Head of Software Engineering

Position Summary The Head of Software Engineering serves as the enterprise leader for application engineering strategy, systems integration architecture, DevOps execution, automation, and AI-enabled solution development across Nimbus Data Systems . This role advances and scales an established engineering function to ensure enterprise-developed applications, integrations, and intelligent automation capabilities are secure, scalable, innovative, compliant, and aligned with enterprise architecture and governance standards across a geographically distributed organization. As the organization continues to expand across regions and service lines, the Head of Software Engineering will accelerate modernization, reduce technical fragmentation, embed AI-driven capabilities into enterprise workflows, and position engineering as a strategic driver of operational excellence and innovation. Reporting to the SVP, Enterprise Systems & Digital Platforms, this leader partners closely with IT Infrastructure & Operations, Security, Data & Analytics, and operating leadership to ensure engineering practices are standardized, performance-driven, resilient, financially disciplined, and aligned with enterprise governance requirements. Mission To deliver secure, scalable, intelligent, and cost-effective enterprise applications that strengthen mission delivery, enable automation and data-driven decision-making, reduce system fragmentation, and support sustainable organizational growth. Scope of Accountability The Head of Software Engineering is accountable for: Enterprise software development standards and advanced SDLC governance • Application architecture patterns and approved engineering frameworks • API and enterprise integration architecture standards • DevOps pipelines, release automation, and engineering productivity optimization • Application performance monitoring and operational observability standards • Technical debt prioritization and modernization acceleration • Automation platforms (RPA, workflow engines, low-code governance) • AI-enabled application capabilities embedded within enterprise systems • Intelligent workflow and decision-support integration in collaboration with Data & Analytics • Engineering documentation and configuration discipline • Secure coding practices and vulnerability remediation coordination • Application lifecycle management and decommissioning governance • Engineering vendor lifecycle management • Engineering budget management and cost discipline • Build-versus-buy analysis and financial justification of custom development initiatives • Engineering capacity planning, delivery forecasting, and productivity optimization • Application components of acquisition integration and enterprise platform consolidation • Implementation of security control requirements • Leadership of distributed onshore, nearshore, and offshore engineering teams Core Responsibilities Application Strategy, Architecture & Innovation Define and evolve enterprise application architecture standards. • Establish consistent development frameworks and approved technology stacks. • Lead modernization of legacy applications while enhancing scalability and maintainability. • Identify and prioritize opportunities to embed automation and AI-enabled decision support within enterprise workflows. • Drive rationalization of redundant custom solutions across business units. • Evaluate emerging technologies and pilot innovation initiatives aligned with enterprise strategy. Engineering Portfolio & Demand Governance Establish structured intake and prioritization processes for engineering initiatives. • Align development roadmaps with enterprise portfolio governance and strategic objectives. • Ensure engineering resources are allocated to the highest-value initiatives. • Prevent proliferation of unauthorized, redundant, or non-strategic custom solutions. • Partner with Systems leadership to ensure build-versus-buy decisions are financially and strategically justified. Software Development Lifecycle (SDLC) & Engineering Maturity Advance existing SDLC governance to improve consistency, automation, and measurable quality outcomes. • Optimize requirements management, code review, testing, and documentation standards. • Govern version control, branching strategies, and release management protocols. • Maintain formal Dev/Test/Production controls with disciplined change management. • Ensure audit-ready engineering documentation aligned with compliance requirements. DevOps, Release Management & Engineering Productivity Mature CI/CD pipelines to enhance deployment reliability and scalability. • Introduce measurable engineering productivity and quality benchmarks. • Implement AI-assisted development tools where appropriate to enhance developer efficiency and code quality. • Define application monitoring, logging, and observability standards. • Improve deployment consistency while enabling faster innovation cycles. Enterprise Integration, Intelligent Automati