Manager – Tech Management

Requirements

• Bachelor’s or Master’s degree in information technology, computer science, or a related field.
• ITIL certification preferred; certifications in Agile/Scrum or PMP is preferred.
• 9-12 years of IT experience with at least 5 years in an IT operations leadership or service delivery role.
• Proven experience managing global operations, incident resolution, change and release management, and team leadership in a GCC setup.
• Familiarity with ITSM tools (e.g., ServiceNow), SLAs, and cloud/hybrid infrastructure monitoring.
• Strong knowledge of IT operations, infrastructure management, and enterprise application support.
• Expertise in incident, change, problem, and release management frameworks and practices.
• Experience leading geographically distributed teams in 24x7 support environments.
• Experience with Agile/Scrum, SRE, AIOps, DevOps, FinOps models, and automation practices.
• Strong communication, crisis management, and stakeholder management skills.
• Proven expertise in managing enterprise-grade Linux environments, including system performance, patching, and automation.
• Strong command over Snowflake architecture , data warehousing concepts, and performance optimization, with experience in managing data pipelines and governance.
• Ability to assess operational health and drive continuous improvement initiatives for stability and performance.
• Experience managing support teams for SAP, cloud-based (GCP), or custom-built platforms.
• Experience in managing end-to-end data integration and transformation using SAP BODS and MuleSoft, ensuring seamless connectivity across heterogeneous systems.
• Working knowledge of monitoring and alerting tools, infrastructure scripting, or observability platforms.
• Exposure to vendor management and service contracts.
• Ability to manage escalations, executive reporting, and audit / regulatory compliance for support functions.
• Strategizes adoption of AI OPS to drive intelligent automation across IT operations.
• Guides teams in operationalizing ML models with a focus on governance and scalability.