GTM DevOps Engineer

Design, build, and maintain CI/CD pipelines for Salesforce (SFDX/Salesforce CLI), NetSuite (SuiteScript/SuiteBundler), MuleSoft (Anypoint Platform), and Workato; establish branching strategies, environment promotion standards, and release gating processes across all GTM platforms.

Extend CI/CD practices to cover AI agent workloads deployed on GCP Cloud Run and AWS Bedrock AgentCore — including containerized builds, deployment pipelines, and automated validation gates.

Implement safe rollout patterns — including feature toggles, phased launches, automated validation, smoke tests, and rollback procedures — to reduce deployment risk on business-critical changes.

Own SLA/SLO definitions for core GTM systems; standardize monitoring, alerting, and runbook patterns across quote-to-cash and GTM integrations, with proactive health checks and synthetic monitoring for critical flows (e.g., Salesforce ↔ NetSuite, Workato).

Extend observability coverage to GCP Cloud Run workloads — Cloud Scheduler jobs, agent pipelines, and integration microservices — and AWS-hosted agent infrastructure.

Conduct root cause analysis (RCA) for platform incidents and drive post-incident reviews with actionable remediation plans.

Manage sandbox, staging, and production environment lifecycles across GTM platforms — including refresh cycles, data masking, environment segmentation, and promotion standards that balance speed with reliability.

Own cloud infrastructure for Business Systems-operated workloads on GCP (Cloud Run, Cloud Scheduler, Cloud Secret Manager, GCS, Artifact Registry) and AWS (Lambda, S3, EventBridge, Secrets Manager, Bedrock AgentCore); apply IaC practices to make provisioning repeatable and auditable.

Establish base image pinning, dependency vulnerability scanning, and supply chain security practices for containerized workloads — particularly AI-generated codebases deployed via tools like Cursor or Claude Code.

Define and enforce patch management and container runtime ownership for vibe-coded and agentic workloads entering production.

Establish and enforce a consistent secrets management standard across all Business Systems workloads — GCP Secret Manager, AWS Secrets Manager, and equivalent — eliminating credential exposure via environment variables, source code, or client-side contexts.

Define and maintain API key rotation policies in alignment with security standards (high-severity keys: quarterly; vendor keys: annually at minimum).

Partner with Security and IT on IAM scoping, least-privilege service accounts, VPC configuration, and public/private endpoint governance for Cloud Run and Bedrock deployments.

Maintain a centralized registry of deployed workloads — GitHub repos, deployment URLs, architecture docs, data classification, and observability dashboard links — accessible to AppSec and infrastructure teams.

Build internal tooling, automation scripts, and automated testing frameworks (unit, integration, regression) to reduce toil and increase deployment confidence; continuously evaluate new tooling to improve developer experience.

Develop or enforce GitHub repository templates for Cloud Run deployments that cover security audits, deployment configuration, API integration, and MCP server patterns — serving as a reusable foundation for AI-assisted builds.

Define where self-service deployment and administration are appropriate versus where stronger change control and operational guardrails are required; serve as the DevOps SME, enabling developers to operate with autonomy within those boundaries.

Document and maintain operational runbooks, architecture decision records (ADRs), and deployment standards as living artifacts.

Collaborate with IT, Data Engineering, Security, and business stakeholders on cross-functional initiatives that touch the GTM platform.

4+ years in a DevOps, Site Reliability Engineering (SRE), or Platform Engineering role

2+ years of hands-on experience deploying and managing Salesforce environments (SFDX, Salesforce CLI, scratch orgs, sandboxes, change sets, or pipeline-based deployments)

Experience with at least one additional GTM/ERP platform, such as NetSuite, MuleSoft, or Workato, in an operational or deployment capacity

Demonstrated experience building and maintaining CI/CD pipelines using tools such as GitHub Actions, GitLab CI, Jenkins, or Copado

Hands-on experience with GCP services — Cloud Run, Cloud Scheduler, Cloud Secret Manager, Artifact Registry, GCS — in a deployment or operations context

Experience with AWS services in an integration or operations context — Lambda, S3, SQS, Secrets Manager, CloudWatch; familiarity with Bedrock AgentCore or similar managed agent hosting infrastructure is a strong plus

Experience managing containerized workloads, including image lifecycle, dependency scanning, and supply chain security practices

Experience with incident management, on-call processes, and writing post-incident reviews

Technical

Strong scripting skills in Python, Bash, or Node.js for automation and tooling

Proficiency with Git, branching strategies, and version control best practices

Understanding of API-based integration patterns (REST, SOAP, event-driven) as they apply to MuleSoft, Workato, or similar iPaaS tools

Hands-on familiarity with secrets management across GCP Secret Manager and AWS Secrets Manager; strong grasp of security best practices in CI/CD contexts (least-privilege IAM, no hardcoded credentials, key rotation)

Monitoring and observability tooling experience (Datadog, Splunk, Google Cloud Monitoring, CloudWatch, or similar)

Infrastructure-as-code proficiency — Terraform or CloudFormation; experience applying IaC to Cloud Run or Lambda-based workloads preferred

Familiarity with container security practices: base image pinning, vulnerability scanning (e.g., Artifact Registry, Trivy), and dependency management for AI-generated codebases

Operational & Mindset

Ownership: Takes full accountability for platform uptime, deployment quality, and the operational health of GTM infrastructure end-to-end.

Velocity: Operates with a bias for action — ships iteratively, reduces toil systematically, and keeps release cycles fast and predictable.

AI & Automation mindset: Actively leverages AI-assisted tooling and automation to accelerate delivery, improve reliability, and reduce manual overhead across the GTM stack.

Collaboration: Fluent cross-functional partner to developers, architects, Security, IT, and business stakeholders — enables autonomy without becoming a bottleneck.

Growth: Continuously evaluates new tooling, patterns, and practices to raise the bar on platform engineering and developer experience.

Salesforce: Core GTM CRM platform; this role owns deployment pipelines, environment management, and release reliability across orgs.

NetSuite: ERP platform integrated into the quote-to-cash flow; managed via SuiteScript/SuiteBundler within CI/CD standards.

MuleSoft (Anypoint Platform): Integration middleware; this role maintains deployment pipelines and observability for MuleSoft-managed flows.

Workato: iPaaS automation platform; monitored and deployed within the broader GTM reliability framework.

GCP (Cloud Run, Cloud Scheduler, Secret Manager, Artifact Registry, GCS): Primary cloud platform for AI agent workloads and integration microservices.

AWS (Lambda, S3, EventBridge, Secrets Manager, Bedrock AgentCore): Secondary cloud environment for agent hosting and integration operations.

GitHub Actions / GitLab CI / Copado: CI/CD tooling used to build, validate, and promote changes across GTM platforms.

Terraform / CloudFormation: IaC tooling for repeatable, auditable infrastructure provisioning.

Datadog / Splunk / Google Cloud Monitoring / CloudWatch: Observability and alerting stack across GTM and cloud workloads.

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