Data Engineer, Darden School of Business at UVA Health

, Virginia, United States -

Full Time

Start Date

Immediate

Expiry Date

01 May, 26

Salary

0.0

Posted On

31 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, Databricks, SQL, Python, Data Pipelines, ELT Processes, Data Warehousing, Dimensional Modeling, REST APIs, CI/CD, DevOps, Data Quality, Machine Learning, Feature Engineering, Data Governance, Analytics

Industry

Hospitals and Health Care

Description

The University of Virginia Darden School of Business, one of the world's leading business schools, seeks a highly skilled and motivated Data Engineer to join its Strategic IT Data & Analytics team. As a Data Engineer, the role will play a crucial role in building and maintaining our data infrastructure, ensuring the efficient extraction, loading and transformation (ELT) of data from various sources into our cloud data environments. The ideal candidate should have hands-on experience with ELT processes in Databricks. If you are passionate about data engineering, have strong analytical skills, a passion for higher education and enjoy working in a collaborative environment, we would love to hear from you. This position, which is not available for sponsorship, is hybrid and located in Charlottesville, VA, and is required to be in-person at least 2 days per week. Responsibilities: Design and implement scalable, metadata-driven data pipelines using cloud-based ELT frameworks to support enterprise analytics and reporting. Collaborate with cross-functional teams to translate data requirements into Lakehouse-integrated solutions. Build and maintain data models, integration workflows, and transformation processes supporting analytics and reporting. Develop, orchestrate, and optimize data pipelines to ensure reliability, data quality, and performance. Monitor and troubleshoot production pipelines to maintain availability, scalability, and operational excellence. Identify and resolve performance bottlenecks, data quality issues, and compute inefficiencies. Support implementation of data governance, security controls, and privacy requirements for regulated data environments. Stay current with emerging data engineering and cloud technologies to enhance platform capabilities. Extend ELT architectures to support machine learning workloads. Build feature engineering pipelines using Databricks Feature Store, Delta Lake, or vectorization techniques. Assist in deploying and monitoring machine learning models using MLflow or Databricks Model Serving. Implement vector search, embedding pipelines, or retrieval-augmented generation (RAG) components. Contribute to orchestrating AI workflows or agent-based pipelines using Lakehouse AI capabilities. Develop CI/CD workflows for data and ML artifacts to ensure reproducibility and alignment with governance standards. Troubleshoot and optimize model and feature pipelines. Track emerging AI technologies and identify opportunities to apply them within the platform. Integrate Databricks and Unity Catalog with enterprise data governance tools to ensure compliant access and lineage tracking. Required Education and Experience: High school diploma and at least 4 years of relevant experience required. Education may be substituted in lieu of experience with an Associate's degree or Bachelor's degree and 2 years of relevant experience. Bachelor’s degree in computer science, MIS, engineering, business, or related field, with at least five years of relevant experience preferred. Required Knowledge, Skills and Abilities: Proven experience developing and maintaining data pipelines and ELT processes. Strong proficiency with Databricks, Unity Catalog, and Delta Lake. Expertise in SQL and Python for ingestion, transformation, and automation. Experience with REST APIs and web services for data integration. Strong understanding of data warehousing concepts and dimensional modeling. Familiarity with CI/CD and DevOps practices for data engineering. Strong analytical and problem-solving skills working with complex datasets. Effective communication and collaboration skills. Preferred Knowledge, Skills and Abilities: Databricks Data Engineer Associate or Professional certification. Experience integrating Databricks with enterprise governance platforms such as Microsoft Purview. Coursework or certification in Databricks Machine Learning or AI Engineering. Experience with MLflow, Feature Store, Model Serving, or AI workflow orchestration. Experience with vector search, embeddings, or LLM-based application components. Familiarity with machine learning operational practices and monitoring. Experience with Microsoft Power BI or Microsoft Fabric. To Apply: Apply online using the following link: http://jobs.virginia.edu/us/en/job/R0078946/Data-Engineer-Darden-School-of-Business. Internal applicants may search and apply for jobs on the UVA Internal Careers website. Complete the application, and upload the following Required materials: Resume Cover letter detailing your interest in the position ***Please note that you MUST upload ALL REQUIRED DOCUMENTS into the CV/Resume box, into which multiple documents can be uploaded. Applications that do not contain all of the required documents will not receive full consideration.*** For this position, the selected candidate will need to complete the required background checks prior to the start date of the position. This position will remain open until filled. Questions regarding the application process should be directed to Marcus Rozier, Academic Recruiter, at hjv5vm@virginia.edu. The University of Virginia is an equal opportunity employer. All interested persons are encouraged to apply, including veterans and individuals with disabilities. Learn more about UVA’s commitment to non-discrimination and equal opportunity employment.

Responsibilities

The Data Engineer will design and implement scalable data pipelines and collaborate with cross-functional teams to support enterprise analytics. Responsibilities also include monitoring production pipelines and ensuring data quality and performance.