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 The Fedcap Group.
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 Automation & AI Enablement
- Govern API standards and modern integration architecture patterns across systems.
• Replace legacy point-to-point integrations with scalable API-first models.
• Expand workflow automation and RPA initiatives with measurable operational impact.
• Partner with Data & Analytics to embed predictive models and AI-enabled insights into operational systems.
• Ensure responsible, secure, and governed implementation of AI-enabled application features.
• Maintain disciplined oversight of automation and AI experimentation to prevent fragmentation.
Application Security & Compliance Alignment
- Embed secure coding standards and shift-left security practices.
• Partner with Security to maintain strong vulnerability remediation performance.
• Ensure engineering alignment with HIPAA, SOC 2 Type II, ISO 27001, and other required control frameworks.
• Support audit evidence production for application development controls.
• Ensure identity and role-based access enforcement is consistently implemented in application design.
Acquisition Integration & Expansion Enablement
- Lead application and integration due diligence assessments for acquisitions.
• Execute standardized application integration during entity onboarding.
• Ensure engineering readiness for geographic expansion and new program launches.
• Support enterprise system consolidation aligned with modernization strategy.
Success Metrics (First 12 Months)
- Measurable advancement in engineering maturity, deployment reliability, and release predictability across enterprise applications.
• Reduction of prioritized technical debt while maintaining operational stability.
• Expansion of intelligent automation and AI-enabled capabilities with documented operational impact.
• Standardized integration architecture adopted across major enterprise platforms.
• Demonstrated financial discipline through optimized engineering spend and justified build-versus-buy decisions.
• Engineering organization strengthened across onshore, nearshore, and offshore teams to support modernization and enterprise scale.
Qualifications
- 10+ years of progressive leadership in software engineering and enterprise application environments.
• Demonstrated expertise in secure SDLC advancement, DevOps optimization, and API-first architecture.
• Experience leading distributed onshore, nearshore, and offshore engineering teams.
• Experience implementing automation and AI-enabled capabilities within enterprise systems.
• Experience modernizing legacy systems in distributed, multi-entity organizations.
• Experience operating in regulated environments.
• Proven ability to scale engineering capabilities while maintaining financial discipline.
• Strong executive communication and cross-functional leadership skills.
Leadership Profile
The ideal candidate will:
- Lead with engineering discipline and innovation orientation.
• Balance speed, quality, scalability, compliance, and cost efficiency.
• Build structured governance while fostering a culture of modernization and creativity.
• Drive responsible AI-enabled transformation aligned with enterprise strategy.
• Operate as a strategic partner within an enterprise governance model.
Mission & Stewardship Commitment
- Ensure enterprise applications protect client data and enable intelligent decision-making.
• Advance engineering capabilities that directly strengthen frontline impact.
• Promote responsible innovation and ethical AI implementation.
• Deliver secure, scalable, and financially responsible systems that support mission growth across all operating entities.
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