Data & Analytics Engineer

Are you someone who strives for excellence, values accountability, provides the best service, all while reflecting unwavering integrity? Our talented team members across the state follow the behaviors, beliefs and outcomes of these core values to ensure that our vision is met.

The employee will demonstrate TSTC's Core Values of Excellence, Accountability, Service, and Integrity with internal and external stakeholders, customers, students, and community members.

Designs, develops, and maintains scalable data and analytics solutions using Microsoft Fabric, SQL, Python, and related technologies. Transforms raw and complex source data into well-structured, analytics‑ready datasets to support institutional reporting, business intelligence, advanced analytics, and data science initiatives. Develops and maintains analytical data models, data transformations, automated pipelines, notebooks, and reusable data assets while partnering with Data Engineering to manage Silver and Gold architectural layers.

Applies robust software and data engineering practices, including version control, testing, technical documentation, code reviews, and deployment management, while troubleshooting and optimizing solutions for peak performance, reliability, and maintainability.

Designs and delivers enterprise Power BI dashboards, reports, semantic models, and analytical tools that drive institutional decision-making. Builds and maintains reusable semantic models, standardized measures, and institutional performance metrics using Power BI, DAX, Power Query, and Microsoft Fabric. Implements best practices for data visualization, dimensional modeling, performance tuning, row‑level security, and report lifecycle management. Partners closely with Data Governance and Institutional Research to ensure calculations, metrics, and institutional data definitions are consistently executed across all analytical deliverables while supporting and mentoring team members in utilizing shared analytical assets.

Serves as a key technical resource for data science, predictive analytics, statistical modeling, and machine learning across the institution. Evaluates complex business problems to determine where advanced statistical or machine‑learning methodologies deliver actionable value beyond traditional reporting. Designs, develops, validates, and operationalizes predictive and analytical models supporting critical institutional priorities, including enrollment forecasting, student retention and completion, program alignment, financial optimization, and operational capacity planning.

Executes feature engineering, exploratory data analysis, regression, classification, clustering, and time‑series forecasting, collaborating with Data Engineering and Application Engineering to integrate model outputs into enterprise platforms and workflows.

Evaluates and prototypes emerging capabilities in analytics, machine learning, and generative artificial intelligence to address institutional challenges and enhance operational effectiveness. Ensures all data and analytics solutions adhere strictly to institutional data governance, security, privacy, and data‑handling standards. Collaborates with Data Governance to maintain data quality, lineage, metadata dictionaries, data classification, and responsible data usage. Embeds responsible AI and data science principles across all deliverables, ensuring transparency, explainability, reproducibility, and appropriate oversight while maintaining comprehensive documentation of data sources, transformations, calculations, and analytical methodologies.