Principal Data Privacy Architect

Job Summary This role will design and implement scalable, AI‑ready data privacy architecture across enterprise data environments, applications, and AI‑enabled workflows. The Principal Data Privacy Architect will serve as a hands‑on subject matter expert responsible for embedding privacy‑by‑design, consent enforcement, data sovereignty, data loss prevention, and compliance controls into large, complex global data environments. The architect will partner closely with Data Engineering, Cybersecurity, Legal, Privacy, AI Governance, Product, and Enterprise Architecture teams to ensure customer, employee, partner, and sensitive enterprise data is accessed, processed, shared, retained, and protected in a compliant, secure, and trustworthy manner. Why This Role Matters Architect for Trust & Scale: Build reusable privacy architecture patterns that enable secure, compliant, and scalable data usage across platforms, products, and regions. Enable Responsible AI: Design privacy guardrails for AI agents, generative AI, RAG pipelines, model inputs and outputs, embeddings, vector stores, and automated data workflows. Reduce Risk While Enabling Innovation: Translate privacy, consent, regulatory, and data sovereignty obligations into practical engineering controls that accelerate business outcomes. Responsibilities Think Customer First Embed customer trust, transparency, and privacy‑by‑design principles into enterprise data platforms and customer‑facing applications. Design consent‑aware data access and usage patterns across analytics, personalization, marketing, product telemetry, support, and AI use cases. Ensure customer data is collected, processed, shared, retained, and deleted according to approved purposes, consent preferences, and regulatory obligations. Innovate for Growth Architect reusable privacy engineering components, including APIs, SDKs, reference architectures, automation patterns, and policy‑as‑code controls. Design privacy controls for AI agents and AI‑enabled workflows that access, process, summarize, or publish sensitive data. Build technical patterns for data minimization, anonymization, pseudonymization, tokenization, encryption, masking, and secure data sharing. Act with Integrity Partner with Legal, Privacy, Cybersecurity, and Compliance teams to translate global privacy regulations and internal policies into enforceable technical controls. Support compliance with GDPR, CCPA/CPRA, LGPD, PIPL, India DPDP Act, data sovereignty mandates, cross‑border transfer requirements, and regional data residency obligations. Define auditable controls for consent enforcement, access monitoring, retention, deletion, lineage, and compliance evidence collection. Build for the Future Establish privacy architecture patterns across data warehouses, lakehouses, metadata platforms, customer data platforms, AI/ML environments, vector databases, and cloud platforms. Integrate sensitive data discovery, classification, lineage, DLP, DSPM, IAM, KMS, and monitoring capabilities into the enterprise data ecosystem. Advance automated compliance monitoring, privacy control validation, and risk detection across the data lifecycle. Work as One Team Collaborate with Data Engineering, Product, AI Governance, Cybersecurity, Legal, Privacy, and Enterprise Architecture teams to embed privacy controls into delivery workflows. Provide hands‑on architecture guidance for high‑risk data initiatives, AI programs, customer data products, and platform modernization efforts. Mentor engineers, architects, data scientists, and product teams on privacy engineering best practices. Strategic & Technical Focus Areas AI‑Ready Privacy Architecture: Privacy controls for AI agents, generative AI, RAG pipelines, model inputs and outputs, embeddings, vector stores, and automated data workflows. Consent & Purpose‑Based Usage: Consent propagation, purpose limitation, consent revocation, customer preference enforcement, and downstream data usage controls. Data Loss Prevention & Sensitive Data Protection: DLP integration, sensitive data classification, risky sharing detection, exfiltration prevention, and AI prompt/output inspection. Data Sovereignty & Compliance Engineering: Regional data residency, cross‑border transfer controls, localization requirements, encryption key residency, and audit evidence automation. Reusable Privacy Frameworks: Standardized architecture patterns for encryption, masking, tokenization, anonymization, retention, deletion, access control, and monitoring. Education & Experience & Skills Education & Experience Bachelor’s or master’s degree in Computer Science, Engineering, Information Systems, Cybersecurity, Data Engineering, or related field. 10+ years of progressive experience in data privacy, data protection, cybersecurity, data architecture, or enterprise data platforms. Proven experience architecting privacy and data protection solutions in large, complex, global environments. Hands‑on experience implementing privacy‑by‑design, consent management, data sovereignty, DLP, and sensitive data protection controls. Technical Expertise Strong understanding of global privacy regulations and frameworks, including GDPR, CCPA/CPRA, LGPD, PIPL, India DPDP Act, NIST, ISO 27001, and related privacy/security standards. Experience with cloud platforms such as AWS, Azure, or GCP, and enterprise data platforms including data warehouses, lakehouses, data catalogs, metadata platforms, and big data environments. Working knowledge of privacy and data protection technologies such as BigID, OneTrust, Securiti, Collibra, Informatica, Microsoft Purview, AWS Macie, Google Cloud DLP, Azure Information Protection, DLP, DSPM, CASB, IAM, and KMS capabilities. Strong technical skills in Python, Java, SQL, APIs, Spark, data pipelines, infrastructure‑as‑code, and policy‑as‑code. Experience with AI/ML, generative AI, AI agents, RAG architectures, vector databases, feature stores, model governance, or AI‑enabled data products. Leadership & Business Skills Ability to translate legal, privacy, compliance, and business requirements into scalable technical architecture. Strong communication and influencing skills with engineers, architects, legal teams, privacy teams, product leaders, and senior executives. Demonstrated ability to balance customer trust, regulatory compliance, engineering practicality, and business agility. Preferred Qualifications Certifications such as CIPP/E, CIPP/US, CIPM, CIPT, CISSP, CCSP, CDPSE, or equivalent. Experience building consent management platforms, privacy preference centers, data subject rights automation, or customer data governance capabilities. Experience implementing purpose‑based access control, attribute‑based access control, zero‑trust data architecture, or data‑centric security models. Active industry participation, publications, or memberships related to privacy engineering, AI governance, cybersecurity, or customer trust. Cross‑Org Skills Effective Communication Results Orientation Learning Agility Digital Fluency Customer Centricity Salary The pay range for this role is 154,400.00 - 227,750.00 USD annually with additional opportunities for pay in the form of bonus and/or equity (applies to United States of America candidates only). Pay varies by work location, job‑related knowledge, skills, and experience. Benefits HP offers a comprehensive benefits package for this position, including: Health insurance Dental insurance Vision insurance Long‑term/short‑term disability insurance Employee assistance program Flexible spending account Life insurance Generous time off policies, including 4-12 weeks fully paid parental leave based on tenure 11 paid holidays Additional flexible paid vacation and sick leave (US benefits overview) The compensation and benefits information is accurate as of the date of this posting. The Company reserves the right to modify this information at any time, with or without notice, subject to applicable law. #J-18808-Ljbffr HP