Platform Tools Engineer- Jira Admin

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