Principal Data Protection Engineer

Equinix•Dallas, TX

About The Position

The Digital Information Security Engineering team is seeking a highly experienced Principal Data Protection Engineer to lead the design, development, and automation of data protection capabilities embedded across enterprise platforms and services. This role focuses on protecting sensitive data throughout its lifecycle, enabling privacy-by-design architectures, and building developer-first APIs and AI-enabled solutions that integrate seamlessly into modern engineering workflows. The ideal candidate is a hands-on technical leader with deep expertise in data protection, privacy engineering, and secure data architecture, combined with strong software development skills and a forward-looking mindset around AI, agentic agents, and autonomous systems.

Requirements

Bachelor’s or Master’s degree in Computer Science, Software Engineering, Information Security, or a related field
10+ years of hands-on experience across data protection, security engineering, or privacy engineering
Deep understanding of data protection technologies and practices, including encryption, key management, data classification, tokenization, anonymization, and access control
Strong experience designing and delivering RESTful (and/or gRPC) APIs, including containerization, API gateways, service orchestration, and platform components
Advanced programming expertise in Python and Go (additional languages are a plus)
Experience performing threat modeling, data risk analysis, and privacy impact assessments
Real-world experience with CI/CD pipelines (e.g., GitHub Actions)
Experience deploying and operating services on public cloud platforms (AWS, Azure, or GCP) using Docker and Kubernetes
Exposure to data analysis, machine learning, or AI systems, particularly where sensitive or regulated data is involved

Nice To Haves

Is a transformational leader who excels at driving bold and meaningful change at scale and across multiple dimensions
Leads with a global mindset, across cultures, in a highly matrixed, relationship and consensus-driven environment
Cultivates Innovation – ability to foresee potential security challenges and trends in the industry, and to develop strategies to address them
Be relentlessly driven, pragmatic, and outcome-focused
Demonstrate a strong backbone—willing to challenge assumptions and raise the bar on data protection
Exhibit high curiosity and stay current with privacy, data security, AI, and agentic system trends
Show pride of ownership and strive for technical and operational excellence
Thrive in a fast-paced, diverse, and constantly evolving environment
Enjoy learning and applying new technologies and paradigms

Responsibilities

Design and implement data protection controls across data ingress, processing, storage, inference, and egress layers (e.g., classification, tokenization, encryption, masking, policy enforcement)
Define and operationalize privacy-by-design and secure-by-default patterns for platforms, APIs, and AI-driven systems
Evaluate and implement data governance, consent, residency, retention, and deletion mechanisms aligned with regulatory and enterprise requirements
Analyze and assess data flows to identify privacy risks, data exposure paths, and compliance gaps
Design, develop, and deliver high-quality, production-grade APIs that expose data protection and privacy capabilities as reusable services
Own the API layer design, including authentication, authorization, schema design, rate limiting, observability, and security controls
Research, select, and implement technologies for API gateways, service orchestration, and platform integration
Ensure APIs meet published SLAs and SLOs, with built-in instrumentation and monitoring
Develop core services, tooling, and automation primarily using Python and Go (Golang).
Build integrations, hooks, plugins, and pipelines that leverage data from security, privacy, and AI platforms
Automate data protection workflows across CI/CD pipelines and cloud platforms
Perform rigorous testing and validation to ensure reliability, scalability, and performance
Evaluate and integrate AI and open-source technologies to enhance data protection, detection, and enforcement
Design or contribute to agentic agents and autonomous workflows that make context-aware decisions about data use, access, and policy enforcement
Partner with AI platform teams to address data protection challenges in model training, fine-tuning, inference, and RAG systems
Research emerging trends in AI safety, privacy-preserving ML, and secure data sharing
Collaborate closely with Product Management and cross-functional engineering teams to translate requirements into technical solutions
Anticipate architectural and delivery risks; proactively resolve or escalate technical roadblocks
Own technical implementation end-to-end from design through production and operations
Operate, support, and continuously improve data protection services in live environments