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. If you're ready to join the TSTC family and make great memories please complete our application process.

Job Description

The Data & Analytics Engineer is a hands-on practitioner within the Data & Analytics team responsible for developing data, analytics, business intelligence, and data science solutions that support institutional decision-making and operations. This role works across the analytics lifecycle, including data transformation, analytics-ready data structures, semantic modeling, business intelligence, advanced analytics, predictive modeling, and AI. The position works extensively with Microsoft Fabric and Power BI and provides expertise in data science and advanced analytics when institutional needs require statistical, predictive, or machine-learning solutions. The Data & Analytics Engineer partners with Data Engineering, Data Governance, Institutional Research, Application Engineering, and business stakeholders to transform institutional data into trusted, scalable, and actionable analytical products. The position contributes to shared standards and reusable approaches while continuing to develop expertise across the institution's data and analytics ecosystem.

Essential Functions

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

Data & Analytics Engineering

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.

Business Intelligence & Power BI

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.

Advanced Analytics & Data Science

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.

Data, AI & Governance

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.

Collaboration & Continuous Improvement

Engages directly with institutional stakeholders to translate complex operational needs into robust data and analytics solutions. Partners across Data Engineering, Data Governance, Institutional Research, Application Engineering, and academic and administrative units to deliver integrated, high-impact data products. Effectively communicates analytical findings, methodology, and technical concepts to both technical and non-technical audiences. Actively participates in technical design reviews, peer code reviews, and standard-setting initiatives while monitoring advancements in Microsoft Fabric, Power BI, and AI technologies to drive continuous improvement across the Data & Analytics ecosystem.

Education & Experience

Minimum Requirements

• •Bachelor's degree in Computer Science, Data Science, Information Systems, Statistics, Mathematics, Engineering, or a related field.
• •3 to 5 years of experience in data analytics, analytics engineering, business intelligence, data science, data engineering, or a related field.
• •Experience with Microsoft Power BI or comparable business intelligence and visualization technologies, developing analytical data models and transforming source data into analytics-ready datasets.
• •Experience working with data warehouses, lakehouses, data pipelines, or modern cloud-based data platforms with software development practices such as source control, testing, documentation, and code review.
• •Understanding of data visualization, dimensional modeling, business intelligence concepts, statistical analysis and data science concepts.
• •Proficiency in Python or a comparable programming language used for data analysis, automation, or data science.
• •Strong SQL skills and experience working with complex analytical datasets.
• •Ability to translate business requirements and questions into data and analytical solutions.

Preferred Qualifications

• •Master’s degree in Computer Science, Data Science, Information Systems, Statistics, Mathematics, Engineering, or a related field.
• •5+ years of experience in data analytics, analytics engineering, business intelligence, data science, data engineering, or a related field.
• •Experience with Microsoft Fabric, including OneLake, Lakehouse, Warehouse, Data Factory, notebooks, pipelines, or semantic models.
• •Experience with Power BI, including semantic modeling, DAX, Power Query, performance optimization, and deployment practices.
• •Experience with Azure data and analytics services.
• •Experience developing predictive models or machine-learning solutions.
• •Knowledge of statistical modeling, forecasting, regression, classification, clustering, and other advanced analytical techniques.
• •Experience with machine-learning frameworks such as scikit-learn, TensorFlow, PyTorch, or similar technologies.
• •Understanding of MLOps concepts, including model versioning, deployment, monitoring, and lifecycle management.
• •Experience with Microsoft Purview or similar data governance and lineage technologies.
• •Experience with CI/CD