Salesforce DevOps Release Engineer

Avalara•UNAVAILABLE, UNAVAILABLE

1d•Remote

About The Position

As a DevOps and Release Engineer, you'll lead the release management and deployment pipeline across our Salesforce, Boomi, and AWS environments, ensuring changes move safely and predictably from development through production. You'll partner with Salesforce developers, integration engineers, and cloud infrastructure teams to build guardrails that catch breaking changes before they reach customers. This role is critical to reducing production incidents caused by uncoordinated changes across interconnected systems. This is a remote role reporting to the Senior Manager of Business Technology.

Requirements

B.S. in Computer Science, Engineering, Information Systems, or a related technical field, or equivalent practical experience.
8+ years of experience in DevOps, release engineering, or a similar role supporting enterprise environments
Hands-on experience with Salesforce CLI, change sets, and Salesforce metadata deployments
Proficiency with GitLab CI/CD pipelines and Terraform for infrastructure provisioning
Experience managing release processes across multiple interconnected systems (Salesforce, Boomi, AWS)
Troubleshoot with the ability to resolve deployment failures
Collaborate to work with multiple engineering teams
Experience implementing release governance practices such as approval gates, dependency checks, and rollback procedures
Experience applying AI-powered tools and automation (e.g., GitHub Copilot, AI-assisted code reviews, or AI-driven testing frameworks) to improve deployment efficiency and reduce manual effort
Familiarity with Boomi or similar integration platforms and their deployment processes
Experience with Salesforce DevOps Center or third-party Salesforce release management tools (Copado, Gearset, or Flosum)
Knowledge of AWS CloudFormation, CDK, or other infrastructure-as-code tooling beyond Terraform
Exposure to SRE practices, incident management frameworks (e.g., Firehydrant, Opsgenie), and observability platforms (e.g., Datadog, Splunk)
Relevant certifications such as AWS DevOps Engineer, Salesforce Administrator/Developer, or HashiCorp Terraform Associate
You’ll bring experience using AI and AI-related technologies, ready to thrive here.
You’ll apply AI every day to business challenges - improving efficiency, contributing solutions, and driving results for your team, our company, and our customers.
You’ll grow with AI by staying curious about new trends and best practices, and by sharing what you learn so others can benefit too.

Responsibilities

Review and validate Salesforce, Boomi, and AWS deployment requests, ensuring you check change sets for dependency conflicts before promoting them between environments.
Develop CI/CD pipelines using Salesforce CLI, change sets, and GitLab for Salesforce deployments, alongside Terraform for AWS infrastructure provisioning.
Troubleshoot deployment failures with engineering teams and maintain automated rollback procedures to minimize production impact.
Build monitoring dashboards to ensure deployment issues are identified and addressed before they affect customers.
Establish and enforce release governance standards including approval gates, dependency checks, environment promotion criteria, and change advisory board (CAB) processes.
Define and report on important deployment metrics — deployment frequency, change failure rate, mean time to recovery (MTTR), and lead time for changes — to improve release performance.
Partner with security and compliance teams to ensure all deployments meet organizational controls, audit requirements, and regulatory standards.
Develop runbooks, deployment strategies, and operational documentation to ensure repeatability and knowledge transfer across engineering teams.
Hold standards for release quality, deployment reliability, automation across Salesforce, Boomi, and AWS environments.
Create a culture of "automation first" by eliminating manual deployment activities, reducing operational risk, and improving deployment velocity through scalable CI/CD practices.
Use data, operational metrics, and production insights — not assumptions — to improve deployment success rates, release predictability, change failure rates, and recovery times.
Simplify complex cross-platform deployment workflows into standardized, repeatable, and release processes that scale with business growth.
Establish and improve engineering guardrails, including dependency validation, release governance, automated testing, approval gates, rollback strategies, and deployment observability.
Mentor developers, integration engineers, and platform teams on DevOps best practices, release discipline, Infrastructure as Code, and to raise engineering across the organization.
Challenge existing deployment practices respectfully and driving continuous improvements that reduce technical debt, improve system reliability, and strengthen collaboration.
Stay current with modern DevOps, platform engineering, cloud infrastructure, and AI-assisted engineering capabilities, introducing practical innovations that improve developer productivity while maintaining security and governance.
Leave every deployment pipeline, release process, operational framework, and engineering standard stronger and easier to operate than before.
Identify and adopt AI-powered tools that improve deployment pipeline efficiency, such as AI-assisted code review, intelligent test selection, anomaly detection in deployment telemetry, and predictive change risk scoring.
Champion the use of AI and automation to eliminate repetitive, error-prone manual release activities. Use tools like GitHub Copilot, AI-assisted runbook generation, or LLM-driven incident summarization to accelerate engineering workflows.
Evaluate and pilot new AI-native DevOps capabilities (e.g., AI-powered observability, automated root cause analysis, intelligent rollback triggers) and bring well-reasoned recommendations to the team.
Model responsible AI use — applying AI tools with appropriate governance, validating AI-generated outputs, and maintaining human accountability for all production decisions.
Mentor peers on practical AI tool usage within the DevOps lifecycle, helping the broader team develop AI fluency and reducing resistance to adopting AI-assisted workflows.
Improve for how AI amplifies engineering capacity — not as a novelty, but as a measurable lever for deployment reliability, developer productivity.