Security Operations Data Loss Prevention Engineer

Everpure•Santa Clara, CA

7d•$225,000 - $338,000•Onsite

About The Position

As our Security Operations Data Loss Prevention (DLP) Engineer, you will own and elevate Everpure’s enterprise data protection strategy across every digital and physical vector. In this high-impact individual contributor role, you will establish data governance standards and architect practical controls that safeguard customer information, source code, intellectual property, and other sensitive assets without hindering business velocity. You will partner with Engineering, Legal, Privacy, DevSecOps, and GISO leadership to define how sensitive data is discovered, classified, monitored, and protected across the enterprise.

Requirements

Demonstrated experience owning or materially contributing to an enterprise DLP or data-protection program across architecture, deployment, policy design, tuning, operations, and continuous improvement.
Hands-on experience with enterprise DLP technologies such as Microsoft Purview, Zscaler, Netskope, Forcepoint, Broadcom/Symantec, Trellix, CrowdStrike Data Protection, or equivalent platforms.
Strong experience with regex, EDM/IDM, document fingerprinting, data classification, custom classifiers, and detection tuning, including reducing false positives while maintaining effective coverage.
Experience with data discovery, classification, CASB/SaaS security, cloud data protection, and controls for sensitive data across enterprise and cloud environments.
Experience using Python, PowerShell, REST APIs, SIEM, and/or SOAR platforms to automate response, analyze telemetry, and improve operational outcomes.
Working knowledge of data-protection and security frameworks such as NIST CSF, ISO 27001, SOC 2, GDPR, CCPA, PCI DSS, and HIPAA.
Ability to translate complex data-protection risks into clear decisions and business outcomes for technical teams, cross-functional partners, executives, auditors, and other stakeholders. You know how to balance effective controls with user experience and business needs.

Nice To Haves

Experience with AI/GenAI data protection, DSPM, insider risk, privacy engineering, or source-code and secrets protection.
Experience securing data in AWS, Azure, or GCP, or integrating data-protection controls into CI/CD, DevSecOps, containers, or Kubernetes.
Background in application security, cloud security, or privacy engineering.
Experience communicating data-protection strategy and posture to executives, auditors, regulators, or enterprise customers.
Relevant security, privacy, or cloud certifications such as CISSP, CISM, CIPP, GIAC, SC-400, or equivalent.

Responsibilities

Own and mature the multi-channel DLP program end to end across endpoint, network, SaaS, application, cloud, and generative AI environments. Define program metrics, track outcomes, evaluate build-versus-buy decisions, and report data-protection posture to GISO leadership.
Author data-classification frameworks, handling requirements, and exception processes. Maintain a sensitive-data map with data owners so the organization understands where regulated and proprietary data lives, how it is classified, and who is accountable for it.
Design and operate controls across macOS, Windows, and Linux, including removable media, printing, clipboard, screen capture, and local cloud-sync channels. Define requirements for hard-copy handling, device disposal, and media sanitization, including the specific needs of manufacturing and lab environments.
Define egress inspection strategies across email, web, and general network traffic, including practical SSL/TLS inspection boundaries and encrypted-flow visibility. Detect and prevent sensitive-data movement through applications, APIs, databases, file shares, CI/CD systems, and unstructured repositories while partnering with Cloud and DevSecOps teams across AWS, Azure, and GCP.
Establish practical guardrails for sensitive data flowing into and out of public AI assistants, embedded copilots, internal models, and agentic tools. Address prompt content, file uploads, context windows, tool integrations, source code, and build-system secrets while enabling safe AI adoption.
Design, tune, and maintain high-fidelity DLP detections using regex, exact data match (EDM), indexed document matching (IDM), document fingerprinting, custom classifiers, and contextual conditions. Develop SIEM queries, correlate DLP events with identity, asset, vulnerability, and threat context, and integrate telemetry with SOAR platforms to reduce false positives and manual response effort.
Direct investigations into significant data-loss events by reconstructing what left the enterprise, through which channel, and with what impact. Serve as the escalation point for complex findings, brief Legal, Privacy, HR, and leadership clearly, support audit and compliance engagements, and maintain runbooks, architectural diagrams, and program documentation.