Lead, Transformation L2 Application Support

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• Level: Lead individual contributor with no direct reports in the baseline JD structure.
• Functionally aligned to transformation, L2 support, and automation/tooling work across SRE and adjacent operational teams.
• Works closely with L2, L3, ODM, GCC, FR, RTE, SRE, and DBA partners to turn operational pain points into scalable tooling and automation solutions.
   

• Build AI-enabled internal tools that automate operational reviews, troubleshooting, and repetitive engineering tasks.
• Transform recurring support and deployment activities into self-serve, policy-driven, and observable workflows.
• Improve speed, quality, and consistency of engineering delivery by embedding automation into day-to-day workflows.

• →Design, build, and maintain AI automations for deployment readiness review, failed deployment analysis, and incident diagnosis.
• →Develop internal applications, scripts, and services that eliminate manual L2 and SRE tasks and improve operational efficiency
• →Create workflow automations that validate change requirements, check ticket completeness, and enforce operational guardrails before human review.
• →Build and improve tooling for release governance, CAB support, deployment coordination, and structured risk assessment.
• →Convert repeated support issues, troubleshooting patterns, and review checklists into reusable automations, scripts, and knowledge assets.
• →Develop and maintain backend services, APIs, data integrations, and lightweight frontends needed to deliver internal automation products.
• →Use SQL and data access patterns to support diagnostics, extraction, transformation, validation, and operational analysis.
• →Strengthen delivery pipelines, quality gates, observability, and monitoring so automations are reliable and production-ready.
• →Troubleshoot issues across application, backend, deployment, and workflow layers, then drive permanent fixes through tooling and automation.
• →Collaborate with engineers, support teams, and operational stakeholders to prioritize high-impact automation opportunities and deliver usable solutions.

• Reduction in repetitive manual effort through AI and workflow automation.
• Number of meaningful AI-enabled improvements adopted by the squad or broader team.
• Improved delivery speed and predictability for intake-approved work.
• Lower deployment revert rates and fewer production defects attributable to owned changes.
• Better engineering quality through stronger tests, cleaner code, and improved observability.

• Strong hands-on software engineering capability across backend, scripting, and internal tooling development.
• Strong Python proficiency for automation, services, and process optimization.
• Strong SQL capability for data analysis, troubleshooting, and query optimization.
• Working knowledge of JavaScript, HTML, CSS, and modern web frameworks for internal UI development when needed.
• Experience building or consuming REST APIs and integrating third-party or cross-platform services.
• Familiarity with cloud environments, containerized workloads, and modern deployment patterns.
• Working knowledge of CI/CD, source control, and release engineering practices.
• Strong troubleshooting, analytical, and cross-functional communication skills suited to operational environments.
• Demonstrated ability to apply AI and automation tools in daily engineering work to improve speed, consistency, and quality.

• Experience in L2 application support, SRE-adjacent operations, or production engineering environments where automation can materially reduce toil.
• Experience building internal tools for deployment governance, incident handling, troubleshooting, or operational reporting.
• Experience turning ambiguous operational pain points into practical, low-friction tools adopted by engineers and support teams.