Business Intelligence Analyst

University of North Texas System•Fort Worth, TX

1d•$82,000 - $86,000•Hybrid

About The Position

The Business Intelligence Analyst in the Office of University Assessment and Accreditation (UAA) works collaboratively with the campus community with two integrated functions: 1) enterprise dashboard development and data modeling, including the design, construction, and maintenance of reporting solutions, in collaboration with UAA team members, data owners, ITS Data Analytics, data governance stakeholders, and other internal stakeholders. 2) applied artificial intelligence for institutional analytics, including natural language processing of qualitative student and stakeholder feedback, automation of AI-assisted reporting workflows, and evaluation of emerging AI tools for extending the university's data and analytical capacity. This position is responsible for facilitating data accessibility, analyzing institutional cross-functional data and external benchmark and market data, and producing reports and visualizations to support compliance reporting, evidentiary data for assessment and accreditation purposes, strategic initiatives, ad hoc reports, and operational efficiencies. The BI Analyst develops reports and dashboards according to defined business specifications for publication to internal and public audiences, as appropriate. This position analyzes institutional data for patterns in the flow of students through the academic process and programs, as well as the resources that support student success. To maintain data integrity, accuracy, reproducibility, and usability, the BI Analyst documents data definitions, workflow processes, and standard operating procedures and utilizes various quality control methods spanning both traditional data pipelines and AI-generated outputs.

Requirements

Bachelor's degree and five (5) years of related experience; or equivalent combination of education and experience.
Ability to analyze quantitative data, identify patterns, trends, relationships, and anomalies, and derive accurate, actionable conclusions from complex datasets.
Ability to analyze qualitative data applying statistical and natural language processing techniques, including sentiment analysis, topic modeling, text classification, or similar methodologies, to structured and unstructured qualitative data such as open-ended survey responses or narrative feedback.
Ability to present and translate complex analytical, statistical, and AI-derived results into decision-ready narratives for non-technical academic and administrative leadership.
Advanced proficiency in Microsoft Power BI, including DAX, Power Query, semantic model development, report design, and data modeling.
Working knowledge of Microsoft Fabric, including Lakehouse architecture, Dataflows Gen2, and enterprise analytics solutions.
Advanced proficiency in SQL Server and relational database technologies, including T-SQL, stored procedures, query optimization, and performance tuning for large datasets.
Proficiency in Python for data extraction, transformation, automation, statistical analysis, and reporting, including experience with libraries such as pandas and other analytics, automation, or natural language processing libraries.
Experience collecting, integrating, cleaning, validating, and mapping data from disparate internal and external data sources while maintaining data quality and integrity.
Working knowledge of PeopleSoft data structures and relationships, including student records, admissions, financial aid, and academic program data sufficient to independently extract, validate, and analyze institutional data.
Ability to evaluate model and AI output quality, documenting validation methodology, and applying responsible AI, FERPA, and data privacy requirements to institutional data.
Ability to design, document, maintain, and improve automated analytics and AI-assisted reporting workflows, including testing, version control, validation procedures, and change management practices for model-driven processes.
Ability to assess emerging analytics and AI technologies for institutional applicability and communicate potential benefits, limitations, costs, risks, and implementation considerations to stakeholders.
Ability to think strategically and apply an institution-wide perspective when conducting analyses, developing reporting solutions, and supporting planning, assessment, accreditation, and compliance activities.
Ability to work effectively in a collaborative, team-oriented environment and serve as a technical resource by providing guidance, consultation, training, and documentation related to institutional data, reporting systems, analytics tools, and business intelligence solutions.
Ability to perform work with a high degree of accuracy, attention to detail, sound judgment, and problem-solving capability with minimal supervision.

Nice To Haves

Master's degree or higher in Data Analytics, Information Systems, Computer Science, Statistics, Higher Education Administration, Institutional Research, or a related field.
Seven (7) or more years of progressively responsible experience in business intelligence, institutional research, data analytics, data management, or decision support within higher education.
Seven (7) or more years of experience with SQL Server and relational database technologies, including T-SQL, stored procedures, query optimization, data integration, and performance tuning.
Extensive experience working with higher education data environments, including student records, admissions, financial aid, curriculum, enrollment, graduation, and academic program data.
Experience working with PeopleSoft Campus Solutions or similar higher education student information systems.
Experience supporting institutional research, assessment, accreditation, strategic planning, compliance, or regulatory reporting functions.
Demonstrated experience developing and maintaining enterprise reporting and data integration solutions using Microsoft Power BI, Microsoft Fabric, or comparable analytics platforms.
Experience applying statistical analysis, predictive analytics, forecasting methodologies, or quantitative research techniques to support institutional decision making.
Demonstrated experience applying advanced critical thinking and analytical reasoning to research data sources, evaluate data quality, resolve inconsistencies, and prepare accurate, validated datasets for reporting, analysis, and institutional decision support.

Responsibilities

Collaborate with UAA team members, data owners, data requestors, subject matter experts, and institutional decision-makers to define business questions, analytical requirements, methodologies, deliverable specifications, validation criteria, and intended uses of data products supporting institutional initiatives, program objectives, assessment, accreditation, compliance, and decision-making.
Support UAA objectives and functions by independently collecting, integrating, cleaning, validating, analyzing, and interpreting quantitative and qualitative data and developing reports, dashboards, semantic models, visualizations, and automated reporting solutions according to defined business specifications and research goals.
Develop and maintain enterprise business intelligence and analytics solutions using Microsoft Power BI, Microsoft Fabric, SQL Server, Python, and related technologies, including data models, DAX measures, Power Query transformations, Dataflows, automated workflows, and other reusable analytical components.
Work closely with ITS Data Analytics to facilitate secure and reliable access to institutional data; support the ingestion and integration of official institutional data snapshots into the enterprise analytics environment; and manage the acquisition, validation, documentation, and integration of external benchmarking, market, and reference data of strategic value to the institution.
Build and maintain collaborative relationships with data owners and functional and technical subject matter experts to understand data structures, business processes, system relationships, and data lineage and to facilitate the accurate and timely flow of information into institutional data repositories and analytical solutions.
Engage data owners, data stewards, and subject matter experts in the collaborative development and maintenance of business terms, data definitions, data dictionaries, metadata, source-to-target mappings, and other documentation that provides a consistent institutional reference for stakeholders.
Develop and maintain comprehensive documentation of institutional data definitions, data sources, analytical methodologies, data models, validation procedures, automated workflows, version history, standard operating procedures, and reporting processes to promote accuracy, transparency, reproducibility, continuity, and appropriate change management.
Establish and apply data quality controls in collaboration with data owners and data stewards to identify anomalies, errors, inconsistencies, and gaps in institutional data; investigate root causes; document findings; validate corrective actions; and support the continued integrity, reliability, and usability of institutional information.
Conduct statistical, predictive, forecasting, and qualitative analyses of structured and unstructured institutional data, including survey responses and narrative feedback, as appropriate; evaluate analytical models and AI-generated outputs for accuracy, reliability, bias, and appropriate use; and document validation methods, limitations, and assumptions.
Translate complex analytical, statistical, and AI-assisted findings into clear, concise, and decision-ready reports, visualizations, narratives, and recommendations for technical and non-technical academic and administrative audiences.
Evaluate institutional data flows, reporting processes, analytical models, and emerging analytics and AI technologies to identify risks, obstacles, inefficiencies, and improvement opportunities and communicate potential benefits, limitations, costs, governance considerations, implementation requirements, and institutional applicability to stakeholders.
Serve as a technical and analytical resource to UAA and institutional stakeholders by providing consultation, guidance, training, troubleshooting assistance, and documentation related to institutional data, reporting systems, analytical methodologies, business intelligence platforms, and responsible use of AI-assisted solutions.
Promote responsible data governance and ethical analytics practices and protect the confidentiality, integrity, privacy, and availability of institutional information in accordance with FERPA, applicable data privacy requirements, the UNT Health Code of Culture, UNT System information technology policies and regulations, and UAA reporting and documentation standards.
Manage multiple concurrent analytical, reporting, and development priorities; organize work independently; communicate project status, risks, and dependencies; and meet established service-level expectations while supporting assessment, accreditation, curricular services, institutional research, compliance, and other institutional functions.