DevOps Professional

• Design, build, and support AWS-based development and QA environments, with all development infrastructure provisioned and managed through Infrastructure as Code using Terraform, CloudFormation, or equivalent technologies.

• Define reusable IaC modules, self-service provisioning patterns, configuration standards, and automated environment lifecycle controls.

• Design and support Kubernetes platforms; develop, version, and govern reusable Helm charts for application configuration, packaging, deployment, upgrade, and rollback.

• Lead the migration of legacy Jenkins workflows to GitHub Actions and build Harness-based deployment pipelines with automated testing, security controls, quality gates, release governance, and rollback capabilities.

• Establish database DevOps practices using Liquibase, Flyway, or similar technologies; integrate versioned schema migrations, validation, and rollback controls into CI/CD pipelines.

• Create and automate client QA environments, including legacy blueprints, representative test data, scheduled maintenance-release testing, and repeatable configuration.

• Lead Jira and Confluence migrations and modernization, including content and space migration, permissions, integrations, governance, and engineering knowledge-management standards.

• Migrate application artifacts and container images into enterprise Artifactory; establish package, dependency, versioning, and retention standards, including Conan where applicable.

• Implement centralized logging, monitoring, alerting, and operational dashboards using Splunk and related technologies.

• Enable AI-assisted engineering workflows, including automated defect routing, story and task support, and pipeline productivity improvements across repositories.

The successful candidate will bring 7+ years of experience in DevOps, Platform Engineering, Site Reliability Engineering, Cloud Engineering, or a comparable role, along with strong hands-on expertise across AWS, Kubernetes, Docker or containerization, Helm, and Infrastructure as Code. The role requires practical experience with Terraform and/or CloudFormation; enterprise CI/CD using GitHub Actions, Jenkins, and deployment automation; database migrations using Liquibase, Flyway, or similar technologies; and artifact repositories, container registries, package management, and observability platforms such as Artifactory and Splunk.

Experience with Harness, GitOps practices, Jira and Confluence migrations, Conan, AI-assisted software engineering, and financial services, capital markets, or post-trade processing platforms is highly valuable. Strong scripting, troubleshooting, communication, stakeholder management, and technical leadership skills are essential.

• Fully automated, repeatable development and QA infrastructure managed through code.

• Standardized Kubernetes deployments through governed Helm charts and reusable platform patterns.

• Modern CI/CD pipelines with integrated application, database, testing, security, and rollback controls.

• Successful Jira, Confluence, artifact repository, and delivery-tool migrations.

• Improved observability, operational readiness, engineering productivity, and release reliability.

The successful candidate will bring 7+ years of experience in DevOps, Platform Engineering, Site Reliability Engineering, Cloud Engineering, or a comparable role, along with strong hands-on expertise across AWS, Kubernetes, Docker or containerization, Helm, and Infrastructure as Code. The role requires practical experience with Terraform and/or CloudFormation; enterprise CI/CD using GitHub Actions, Jenkins, and deployment automation; database migrations using Liquibase, Flyway, or similar technologies; and artifact repositories, container registries, package management, and observability platforms such as Artifactory and Splunk.

Experience with Harness, GitOps practices, Jira and Confluence migrations, Conan, AI-assisted software engineering, and financial services, capital markets, or post-trade processing platforms is highly valuable. Strong scripting, troubleshooting, communication, stakeholder management, and technical leadership skills are essential.

• Fully automated, repeatable development and QA infrastructure managed through code.

• Standardized Kubernetes deployments through governed Helm charts and reusable platform patterns.

• Modern CI/CD pipelines with integrated application, database, testing, security, and rollback controls.

• Successful Jira, Confluence, artifact repository, and delivery-tool migrations.

• Improved observability, operational readiness, engineering productivity, and release reliability.

• Design, build, and support AWS-based development and QA environments, with all development infrastructure provisioned and managed through Infrastructure as Code using Terraform, CloudFormation, or equivalent technologies.

• Define reusable IaC modules, self-service provisioning patterns, configuration standards, and automated environment lifecycle controls.

• Design and support Kubernetes platforms; develop, version, and govern reusable Helm charts for application configuration, packaging, deployment, upgrade, and rollback.

• Lead the migration of legacy Jenkins workflows to GitHub Actions and build Harness-based deployment pipelines with automated testing, security controls, quality gates, release governance, and rollback capabilities.

• Establish database DevOps practices using Liquibase, Flyway, or similar technologies; integrate versioned schema migrations, validation, and rollback controls into CI/CD pipelines.

• Create and automate client QA environments, including legacy blueprints, representative test data, scheduled maintenance-release testing, and repeatable configuration.

• Lead Jira and Confluence migrations and modernization, including content and space migration, permissions, integrations, governance, and engineering knowledge-management standards.

• Migrate application artifacts and container images into enterprise Artifactory; establish package, dependency, versioning, and retention standards, including Conan where applicable.

• Implement centralized logging, monitoring, alerting, and operational dashboards using Splunk and related technologies.

• Enable AI-assisted engineering workflows, including automated defect routing, story and task support, and pipeline productivity improvements across repositories.