The SEVIS Coordinator and Data Analyst at ISSS is responsible for ensuring the accuracy and integrity of data within iPenn and SEVIS. This role oversees SEVIS registration, managing data through quality control, cleanup, integration, and analysis within iPenn, and ensuring it aligns with other Penn systems to maintain high levels of compliance. Additionally, the position involves analyzing both automatic and manual data processes to identify issues and implement practical improvements. Key duties include monitoring alerts within the SEVIS database and iPenn system, addressing errors at the system level, contacting ISSS clients as necessary, and assigning follow-up tasks to ISSS staff. The role also involves configuring templates and automated workflows to handle various combinations of incoming data, moving rule-based processing into automated workflows to allow ISSS staff to focus on more nuanced immigration issues. The Data Analyst analyzes SEVIS reportable data from iPenn, Banner records, and Penn other central record systems to identify and correct reporting gaps. This role proactively monitors and addresses batch errors, collaborating with the ISSS team to identify root causes and implement process adjustments. Additionally, this position generates case processing reports to support internal fee verification and ensure accurate revenue collection. The role also includes generating routine and ad hoc reports to meet the business needs of ISSS and the Penn community, as well as creating and managing the ISSS connection to Penn's Data Warehouse and other data analysis tools. This involves presenting ISSS data in an interactive format to stakeholders and generating diagrams to illustrate data flow among systems, identifying problem areas, and proposing alternatives. The Data Analyst liaises with other central Penn units that handle the international population on campus, including student records, the Registrar, NGSS, ISC Penn Community, and Integrations. They are responsible for understanding and maintaining existing processes, closing gaps, and matching existing data to propose and validate potential future process improvements or system conversions.