Exec Director -BI, Data Platforms, and Applied AI

Excelsior•Albany, NY

74d

About The Position

The Executive Director of Data Engineering and Applied AI is a senior IT leader responsible for designing, developing, and executing Excelsior University’s enterprise data strategy. This includes the architecture, engineering, and operation of data platforms, integration frameworks, business intelligence solutions, and applied AI capabilities—ensuring alignment with institutional goals, compliance requirements, and the University’s evolving needs. Working in close partnership with senior leadership, key institutional stakeholders, and the Analytics & Data Science (ADS) team, the Executive Director leads the Data Engineering team in delivering integrated and high-performing data services that enable data-informed decision-making, operational excellence, and innovation across the University. A central focus of the role is designing, developing, and maintaining secure, scalable, and modern data and analytics systems using sound engineering methods. The Executive Director provides engineering oversight for enterprise software, data integration, and AI initiatives—ensuring the effective design, testing, and implementation of data pipelines, APIs, and analytical platforms that support institutional priorities. Additionally, this position leads the design, development, and governance of AI-enabled systems and applications, ensuring alignment with foundational IT infrastructure, data governance practices, and ethical AI standards.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Information Systems, Data Engineering, or a related field (Master’s degree preferred).
10+ years of experience in data engineering, business intelligence, or enterprise data management, including 5+ years in a leadership role with system and software design oversight.
Proven experience designing and developing enterprise data systems, integrations, and analytics platforms using modern engineering principles.
Strong technical expertise in data architecture, APIs, middleware, and cloud-native solutions (experience with Microsoft Fabric highly desirable).
Experience with SaaS enterprise systems (e.g., Ellucian Banner, Salesforce) and enabling their reporting, analytics, and AI functionalities through secure integrations.
Knowledge of applied AI/ML technologies, including embedded and standalone capabilities within enterprise data environments.
Strong understanding of engineering best practices, including DevOps, CI/CD, version control, automated testing, and secure software development.
Demonstrated experience implementing data governance and compliance frameworks, ideally within higher education.

Nice To Haves

Prior experience in higher education or a similarly complex, data-intensive environment is highly desirable.

Responsibilities

Lead the definition, engineering, and execution of the University’s enterprise data strategy, aligning data architecture, platforms, and AI capabilities with institutional goals and digital transformation initiatives.
Design, develop, and maintain robust data and analytics systems, ensuring scalability, security, and performance through modern engineering practices.
Oversee the Data Engineering team, responsible for enterprise data integration, warehousing, BI reporting, and advanced analytics capabilities.
Engineer and sustain a secure, scalable data-as-a-service environment to support operational reporting, advanced analytics, and AI workloads.
Provide technical leadership in software and systems engineering, including integration architecture, API design, and data pipeline development for enterprise systems such as Banner SaaS and Salesforce.
Oversee the full engineering lifecycle of data and analytics initiatives—from requirements analysis and system design through development, testing, deployment, and continuous improvement.
Collaborate with the ADS team to support advanced analytics, machine learning, and AI model integration within the enterprise data ecosystem.
Lead the engineering and governance of applied AI solutions, ensuring infrastructure, data readiness, and policies support responsible and effective AI adoption.
Establish and maintain data governance frameworks that ensure data quality, security, compliance, and accessibility across institutional systems.
Stay current with emerging trends in data engineering, BI, AI, and integration technologies to continually refine and modernize the University’s data strategy.
Foster a collaborative, high-performing engineering culture focused on innovation, service excellence, and continuous improvement.