ANALYST, IT

Duke Careers•Durham, NC

1d•Hybrid

About The Position

The Office of Information Technology is looking for an AI Data Engineer to join our Data Analytics Practice and help shape the future of Duke’s enterprise data platform. In this role, you’ll build and evolve the data foundations that power analytics, reporting, and AI-driven applications across the university. You’ll work at the intersection of data engineering, analytics, and emerging AI use cases—partnering closely with AI engineers and analysts to ensure institutional data is secure, reliable, and ready for advanced insights. If you enjoy building scalable platforms, working across domains, and turning complex data ecosystems into trusted, reusable assets, this role offers the opportunity to make a broad and lasting impact.

Requirements

Bachelor’s degree in computer science, engineering, information systems, or a related technical field, or an equivalent combination of education and experience.
Strong experience designing and building data platforms, including cloud-based data warehouse or lakehouse architectures.
Proficiency in Python and SQL, with experience developing production-grade data pipelines.
Hands-on experience with modern data stack tools such as dbt for transformations and Prefect, Airflow, or similar orchestration tools.
Solid understanding of data modeling approaches (e.g., dimensional modeling, star schema, data vault).

Nice To Haves

Experience supporting AI and ML data workloads, including unstructured data pipelines, embeddings, and integration with vector databases or search platforms.
Familiarity with data governance practices and tooling (metadata management, lineage, data quality frameworks).
Experience building shared data infrastructure, reusable frameworks, and self-service data access layers.
Exposure to containerization, CI/CD pipelines, and modern cloud architecture principles.
Master’s degree in computer science, engineering, or a related discipline.

Responsibilities

Design, build, and maintain Duke’s core enterprise data platform, supporting analytics, reporting, and AI applications at scale.
Develop and manage scalable, reusable data pipelines that integrate data from institutional systems such as student, HR, finance, and research administration.
Implement standardized transformation frameworks to deliver high-quality, well-documented data models for downstream consumption.
Partner with AI engineers and analysts to enable AI-ready data infrastructure, including support for retrieval-augmented generation, embeddings, and unstructured data ingestion.
Establish platform standards, governance capabilities, and best practices, including metadata management, data quality monitoring, security controls, and performance optimization.