Senior Cloud Data Security Engineer

CVS Health•Carapichaima, TX

21h•$101,970 - $203,940•Remote

About The Position

CVS Health is hiring a Senior Cloud Data Security Engineer — Data Loss Prevention (DLP) to play a critical role in protecting the sensitive data assets of one of the largest pharmacy chains in the United States. As a senior member of the Data Protection Team, this role is responsible for the design, implementation, and continuous maturation of enterprise DLP capabilities across cloud, endpoint, email, network, and AI/ML environments, covering Data in Motion (DIM) and Data in Use (DIU). This engineer will lead efforts to prevent unauthorized data exfiltration, enforce data classification and handling standards, and extend DLP controls to AI/ML pipelines and shadow AI services. The role partners closely with security, engineering, data, and business stakeholders to deliver a cohesive, risk‑based data protection strategy that balances strong controls with a simplified user experience. This is a U.S.-based position; candidates must reside within the United States.

Requirements

5+ years of experience implementing and supporting cloud security solutions in large enterprise environments, with a strong focus on DLP.
5+ years of hands‑on experience with enterprise DLP platforms such as Microsoft Purview, Zscaler, and Palo Alto.
5+ years of experience designing and enforcing DLP policies across cloud, endpoint, email, and network channels.
5+ years of experience in at least two of the following cloud platforms: AWS, Azure, GCP, including data protection implementations.
5+ years of experience with Zero Trust, CASB, CSPM, and Conditional Access frameworks.
Bachelor’s degree from accredited university or equivalent work experience (HS diploma + 4 years relevant experience).

Nice To Haves

3+ years of experience using Regex for DLP policies and leveraging tools such as Splunk, Chronicle, or Power BI for analytics.
3+ years of experience with network security, email security, and firewall technologies related to data exfiltration prevention.
3+ years of experience securing AI/ML platforms, including exposure to LLMs, generative AI, and MLOps, with a data protection focus.
Proven experience leading DLP and data protection engineering initiatives in collaboration with cross‑functional teams.
Ability to provide off‑hours and weekend support on short notice when required.

Responsibilities

Design, implement, and mature enterprise DLP policies and controls across cloud, endpoint, email, and network channels.
Support the full DLP program lifecycle, including strategy, policy development, rule tuning, and continuous improvement.
Lead data classification and labeling initiatives to ensure consistent governance of PII, PHI, PCI, and proprietary data.
Monitor, investigate, and respond to data leakage incidents; manage cases, perform root cause analysis, and drive remediation to closure.
Develop dashboards, metrics, and reporting to communicate DLP effectiveness, risk posture, and trends to leadership.
Automate DLP enforcement, incident triage, and response workflows to improve accuracy and reduce manual effort.
Extend DLP capabilities to cloud‑native and hybrid environments, leveraging CASB, CNAPP, CSPM, SASE, and Zero Trust architectures.
Lead shadow AI discovery and enforce DLP controls across sanctioned and unsanctioned AI services.
Design and implement data protection controls for AI/ML workloads, pipelines, model training, and outputs.
Enforce secure data handling for generative AI, LLMs, and MLOps platforms to prevent improper ingestion, exposure, or transmission of sensitive data.
Partner with business and technology leaders to define DLP strategy, roadmaps, and priorities aligned to regulatory and risk requirements.
Align DLP controls with industry frameworks such as NIST, CIS, CSA, and MITRE ATLAS.
Serve as a subject matter expert on data protection for initiatives including cloud migrations, AI platform deployments, and M&A activities.
Collaborate with infrastructure, operations, data science, and application teams to embed DLP into architecture and operational processes.
Drive a low‑friction, user‑centric security experience while maintaining strong data protection outcomes.
Act as the organizational authority on DLP, cloud data security, and AI/ML data protection best practices.