Cybersecurity Platform and Identity Security Engineer

Verizon•Temple Terrace, NJ

1d•Remote

About The Position

You'll be a senior individual contributor on a platform team that builds and secures the internal microservices powering enterprise identity operations at Verizon — Active Directory management, access provisioning, privileged account lifecycle, and the services that tie it all together. You won't just be handed tickets. You'll own services, investigate problems from first principles, and ship solutions that hold up under scrutiny from both an engineering and a security lens.

Requirements

Bachelor's degree or four or more years of work experience.
Four or more years of relevant work experience required, demonstrated through work experience and/or military experience.
Four or more years of software engineering experience.
.NET (C#) development skills specifically with async/await, dependency injection, REST API design, and common framework patterns.
Experience with injection vulnerabilities (SQL, LDAP), authentication/authorization patterns, and secure configuration practices.
Experience working in a microservice or service-oriented architecture.
Comfort reading and reasoning about unfamiliar codebases — you can navigate a large repo, understand data flow, and identify where trust boundaries exist.
Working knowledge of Active Directory concepts: users, groups, OUs, permissions — enough to understand what the services you're working on actually do.
Hands-on experience with OWASP Top 10 or OWASP API Security Top 10 — not just awareness, but having found or fixed examples.
Experience integrating with or debugging Active Directory operations (LDAP queries, group membership, account management).
JWT / OAuth 2.0 — understanding how tokens are issued, validated, and where they can be abused.
Experience working with shared internal NuGet packages or internal frameworks — understanding how breaking changes propagate.
EF Core, database connection management, migration patterns.
Experience using AI coding tools (GitHub Copilot, Claude Code, or similar) as a real accelerator on day-to-day engineering work.
Track record of producing clear technical documentation — not just for external audiences, but the kind that helps the next engineer understand what a service does and why.

Nice To Haves

Security+ or equivalent foundational security knowledge.
Exposure to secrets scanning tools (TruffleHog, GitLeaks) or SAST tooling in CI/CD pipelines.
Familiarity with CyberArk or other PAM platforms.
Azure DevOps or GitLab pipeline experience.

Responsibilities

Build and maintain microservices within a shared .NET framework (Micro.Framework, SharedServices, WebTools).
Implement feature work, bug fixes, and platform upgrades across a large service fleet — async pattern improvements, framework version migrations, NuGet dependency updates.
Contribute to breaking-change analysis when shared libraries evolve: understand downstream impact, validate that consumer contracts hold.
Build and maintain SCIM provisioning integrations that automate identity lifecycle events (create, update, deprovision) across enterprise systems.
Develop and manage API proxies, policies, Automation, and traffic management in Apigee for services exposed internally and externally.
Implement security remediations identified through assessment, spanning application-layer vulnerabilities and security configuration hardening.
Apply cloud-native secrets management patterns: Key Vault integration, Managed Identity, encrypted connections, and proper JWT validation.
Write secure code by default — parameterized queries, input validation at system boundaries, least-privilege service accounts.
Participate in white-box security reviews of internal services — reading code with an attacker's eye, identifying common application-layer vulnerabilities.
Document findings with enough specificity to be actionable: file paths, line numbers, CVSS context, reproduction steps.
Translate security findings into working code fixes, not just recommendations.
Use AI coding assistants actively in your daily workflow — for large refactors, codebase comprehension, documentation, and repetitive pattern application across multiple services.
Know where AI output needs validation and when to course-correct.