Sr Director, Data and Application Engineering

Abacus is a Managed Services Provider serving financial services and healthcare clients, seeking a Sr Director, Data and Application Engineering to lead technical teams. This role involves overseeing DevOps, Application Development, Data Engineering, and Internal AI Development teams to align them with strategic goals and ensure delivery of enterprise-grade platforms.

Responsibilities:

• Partner with the CTO to define and execute the engineering department's strategy across all four practice areas
• Develop and maintain a unified engineering roadmap that balances platform investment, product delivery, and operational improvement
• Establish cross-team priorities, resource allocation, and delivery cadences aligned to departmental goals
• Serve as an escalation point and executive sponsor for critical engineering initiatives
• Oversee the reliability, scalability, and continuous improvement of infrastructure and CI/CD tooling
• Drive the expansion of the DevOps practice into internal automation — identifying and implementing automation opportunities that reduce toil and increase throughput
• Ensure engineering teams operate with modern DevOps practices including infrastructure-as-code, observability, and automated testing
• Own the technical direction and delivery roadmap for the Abacus portal, ensuring it meets evolving business requirements
• Partner with the service delivery team to support ServiceNow development and configuration needs
• Champion engineering best practices including code quality, security, and maintainability across all application development efforts
• Lead the build-out of the organization's enterprise data platform, ensuring it is scalable, governed, and fit for enterprise use
• Ensure data pipelines, storage, and access patterns are designed for reliability and compliance in regulated industries
• Collaborate with business stakeholders to ensure the platform supports analytics, reporting, and AI/ML workloads
• Lead the design, development, and rollout of internal AI agents and automation tools
• Work directly with internal stakeholders to surface efficiency opportunities addressable through AI
• Ensure AI solutions are scalable, secure, and compliant with regulatory requirements (e.g., HIPAA, financial data controls)
• Oversee integration of AI systems with existing platforms and workflows
• Serve as a subject matter expert on AI trends, tools, and best practices relevant to the MSP and regulated services space
• Build, mentor, and develop high-performing engineering teams across all four practice areas
• Establish consistent engineering standards, development practices, and delivery governance
• Foster a culture of accountability, collaboration, innovation, and continuous improvement
• Attract and retain top engineering talent; support career development at all levels
• Define and track KPIs across teams (e.g., deployment frequency, platform uptime, data quality, AI adoption, development velocity)
• Regularly report progress, risks, and outcomes to the CTO and executive leadership
• Ensure all engineering practices meet governance, compliance, and security standards for regulated environments

Requirements:

• 12+ years of experience in software engineering, data engineering, DevOps, or related technical disciplines
• 7+ years in progressive engineering leadership roles, including managing managers or multi-team organizations
• Demonstrated experience overseeing multiple, diverse technical teams simultaneously
• Proven track record of delivering enterprise-scale platforms and applications in production environments
• Experience working in regulated industries such as financial services, healthcare, or similar
• Strong ability to translate business objectives into technical strategy and measurable delivery outcomes
• Excellent executive communication and stakeholder management skills
• Experience within a Managed Services Provider (MSP) or services-based technology organization
• Familiarity with ITSM platforms, particularly ServiceNow development and configuration
• Hands-on background in one or more of: data platform architecture, AI/ML engineering, or DevOps automation
• Experience with AI governance, risk management, and compliance frameworks
• Exposure to enterprise data platform design (e.g., data lakes, lakehouses, data mesh architectures)