Data & Analytics Engineer

Join Texas State Technical College to build data, analytics, and AI-powered solutions that support institutional decisions and day-to-day operations. This hands-on role works across the full analytics lifecycle, using Microsoft Fabric and Power BI to deliver scalable datasets, trusted metrics, and enterprise reporting. You will also serve as a technical resource for predictive analytics, statistical modeling, and machine learning when advanced analytics can create measurable value.

Responsibilities

• Design, develop, and maintain scalable data and analytics solutions using Microsoft Fabric, SQL, Python, and related technologies.
• Transform raw and complex source data into analytics-ready datasets for reporting, business intelligence, advanced analytics, and data science initiatives.
• Develop and maintain analytical data models, data transformations, automated pipelines, notebooks, and reusable data assets, partnering with Data Engineering to manage Silver and Gold architectural layers.
• Apply robust software and data engineering practices, including version control, testing, technical documentation, code reviews, and deployment management; troubleshoot and optimize for performance, reliability, and maintainability.
• Design and deliver enterprise Power BI dashboards, reports, semantic models, and analytical tools that support institutional decision-making.
• Build and maintain reusable semantic models, standardized measures, and institutional performance metrics using Power BI, DAX, Power Query, and Microsoft Fabric.
• Implement best practices for data visualization, dimensional modeling, performance tuning, row-level security, and report lifecycle management.
• Partner with Data Governance and Institutional Research to ensure calculations, metrics, and institutional data definitions are consistently applied across deliverables, while supporting and mentoring team members using shared analytical assets.
• Serve as a key technical resource for data science, predictive analytics, statistical modeling, and machine learning across the institution.
• Evaluate complex business problems to determine where advanced statistical or machine-learning methods provide actionable value beyond traditional reporting.
• Design, develop, validate, and operationalize predictive and analytical models supporting key institutional priorities, including enrollment forecasting, student retention and completion, program alignment, financial optimization, and operational capacity planning.
• Execute feature engineering, exploratory data analysis, regression, classification, clustering, and time-series forecasting; collaborate with Data Engineering and Application Engineering to integrate outputs into enterprise platforms and workflows.
• Evaluate and prototype emerging capabilities in analytics, machine learning, and generative AI to address institutional challenges and improve operational effectiveness.
• Ensure data and analytics solutions adhere to institutional standards for data governance, security, privacy, and data handling.
• Collaborate with Data Governance to support data quality, lineage, metadata dictionaries, data classification, and responsible data usage.
• Embed responsible AI and data science principles across deliverables, focusing on transparency, explainability, reproducibility, appropriate oversight, and comprehensive documentation.
• Engage with institutional stakeholders to translate operational needs into robust data and analytics solutions.
• Communicate findings, methodology, and technical concepts to both technical and non-technical audiences.
• Participate in technical design reviews, peer code reviews, and standard-setting; monitor advancements in Microsoft Fabric, Power BI, and AI technologies.
• Lead communication efforts for projects, programs, or teams, ensuring clarity, alignment, and transparency.
• Demonstrate advanced mastery of technical and functional competencies, take ownership of responsibilities, and support team direction and alignment.
• Collaborate with colleagues respectfully, welcoming different perspectives, and apply independent judgment to evaluate options, balance risks, and make decisions that support organizational success.

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 BI tools), including 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, applying 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 comparable language) used for data analysis, automation, or data science.
• Strong SQL skills with complex analytical datasets.
• Ability to translate business requirements and questions into data and analytical solutions.

Technologies

Microsoft Fabric, Power BI, SQL, Python, DAX, Power Query, OneLake, Lakehouse, Warehouse, Data Factory, notebooks, pipelines, semantic models, Azure, scikit-learn, TensorFlow, PyTorch, Microsoft Purview, FERPA, CI/CD, DevOps, MLOps, generative artificial intelligence

Preferred Qualifications

• Master’s degree in 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 (OneLake, Lakehouse, Warehouse, Data Factory, notebooks, pipelines, semantic models).
• Experience with Power BI (semantic modeling, DAX, Power Query, performance optimization, deployment practices).
• Experience with Azure data and analytics services.
• Experience developing predictive models or machine-learning solutions.
• Knowledge of statistical modeling and advanced analytical techniques such as forecasting, regression, classification, and clustering.
• Experience with ML frameworks including scikit-learn, TensorFlow, or PyTorch.
• Understanding of MLOps (model versioning, deployment, monitoring, lifecycle management).
• Experience with Microsoft Purview or similar governance and lineage tools.
• Experience with CI/CD or DevOps practices for data and analytics solutions.
• Experience working with complex institutional data (higher education, student, financial, workforce, or similar domains).
• Knowledge of data privacy, security, and regulatory requirements such as FERPA.
• Relevant experience may be considered in lieu of formal education, consistent with TSTC’s equivalency philosophy.

Additional Information

• Location: Texas (onsite).
• Employment classification: Exempt under the Fair Labor Standards Act (FLSA).
• Reasonable accommodations: TSTC provides accommodations to qualified individuals with disabilities.
• Telework eligibility: May be considered based on eligibility under TSTC’s Telework Policy aligned with State of Texas requirements.
• Travel: Periodic in-state travel may be required based on business needs.
• Background checks: A criminal history background check will be required for finalist(s).
• Employment eligibility: Successful hire requires completion of the I-9 form and presentation of acceptable original documents within the required timeframe.