Sr Director, Data and Application Engineering

Abacus is a Managed Services Provider (MSP) serving financial services and healthcare clients. They are seeking a highly strategic and execution-focused Sr Director, Data and Application Engineering to lead technical teams in DevOps, Application Development, Data Engineering, and Internal AI Development, ensuring alignment with the organization's strategic goals.

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)