Principal Data Privacy Architect

Description -

Job Summary

- Role Purpose

• 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 & Complianc