Junior Data Security Engineer

Verizon•Alpharetta, GA

23h•Remote

About The Position

When you join Verizon, you want more out of a career. A place to share your ideas freely — even if they’re daring or different. Where the true you can learn, grow, and thrive. At Verizon, we power and empower how people live, work and play by connecting them to what brings them joy. We do what we love — driving innovation, creativity, and impact in the world. Our V Team is a community of people who anticipate, lead, and believe that listening is where learning begins. In crisis and in celebration, we come together — lifting our communities and building trust in how we show up, everywhere & always. Want in? Join the #VTeamLife. What You'll Be Doing: You'll be a contributing member of the Data Services team that builds and maintains the MCP server infrastructure powering enterprise security data operations at Verizon — vulnerability data tooling, data transfers to Google Cloud Storage, analytics data sharing, and the pipelines and services that connect it all together. You'll work on real services with real ownership — not in isolation, but alongside senior engineers who will help you build context and judgment. You're expected to complete assigned work end-to-end, ask the right questions when you're stuck, and grow your understanding of how the data platform fits together over time. You're expected to actively use AI tools in your daily workflow. This team works that way by design, and we expect Engineer IIs to come in ready to learn and apply those capabilities as a productivity accelerator, not just a convenience.

Requirements

Bachelor's degree or one or more years of relevant work experience.
One or more years of software engineering experience.
Working Python or .NET (C#) development skills — comfortable building and maintaining backend services, REST APIs, and data processing logic.
Familiarity with data pipeline concepts — understanding how data moves between systems, basic ETL patterns, and what can go wrong at each stage.
Understanding of common injection vulnerabilities and the importance of input validation and secure credential handling — especially relevant when working with vulnerability datasets
Exposure to cloud storage or data transfer patterns — GCS, S3, or similar object storage in a project or professional context.
Comfort reading unfamiliar code — you can follow data flow and find where the important logic lives even in codebases you didn't write.
Active or recent use of AI coding tools (Claude Code or similar) — not just awareness, but a genuine attempt to build them into how you work.

Nice To Haves

Security+ or equivalent foundational security knowledge, or active pursuit of it
Awareness of OWASP Top 10 or OWASP API Security Top 10
Any exposure to SAST tooling or secrets scanning in a CI/CD pipeline
Hands-on experience with GCS — reading, writing, and managing objects; understanding bucket structure and IAM access patterns
Familiarity with analytics data sharing patterns — exports to BigQuery, Looker, Google Sheets, or similar platforms
Basic familiarity with JWT / OAuth 2.0 — understanding how tokens are issued and validated for service-to-service data access
Any exposure to MCP (Model Context Protocol) — understanding what MCP servers do and how tools are defined and consumed by AI agents
Hands-on experience with Google Cloud Platform (GCP) — Cloud Run, GCS, Pub/Sub, IAM, or related services
Any experience with Azure DevOps, GitLab, or similar CI/CD tooling
Familiarity with database patterns — query design, connection management, or migration tooling (EF Core or similar)
Some exposure to TypeScript or a JavaScript frontend framework (React, Angular, or similar)
Demonstrated genuine use of AI coding tools — can speak to what you've tried, where it helped, and where you learned it needed checking
Ability to write clear technical documentation that helps another engineer understand what you built and why

Responsibilities

Contributing to the team's MCP server — building, maintaining, and extending MCP tools that expose vulnerability data and security datasets to AI agents and internal consumers.
Implementing feature work and bug fixes across data pipeline services under the direction of senior engineers — ingestion improvements, schema changes, and reliability work.
Building and maintaining data transfer pipelines to Google Cloud Storage — moving structured security data reliably between enterprise systems and GCS buckets.
Supporting data sharing integrations for analytics consumers — contributing to the services and exports that make security datasets available to downstream reporting and analytics platforms.
Participating in breaking-change analysis when shared data schemas or MCP tool contracts evolve: understand downstream impact and surface concerns to senior engineers.
Contributing to internal tooling and operator-facing surfaces where data platform capabilities are exposed — learning to think about what the consuming side needs, not just what the service provides.
Contributing to GCP-hosted services and infrastructure work — deploying and maintaining workloads in Google Cloud Platform as the team expands its cloud footprint.
Implementing security remediations identified through assessment, including application-layer vulnerability fixes and security configuration hardening, with senior engineer review.
Applying cloud-native secrets management patterns under guidance: Key Vault integration, Managed Identity, encrypted connections, and JWT validation.
Writing secure code by default — parameterized queries, input validation at system boundaries, least-privilege service accounts.
Participating in white-box security reviews of internal services — beginning to read code with an attacker's eye and learning to identify common application-layer vulnerabilities.
Contributing to documenting findings with specificity: file paths, line numbers, and reproduction steps under the direction of senior team members.
Translating security findings into working code fixes with senior engineer guidance on scope and remediation approach.
Using AI coding assistants as a daily accelerator — for navigating unfamiliar services, drafting code, and applying patterns across a codebase with senior review.
Generating and maintaining technical documentation with AI assistance — service overviews, data flow descriptions, and runbooks that help the next engineer.
Learning to validate generated code against the actual codebase, catch hallucinated APIs, and recognize when a suggestion needs to be overridden rather than shipped.