Platform Tools Engineer- Jira Admin

Job description

**Role Summary** The DevOps Tools Engineer will own and evolve the organisations SDLC tooling ecosystem, driving standardisation, AI enablement, and measurable improvements in engineering productivity and efficiency. This role acts as a bridge between transformation, engineering, and project management teams, ensuring SDLC tools are optimized, integrated, and aligned with the organisation’s AI-enabled software development strategy. The position requires a product mindset, treating tools as platforms that enable scalable, efficient, and compliant ways of working across teams. **Required Information** Role -DevOps Tools Engineer Experience Range -5-10 years of experience Skill Required- - Proven experience administering Atlassian suites (Jira, Confluence, Jira Product Discovery, Service Management) in a large enterprise environment. - Strong experience configuring, customizing, automating, and integrating SDLC tools. - Experience driving AI adoption, automation, and productivity improvements in engineering workflows. - Strong understanding of DevOps principles and Agile practices. - Data-driven mindset with focus on measurable outcomes (KPIs, dashboards). - Experience in Jira setup (company-managed and team-managed projects), including configuration and integrations. - Strong stakeholder management and cross-functional collaboration skills. - Ability to quickly adapt to new tools and technologies. - Ability to operate in a fast-paced environment and manage multiple priorities. - Mindset focused on continuous improvement and innovation. Tool Set needed for the role - - Hands-on expertise with Atlassian suite (Jira, Confluence, Jira Product Discovery, Service Management) including setup, administration, and governance. - Experience integrating SDLC tools with CI/CD platforms (e.g. GitHub), cloud platforms (e.g. Azure), and collaboration tools (Slack, Miro). - Familiarity with AI capabilities within SDLC tools. - Experience defining tool architecture, governance, and optimization strategies for SDLC platforms. - Experience building dashboards and reporting to track SDLC KPIs and performance. **Key Responsibilities** **SDLC Platform Ownership &** **Standardization** - Own and evolve SDLC tooling as a platform across the organization. - Define standardized workflows, templates, and best practices across engineering teams. - Manage tooling roadmap based on business and engineering priorities. - Treat SDLC tooling as a product, managing backlog, roadmap, and continuous value delivery. **AI-Driven Efficiency & Automation** - Drive adoption of AI capabilities across SDLC tools - Identify and implement automation opportunities to reduce manual effort. - Establish governance and guardrails for AI usage (compliance, data privacy). **Transformation Enablement** - Enable DevOps and SDLC transformation by aligning tools, processes, and ways of working across teams. - Collaborate with transformation, engineering, and project management teams to understand requirements and deliver scalable solutions. **Integration & End-to-End Visibility** - Integrate SDLC tools with CI/CD pipelines, cloud environments, and service management tools. - Enable visibility across full delivery lifecycle (idea build deploy operate). **Governance, Security & Compliance** - Define access control, governance models, and audit mechanisms for tools. - Ensure compliance with organizational security and data policies. **Stakeholder Collaboration** - Collaborate with transformation, engineering, project management, Cloud Platform, and Cybersecurity teams. - Act as a strategic advisor on tooling strategy and optimization. **Adoption & Enablement** - Provide training, documentation, and onboarding for users. - Drive adoption of tools and standardized practices across teams. **Continuous Improvement** - Continuously improve tools and processes based on feedback and metrics. - Stay updated with latest features and industry best practices. **Key Outcomes / KPIs** - Reduction in cycle time and improved delivery efficiency. - Increased adoption of standardized workflows. - Higher automation coverage across SDLC processes. - Measurable productivity gains through AI adoption. - Improved visibility and traceability across the SDLC lifecycle