Top Remote Analysis Reporting Jobs in Salt Lake City, UT

Reviews, develops, validates, and refines dashboards, reports, visualizations, calculations, data sources, KPIs, and analytical workflows across BI platforms. The role documents findings, provides structured feedback, troubleshoots reporting accuracy, and evaluates AI-powered agents and workplace connectors used in analytics processes.

Design and manage financial systems for accurate cashback calculations, build and maintain reconciliation processes, ensure data integrity for yield-bearing instruments, collaborate with compliance, and partner with cross-functional teams to improve financial system efficiency.

Analyze large healthcare datasets, build and implement quantitative models, perform statistical analyses, ensure data accuracy, collaborate with cross-functional teams, and provide actionable insights to improve healthcare delivery.

Leads College Board’s Operations analytics strategy and team, developing forecasting, predictive modeling, optimization, cost assessment, and decision-support capabilities. Partners with senior leaders across Operations, Product, Program, Technology, and Finance to improve execution, customer experience, efficiency, and cost savings. Responsibilities include setting technical direction, embedding analytics into operational decisions, presenting insights, hiring and coaching analytics staff, and building future AI and data-infrastructure capabilities.

Develop and maintain data ingestion pipelines, relational databases, ETL/ELT workflows, cloud resources, data reliability monitoring, and AI-enhanced applications. Integrate LLM APIs and MCP connectors, write Python and R automation code, migrate siloed processes into centralized systems, and collaborate with technical and non-technical stakeholders. The role also supports Azure deployments, enterprise data integrations, dashboards, reporting, and cloud data security.

Leads data engineering teams responsible for secure, scalable, governed data platforms and pipelines. Oversees Snowflake architecture, performance, security, cost optimization, data quality, governance, access controls, metadata, lineage, and audit readiness. Manages budgets, staffing, engineering standards, platform reliability, and continuous improvement while partnering with product, analytics, AI, security, compliance, infrastructure, vendors, and auditors. Develops managers and builds high-performing data engineering organizations.

Own healthcare analytics data methodology, including episode grouping, provider attribution, cost standardization, quality measures, reference data, pipelines, QA, and production delivery. Translate feature requests into specifications and validated analytics. Reconcile results, establish recurring data-quality coverage, investigate failures, and collaborate with engineering, data science, product, clinical informatics, and customer teams. Advanced SQL and healthcare claims expertise are central to ensuring accurate, defensible customer metrics.

Lead partner analytics by building scalable dashboards, forecasting and ROI models, and experimental designs to evaluate and optimize partner performance. Collaborate with Product, BD, Marketing, and Data Engineering to improve pipelines, translate insights into recommendations, and mentor analysts to scale partner growth.

Own analytics for Customer Success and Operations by translating ambiguous business questions into metrics, reports, dashboards, and PRDs. Partner with senior leaders, CSMs, Analytics Engineers, and Operations teams to improve retention, permit turnaround, and organizational scalability. Build self-service dashboards, develop dbt models, document analytics assets, prioritize high-impact requests, and use LLM tools to accelerate SQL, reporting, and data-model development.

Build and maintain Snowflake and dbt data platforms, including ELT pipelines, tested models, orchestration, monitoring, documentation, access controls, and governance. Optimize performance and costs while ensuring reliable, discoverable data for internal dashboards, client products, and AI/ML use cases. Translate ambiguous stakeholder needs into durable solutions and apply strong software engineering practices, including Git, CI/CD, code review, Python, testing, and alerting.