AI Data Engineer

Job Description

The AI Data Engineer leverages AI-assisted tools (e.g., code generation, chat-style assistants, agentic workflows) to accelerate pipeline development, documentation, and problem-solving that transforms structured and unstructured data into enterprise-ready assets to power AI and analytics solutions. Built on Microsoft Fabric, Syracuse University’s standard data and analytics platform, this role bridges raw data sources with generative AI applications across OneLake, ensuring data quality, compliance, and scalability. Joining an established Enterprise Data & AI team already building in Fabric, the AI Data Engineer serves as a key technical contributor to large-scale, university-wide initiatives, both current and emerging, that advance institutional strategy. At times, the incumbent may be dedicated to specific, university initiatives or partner units based on institutional priorities and cross-departmental projects. Education and Experience

• Bachelor’s degree in Artificial Intelligence, Computer Science, Data Science, or related field, or equivalent combination of education and experience.
• 4+ years of experience in data engineering, AI/ML integration, or enterprise IT.
• Experience in a higher education IT environment preferred.

Skills and Knowledge

• Expertise in data wrangling for structured and unstructured data.
• Proficiency in SQL and at least one programming language (Python, Java, or C#).
• Experience with Microsoft Fabric (OneLake, Data Factory, Real-Time Intelligence, Fabric IQ, notebooks) and comparable data platforms
• Proficiency with Power BI and semantic modeling (data modeling, relationships, DAX, and OneLake-integrated semantic models) to enable trusted self-service analytics.
• Familiarity with API development, microservices, and Model Context Protocol (MCP) integrations.
• Experience with cloud infrastructure (Azure, AWS, GCP), containerization (Docker, Kubernetes), and serverless platforms (e.g., Logic Apps).
• Familiarity in deploying AI/ML platforms (Azure AI Foundry, Google Vertex, Amazon Bedrock, OpenAI, etc.).
• Demonstrated familiarity using generative AI tools to improve personal and team productivity.
• Understanding of data governance, privacy, and ethical AI principles.
• Strong problem-solving, analytical, and collaboration skills.
• Excellent written and verbal communication skills, including the ability to translate complex technical concepts for non-technical audiences and to deliver presentations, demonstrations, and briefings to campus stakeholders and leadership.

Responsibilities Data Engineering & Pipeline Development
Design and implement scalable pipelines that ingest, clean, transform, and aggregate data from diverse sources (ERP, LMS, research systems, APIs, and external datasets) into formats optimized for AI and analytics. Ensure data quality, integrity, and reproducibility through robust engineering practices.

Integration & Platform Support
Build connectors, workflows, and APIs to unify and operationalize data across cloud and on-premises platforms. Support deployment and lifecycle management of AI/ML models, ensuring seamless integration with enterprise applications.

Governance, Security & Compliance
Maintain metadata, lineage, and documentation to support transparency and auditability. Ensure adherence to Syracuse University’s ISF, FERPA, HIPAA, and other regulatory requirements, while applying ethical AI and data governance principles.

Collaboration & Stakeholder Engagement
Gather and translate requirements from academic and administrative units to deliver tailored solutions aligned with institutional priorities. Represent AI/ML teams in cross-campus meetings, working groups, and governance bodies. Deliver presentations and demonstrations to technical and non-technical audiences.

Innovation & Mentorship
Provide technical mentorship on data engineering and integration best practices. Anticipate and scale for future institutional needs (e.g., MCP, serverless, containerization, and generative AI) to drive innovation in data and AI adoption. Physical Requirements Not Applicable Tools/Equipment Not Applicable