Associate Director, Business Intelligence

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Reporting to the Senior Director of Institutional Research, the Associate Director of Business Intelligence (BI) functions as a key technical and strategic leader, contributing to the planning, execution, and daily operations of the BI team. The Associate Director is responsible for leading BI and data projects from inception to completion, including the development and oversight of data modeling and engineering solutions.

The Associate Director is expected to demonstrate rapid learning and technical agility, applying emerging technologies and complex data analysis, data science and predictive modeling to achieve meaningful business outcomes. Active participation in AI initiatives is also a core responsibility.

ESSENTIAL JOB DUTIES Lead the planning and execution of Business Intelligence (BI) projects, providing technical direction, managing team performance, and ensuring timely deliverables in collaboration with the Project Management Office (PMO).Recommend and implement innovative technologies, methods, and industry-leading data warehousing tools and best practices to advance the BI program and achieve strategic business outcomes.

Oversee all technical aspects of the Snowflake data warehousing environment, including infrastructure, standards (table naming, schema, user security), performance troubleshooting, and documentation.

Design and maintain a robust data warehousing system for structured and unstructured data, developing queries, tables, views, and data applications to support the College's Data Strategy roadmap and strategic plan.

Design and implement robust data models that reduce redundancy, streamline data movement, and ensure adherence to data security and privacy standards across institutional subject areas (e.g., Enrollment Management, Academic Affairs, Finance, HR and Enterprise integration domain).Implement end-to-end data pipelines (ETL) utilizing APIs, scripts, web services, files, and RaaS.Administer and evolve data pipeline and integration processes, monitoring data usage and performance in Snowflake.

Enforce data quality, knowledge retention, and process consistency through the curation and expansion of the institutional data dictionary and business glossary.

Apply complex data analysis, data science, and modeling techniques to develop advanced reports and analytical solutions, including statistical analysis, predictive modeling, and data mining to identify trends and patterns in business and academic performance.

Identify business-relevant performance metrics and KPIs with area leaders (e.g., Enrollment Management, Finance, Academic Affairs, HR), and apply innovative visualization and advanced analytics to develop interactive dashboards that create new business value.

Drive the College’s Artificial Intelligence (AI) initiatives by researching and utilizing cutting-edge technologies, implementing AI and Machine Learning models to support data initiatives, and presenting on AI usage to the broader College community.

Communicate effectively with internal teams and external business users through written, verbal, and visual storytelling, and develop detailed technical specifications, data mapping, and data flow documentation for major analytics projects.

Handle complex data issues with minimal supervision, using sound judgment to escalate critical issues to management.

Commit to professional development by fostering team learning, cross-training, and knowledge building.

Map data from new sources to the central data model to support the creation of new or extension of existing reports/dashboards and perform data validation. Provide requirements and execute data extraction, transformation, and load procedures. Use complex data analysis and query, data science and data modeling techniques to develop reports and data analytics solutions. This includes engineering solutions in the form of business dashboards, reports and data visualizations integrating with internal and external public data sources like College Board or IPEDS.

Provide statistical analysis of data, predict trends on business and academic performance, use data mining to extract information from data sets and identify correlations and patterns.

Handle complex data issues with minimal supervision, while escalating critical issues to management using sound judgment. Enforce data quality, knowledge retention, and process consistency through the curation and expansion of the institutional data…