transparent governance dashboards.

Collaboration and Process Automation

• Collaborate with Data Engineering, Product, Legal, and Cybersecurity teams to embed privacy into enterprise data platforms.
• Process Automation: Streamline privacy workflows across data lifecycle-design, build, deploy, monitor.