Top Senior Data & Analytics Jobs in Salt Lake City, UT

Lead development and governance of the supply chain data warehouse and analytics platforms. Build SQL-driven models, dashboards, and KPIs to drive inventory optimization, cost reduction, supplier performance, and working capital improvements. Support ERP integrations and data migrations, perform deep data discovery and predictive analytics, and present actionable insights to senior leaders to enable strategic decision-making.

Build and maintain Quince's compensation data environment: integrate HR systems, automate benchmarking and AI workflows, support comp reviews and equity approvals, analyze pay trends, and partner with Talent, Finance, and Legal on global comp strategy.

Design, build, and maintain Power BI reports and semantic star-schema models; author and audit SQL; create visualizations; collaborate with stakeholders to scope solutions; improve BI dashboards; ensure data quality and documentation; share best practices and support cloud and on-prem reporting architectures.

Lead design and delivery of Power BI dashboards and data models, analyze complex datasets using SQL, translate business needs into scalable BI solutions, partner with executives, maintain data integrity, and drive BI process improvements and performance tuning.

The Staff Data Engineer will lead financial data operations, ensuring accuracy and reliability of data pipelines, while collaborating with Finance, Product, and Engineering teams to enhance systems and decision-making processes.

Lead implementations and ongoing analytics consulting for community health centers: extract, clean, and analyze EHR data (heavy SQL), build reports and visualizations, manage projects and stakeholders, train client data teams, and inform product improvements based on customer feedback and domain expertise.

Lead implementations and provide consultative analytics for community health centers: extract, clean, and analyze EHR data (heavy SQL), build reports and visualizations, manage projects and stakeholders, train health center teams, and inform product improvements based on customer feedback and domain expertise.

Lead implementations and analytics for community health centers: run stakeholder meetings, extract/clean/analyze EHR data, write SQL, build reports and visualizations, provide consultative support and training, manage data initiatives, and recommend product improvements.

Lead IJM's global donor and audience insights by developing segmentation, motivation models, and behavioral analysis to guide marketing and fundraising strategy. Translate complex data into actionable recommendations, manage global projects and workflows, build cross-functional adoption of analytics, produce foresight and trend reports, and embed an insight-driven culture while participating in organizational spiritual formation.

Build and maintain scalable batch and streaming ETL/ELT pipelines, data models, and data warehouse architectures. Implement orchestration, quality, governance, and monitoring. Create self-service dashboards, enable ML/AI-ready datasets and feature pipelines, optimize performance, and collaborate with cross-functional teams to deliver analytics and AI-enabled solutions for customer experience and post-sales operations.

Lead the Sales Strategy & Analytics function to deliver reporting, KPI tracking, profitability and competitive analysis, and sales technology ownership. Partner cross-functionally to provide executive insights, optimize tools and processes, drive adoption, and develop a high-performing analytics team that improves sales productivity, profitability, and strategic decision-making.

Own and optimize data infrastructure and ETL/ELT pipelines using SSIS and Microsoft Fabric. Build and maintain SQL Server data warehouse staging, transformations, and lakehouse ingestion. Implement CI/CD, version control with GitHub, monitor pipeline health, troubleshoot data issues, document data flows, and translate stakeholder requirements into data engineering solutions that support BI reporting.

Lead development of credit and fraud strategies using data, ML models, and advanced analytics. Design A/B tests, analyze portfolio performance, conduct root-cause and ad-hoc analyses, and partner with Data Science, Product, and Engineering to implement and validate risk and fraud prevention strategies while communicating findings to stakeholders.