AI Data Engineer

Syracuse University is committed to delivering an exceptional student experience through vibrant, engaged campus communities. This position is based at the above campus location and requires regular in-person presence to support our students, collaborate with colleagues, and contribute to our thriving academic environment. Syracuse University values the collaboration, mentorship, and spontaneous connections that happen when our community works together on campus.

Remote work arrangements are limited in accordance with University policy.

Pay Range

Pay Range $89,000 - $95,000

Pay rates at Syracuse University are based on a combination of factors including, but not limited to, the job responsibilities; the candidate’s education, training, work experience and key competencies; the university’s strategic priorities; internal peer equity; applicable federal, state, local laws, grant funding and contractual requisites; and external market analyses.

Staff Level S5

FLSA Status

FLSA Status Exempt

Hours

Standard University business hours
8:30am - 5:00pm (academic year)
8:00am - 4:30pm (summer)

Hours may vary based on operational needs.

Job Type

Job Type Full-time

Unionized Position Code Not Applicable

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 One Lake, 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 (One Lake, Data Factory, Real‑Time Intelligence, Fabric IQ, notebooks) and comparable data platforms
• Proficiency with Power BI and semantic modeling (data modeling, relationships, DAX , and One Lake‑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…