Data Analyst Python Sql Jobs in Utah (NOW HIRING)

This role will provide department leadership with reliable data from trusted source systems that will empower and influence key decision making. Access and integrate data for reporting to establish data standards, quality, and reliability.  This role applies advance analytics, foundational data science techniques, and responsible for generating insights and enhanced data-driven storytelling - while maintaining data quality and data governance.

Qualifications

General Workforce Requirements

• Temple worthiness

• Regular attendance and timely arrival at assigned work location

Education: Identify the minimum educational requirements for this role (High School, Bachelor's, Master's, or other included field(s) of study)

• BS/BA Degree in mathematics, statistics, business information systems, or a related field

Work Experience: Identify the minimum experience required for this role, including specific roles, experiences, and/or industries

• minimum 5 years performing multiple system data validation, quality assessments, and configuration of reports and standardized dashboards

Demonstrated Skills & Abilities: Identify what experience-based skills and abilities the successful incumbent for this role will possess

• Strong knowledge and skill in SQL, Databricks, AWS, Azure and Tableau

• Writing of queries, and reports using SQL

• Strong understanding of data structures and applying complex analysis of different statistical methodologies and data modeling

• Demonstrated ability to present data with awareness of the audience receiving the data

• Use of data to create visualizations that tell the data's story, skillfully utilizing visual, written, and verbal communication methods

• Works with minimal to moderate oversight and meets established deadlines

• Delivers defined projects with regular oversight

• Thrives in both a team environment and as an individual contributor

Specific Degrees, Certifications, Licenses: Identify any required or preferred specialized degrees, etc., if any, required for this role

• Prefer data management, data quality, or data visualization certification

Principles of Operation: This level of Data Analyst will work on data coming from multiple systems, will integrate both structured and unstructured data for the creation and interpretation of standardized dashboards and reporting. They must be knowledgeable and have full understanding of the data lifecycle and the data relationships between various systems that allow integration. This level will also perform ad hoc, deep dive analytical analysis, and interpretation.

Responsibilities:

Understand the Data

• Defining what the data is, where it originates, how it is captured, and how it is consumed

• Document data definitions, lineage, and relationships across multiple systems

• Identify opportunities to apply basic data science techniques (e.g., clustering, trend analysis, text analysis) to improve understanding of business data

Data Quality Assessment

• Validation of data quality including sources, reliability, completeness, validity, and uniqueness

• Use AIassisted profiling, statistical methods, and anomaly detection to uncover data issues

Organize the Data

• Design and maintain data models that support analytics, dashboards, and entrylevel data science use cases.
• Prepare datasets for exploration, feature analysis, and predictive modeling through effective structuring and aggregation.
• Contribute to data warehouse and analytics platform design that supports analytics and machineassisted modeling.

Configuring Data for Access and Analysis

• Build queries, datasets, and feature-ready tables using SQL and Databricks

• Support exploratory data analysis (EDA), basic predictive modeling, and statistical testing under guidance

• Perform quality assurance on analytical datasets, models, 

Reporting, Visualization & Insight Development

• Translate business questions into dashboards, reports, and analytical summaries that are actionable and measurable
• Develop visualizations that explain both historical patterns and emerging trends or forecasts