Release Automation Engineer

Origami Risk delivers single-platform SaaS solutions that help organizations best navigate the complexities of risk, insurance, compliance, and safety management. The Release Automation Engineer owns the engineering of the release pipeline, focusing on building, hardening, and improving automated systems for production deployment.

Responsibilities:

• Designs, builds, and owns the CI/CD pipeline infrastructure across the Engineering organization — engineering reliable, fast, and maintainable pipelines in Azure DevOps and GitHub Actions that carry artifacts from code commit through build, test, security scan, and deployment with consistency and precision
• Develops and maintains reusable pipeline templates, shared task libraries, and parameterized pipeline components that allow engineering teams to adopt standardized CI/CD patterns without rebuilding pipeline logic from scratch for every service
• Engineers automated build, test, and packaging stages that produce verified, traceable artifacts, ensuring every artifact that enters the promotion workflow has a complete, auditable build provenance and has passed all required automated quality gates
• Implements and maintains pipeline caching, parallelization, and optimization strategies that minimize build and test cycle times, ensuring pipeline speed never becomes a bottleneck to engineering delivery velocity
• Owns pipeline reliability, monitoring pipeline health, diagnosing and resolving flaky tests and intermittent failures, and driving root cause elimination so that pipeline failures reflect genuine code or configuration problems rather than infrastructure noise
• Engineers the automated artifact promotion workflow across development, QA, staging, and production environments, implementing promotion logic that enforces defined quality gates, approval requirements, and compliance checks without requiring manual intervention for standard release scenarios
• Builds automated release gates that integrate quality signals from multiple sources, including unit test results, integration test outcomes, SAST/DAST security scan findings, code coverage thresholds, and performance benchmarks, blocking promotion automatically when criteria are not met
• Automates Flyway database migration execution within the promotion pipeline, engineering reliable, sequenced migration runs with pre-migration validation, execution monitoring, and automated rollback triggers that protect environments from failed schema changes
• Integrates LaunchDarkly feature flag operations into the automated release workflow, engineering pipeline steps that provision, configure, and validate feature flag states as part of the promotion process, enabling controlled progressive delivery without manual flag management during deployments
• Builds automated rollback capabilities into the release pipeline, engineering trigger conditions, rollback execution logic, and post-rollback validation steps that enable rapid, reliable recovery from failed deployments without requiring manual intervention under pressure
• Actively uses AI development tools, including GitHub Copilot, Claude Code and Cursor, to accelerate pipeline engineering work: generating pipeline-as-code, writing automation scripts, building test harnesses, and producing integration logic that connects release tooling across the stack
• Uses AI to analyze pipeline failure patterns at scale, applying AI-assisted log analysis and pattern recognition to identify recurring failure categories, flaky test root causes, and systemic pipeline weaknesses that manual review would take days to surface
• Leverages AI to generate and maintain pipeline documentation, runbooks, and change impact analysis, keeping release infrastructure documentation current with reduced manual overhead as the pipeline evolves
• Evaluates AI-native pipeline intelligence tools, including AI-assisted deployment risk scoring, intelligent change impact analysis, and LLM-powered release note generation, and drives adoption of those that meaningfully improve release quality and operational confidence
• Contributes to team best practices around AI-assisted pipeline engineering, sharing effective automation patterns, AI tooling techniques, and lessons learned that raise the automation capability of the broader Engineering Systems team
• Engineers infrastructure automation that supports the release pipeline, including environment provisioning, configuration validation, secrets management, and post-deployment environment state verification, using Terraform and AWS CDK to ensure environments are consistently and repeatably configured
• Builds and maintains automated environment health checks and pre-deployment readiness validations that confirm target environments are in the expected state before promotion proceeds, eliminating a class of deployment failures caused by environmental drift
• Automates secrets injection, environment variable management, and configuration promotion across environments, ensuring sensitive configuration is handled securely within the pipeline without manual credential handling at deployment time
• Partners with the Director of DevOps / Cloud Operations to ensure release automation integrates cleanly with cloud infrastructure provisioning workflows, enabling fully automated environment creation and teardown for ephemeral test and staging environments
• Implements container image build, tagging, scanning, and promotion automation, ensuring containerized services move through the release pipeline with consistent, traceable image provenance and automated vulnerability scanning before production deployment
• Instruments the release pipeline with comprehensive observability, building dashboards and alerting that provide real-time visibility into pipeline health, deployment success rates, gate failure trends, promotion cycle times, and rollback frequency
• Tracks and reports DORA metrics, deployment frequency, lead time for changes, change failure rate, and mean time to recovery, using pipeline telemetry data to give engineering leadership an accurate, data-driven picture of delivery performance
• Continuously identifies and eliminates automation gaps within the release workflow, partnering with the Release Workflow Manager to convert manual release steps into automated pipeline logic and systematically reducing the human touch points required to ship code
• Conducts regular pipeline health retrospectives, analyzing failure trends, bottleneck patterns, and engineer friction points, producing prioritized improvement backlogs and driving implementation of the highest-impact automation investments
• Benchmarks pipeline performance over time, tracking build and deployment cycle time trends and setting measurable improvement targets that keep release velocity improving as the Engineering organization and codebase scale
• Defines and maintains pipeline engineering standards for the Engineering organization, including pipeline-as-code conventions, branching and tagging strategies, artifact versioning standards, and deployment pattern guidelines that ensure consistency and auditability across all teams
• Partners with the Engineering Systems Manager to ensure CI/CD pipeline configurations are governed within Azure DevOps and GitHub with appropriate access controls, change tracking, and audit trails that support SOC 2, ISO/IEC 27001, and NIST 800-53 compliance requirements
• Partners with the Product Security team to integrate SAST, DAST, SCA, and container scanning into the release pipeline as mandatory, automated security gates, ensuring security findings are surfaced and enforced within the pipeline rather than discovered post-deployment
• Collaborates with Engineering teams as the subject matter expert on CI/CD and release automation, providing consultation on pipeline design, troubleshooting pipeline issues, and building the Engineering organization's collective capability around automated delivery
• Maintains thorough pipeline-as-code documentation, architecture diagrams, and runbooks that give the engineering organization a clear, accurate, and current picture of how the release pipeline works and how to operate it

Requirements:

• Bachelor's Degree in Computer Science, Software Engineering, or a related field required
• Professional experience in CI/CD pipeline engineering, release engineering, DevOps, or platform engineering with a primary focus on release automation
• Hands-on expertise with Azure DevOps Pipelines and GitHub Actions — including pipeline-as-code authoring, reusable template design, and multi-stage pipeline configuration for build, test, and deployment workflows
• Strong software engineering fundamentals — able to write clean, maintainable automation code and scripts in languages such as Python, PowerShell, Bash, or equivalent
• Experience building automated artifact promotion workflows with multi-environment gate logic, approval integrations, and automated rollback capabilities
• Familiarity with container build and deployment automation, including Docker image build pipelines, image scanning integration, and container registry management
• Experience with infrastructure-as-code tooling such as Terraform or AWS CDK for environment provisioning and configuration automation
• Demonstrated use of AI tools to accelerate pipeline engineering, automation scripting, or release infrastructure work
• Strong analytical and troubleshooting skills — able to diagnose pipeline failures, trace automation breakdowns across complex multi-stage workflows, and drive systematic root cause elimination
• Experience integrating database migration tooling such as Flyway into CI/CD pipelines, including automated migration execution, validation, and rollback logic
• Experience integrating feature flag platforms such as LaunchDarkly into release pipeline automation, including flag provisioning and state validation steps
• Familiarity with AWS-native deployment services including CodePipeline, CodeDeploy, ECS deployment automation, and Lambda deployment patterns
• Experience integrating SAST, DAST, SCA, and container security scanning tools into CI/CD pipelines as automated quality and security gates
• Familiarity with DORA metrics and experience using pipeline telemetry data to measure and improve delivery performance
• Experience with AI-native pipeline intelligence tools, including AI-assisted deployment risk scoring or LLM-powered release analysis
• Experience working within compliance frameworks such as SOC 2, ISO/IEC 27001, or NIST 800-53 in the context of pipeline access controls and release audit trail requirements
• A personal conviction that a great release pipeline is one that engineering teams trust completely — because it has never let them down