Cybersecurity Engineer

Workday•Boulder, CO

9d•$117,800 - $210,000•Hybrid

About The Position

Workday is seeking a Cybersecurity Engineer to join the Security Automation & Integration Engineering (SecAIE) team. This role will focus on owning the infrastructure, automation, and platform reliability for Workday's Active Defense charter. The engineer will be responsible for developing reliable data pipelines, cloud infrastructure, CI/CD processes, and cross-tool integrations to support AI-driven security execution and decision-making. The ideal candidate will have a strong understanding of cybersecurity concepts and best practices, and will partner with security domain experts to enhance their work. This is an opportunity to leverage AI as a force multiplier in security engineering.

Requirements

Strong Python skills with hands-on automation and integration experience (APIs, Lambdas, batch jobs, workflow glue), including code review, testing, and shipping to production
Deep hands-on experience with infrastructure-as-code (Terraform) and CI/CD (Jenkins, GitHub Actions, or similar), including production ownership
Working fluency across a broad AWS surface (Lambda, ECS, S3, IAM, KMS, VPC, and at least one compute service such as EMR, Batch, Glue, or EKS)
Experience with containerization (Docker) and modern deployment patterns
Working knowledge of core cybersecurity practices - identity and access, secrets and key management, secure-by-default cloud configuration, and reasoning over common security data (vulnerabilities, incidents, identity, threat intel) - so you can build for security outcomes, not just infrastructure ones
Comfortable owning on-call and operational readiness (runbooks, alarms, SLOs) for services you deliver

Nice To Haves

Security domain depth - hands-on with security tools or data for triage and investigation (SIEM, vulnerability scanners, identity systems, cloud security posture, secrets management); working knowledge of vulnerability data and workflows (scoring, prioritization, at least one scanner in practice); familiarity with security orchestration platforms as an integration target; curiosity about deception and adversary-emulation tooling
DevOps / platform depth - contributing to shared platform components (reusable Terraform modules, internal libraries, developer tooling); observability tooling (metrics, logs, traces) for services you own; building Slack bots, workflow automations, or enterprise tool integrations (Jira, PagerDuty, ServiceNow, etc.)
Data engineering depth - distributed data technologies (Spark, Trino, Hive, S3-as-datalake) and SQL; comfort reasoning about shared schemas or data contracts that span team boundaries, treating what / who / state as a first-class artifact rather than fields on a table
AI-augmented engineering depth - hands-on use of AI-augmented development workflows (Copilot, coding agents, LLM-driven code review, agentic pipelines) with a point of view on where they help; experience contributing to LLM-based systems moving toward production (agent frameworks, tracing and observability for agent runs, offline evaluation of prompts and tool calls)
Comfort operating in ambiguous problem spaces where you help define the solution, not just implement it

Responsibilities

Own the infrastructure-as-code and platform substrate that our security data and automation services run on - from tuning existing services to landing net-new modules and multi-environment rollouts
Design, build, and operate the automations and data pipelines that ingest, transform, and route security data reliably at scale
Extend and harden our continuous delivery and cloud-security posture so every service is deployable, observable, and secure by default
Take partner requests from vague to shipped: scope the problem, propose approaches, choose one, and drive it through design, review, rollout, and operational readiness
Build and maintain integrations across security tools and enterprise platforms - SOAR, SIEM, EDR, vulnerability scanners, ticketing - to accelerate response and reduce toil
Raise the team's AI-augmented engineering floor: shared skills, reusable scaffolds, and a review discipline for AI-generated code
Iterate quickly: prototype, test, ship, learn, improve - and bring the team with you