Data & Analytics Engineer

tstc; United States; On-site; USD 120,000 - 160,000; Full time

Job summary

The Data & Analytics Engineer at tstc will design, develop, and maintain scalable data and analytics solutions using Microsoft Fabric, SQL, Python, and Power BI to support institutional decision-making.

You'll build analytics-ready data models, semantic layers, and enterprise dashboards, while partnering with Data Governance, Institutional Research, and engineering colleagues to ensure data quality, security, and scalable insights across the organization.

Qualifications

• Experience designing analytics-ready data models and scalable pipelines.
• Proficiency in Power BI, DAX, and semantic modeling.
• Strong collaboration with governance, IR, and engineering teams.

Responsibilities

• Develops data analytics solutions and pipelines using Microsoft Fabric, SQL, Python.
• Builds enterprise Power BI dashboards and semantic models.
• Evaluates and operationalizes predictive analytics and AI capabilities.
• Ensures data governance, security, and data quality.
• Mentors team members in reusable analytical assets.

Skills

SQL

Python

Power BI

DAX

Power Query

Data modeling

ETL

Analytics

Collaboration

Tools

Microsoft Fabric

Git/version control

Job description

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.

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 institution