Data and Analytics Engineer

Virginia Tech is a comprehensive land-grant institution dedicated to knowledge, discovery, and creativity. The Data and Analytics Engineer role focuses on designing and developing data solutions that enhance institutional reporting and decision-making through advanced analytics and cloud technologies.

Responsibilities:

• Designing, developing, and supporting enterprise data products, cloud data pipelines, and analytics solutions that enable institutional reporting, decision support, advanced analytics, and AI initiatives across Virginia Tech
• Developing scalable and secure data solutions utilizing Snowflake, AWS, Power BI, and related technologies
• Partnering with business stakeholders, data stewards, and technical teams to transform institutional data into trusted, governed, and reusable data assets that support self-service analytics, operational excellence, research, and strategic decision-making
• Supporting Virginia Tech's enterprise data and analytics strategy through the development and support of modern cloud-based data solutions

Requirements:

• Master's degree, or a combination of education, training, and progressive experience that equates to a Master's degree
• Working experience designing, developing, supporting, and optimizing enterprise data, reporting, and analytics solutions
• Significant experience with programming, scripting, or automation technologies used in data engineering and analytics environments, such as Python, SQL, or similar tools
• Significant experience with relational databases (Oracle, Postgres, or Snowflake) and advanced SQL
• Working experience with SQL development, data transformation, query optimization, and working with enterprise-scale relational and analytical data platforms
• Working experience translating business requirements into scalable technical solutions that support data management, reporting, analytics, and decision-making needs
• Working experience developing and supporting data transformation, data pipelines, or enterprise data platforms
• Experience working with cloud-based data, analytics, or enterprise application platforms
• Experience with data modeling, data quality management, metadata management, data governance, and enterprise data security principles
• Demonstrated analytical, troubleshooting, and problem-solving skills
• Demonstrated ability to communicate effectively with technical and non-technical audiences and collaborate across cross-functional teams
• Demonstrated ability to manage multiple priorities and deliver high-quality solutions in a dynamic environment
• Experience designing, implementing, and supporting Snowflake-based data platforms in a production environment
• Experience utilizing Snowflake platform capabilities such as Snowpark, Dynamic Tables, Streams and Tasks, Data Sharing, Horizon Catalog and Governance, and Cortex AI services
• Experience with analytics engineering and modern data stack technologies such as dbt or similar transformation frameworks
• Experience supporting DataOps practices, including automated testing, deployment pipelines, monitoring, observability, and operational automation
• Experience using workflow orchestration technologies such as Apache Airflow or comparable tools
• Experience working with infrastructure-as-code technologies such as Terraform
• Experience supporting machine learning, predictive analytics, artificial intelligence, or generative AI initiatives
• Experience developing enterprise data products, semantic models, or reusable analytics assets that support self-service analytics and institutional decision-making
• Experience in integrating enterprise applications, SaaS platforms, and APIs with modern cloud data platforms
• Experience working with higher education data domains including student, finance, human resources, advancement, research, or academic operations
• Experience developing dashboards, reports, semantic models, or self-service analytics solutions using enterprise business intelligence platforms such as Power BI, Tableau, MicroStrategy, or comparable technologies
• Experience using source control and collaborative development practices with tools such as Git or comparable platforms
• Experience supporting software development lifecycles, including solution design, development, testing, deployment, documentation, and operational support activities
• Experience developing and supporting cloud-based data platforms, data integration processes, and data pipelines using AWS or comparable cloud technologies
• Experience developing, maintaining, and supporting data pipelines, ETL/ELT processes, and related data engineering solutions that enable reporting, analytics, and business operations