Top Remote Analysis Reporting Jobs in Las Vegas, NV

The Business Analyst will gather requirements from clients, support project lifecycles, conduct demos, and manage change, while utilizing ServiceNow. Responsibilities include documentation and relationship building.

The Product Analyst will measure results and deliver insights on new AI products, identify opportunities, and guide product strategy through data analysis and visualization.

Manage the end-to-end data issue lifecycle for North America: intake, triage, root cause analysis, remediation planning and execution oversight. Produce IMR reporting and dashboards, maintain audit-ready records, engage stakeholders, and improve IMR processes and tooling to strengthen data quality and controls.

Build, maintain, and improve AI-driven automations and data systems. Own Power BI dashboards and underlying pipelines (Python, SQL, DAX), manage complex margin/profit-share processes, create low-code AI automations (Claude, Zapier, Make), ensure data governance, and document/support solutions for company-wide use.

Manage the full data quality lifecycle for constituent records in Salesforce: deduplication, merges, data appends/enrichment, imports/exports, vendor management, audits, governance, reporting, staff training, and supervise a team focused on CRM data hygiene to support nonprofit fundraising operations.

Design, develop, and troubleshoot SAP reporting solutions, data marts, OLAP and dashboards. Translate business needs into BI solutions, follow data management processes, analyze trends, collaborate on database and architecture design, and evaluate future data delivery tools.

Lead process mining and improvement projects across business areas, develop dashboards and analyses (e.g., Celonis), apply industrial engineering practices, identify root causes, translate functional requirements to technical solutions, and drive customer experience optimizations.

The Data Engineer will build and maintain scalable data pipelines, improve ETL processes, collaborate with AI/ML teams, and contribute to engineering culture.

Design and analyze experiments, measure campaign incrementality, identify customer segments and engagement opportunities, validate tracking and anomalies, surface actionable insights, and build scalable measurement infrastructure with cross-functional partners.

Design, build, and optimize scalable batch and streaming data pipelines. Model and process structured and unstructured data, ensure production reliability and observability, apply ML techniques for insight extraction, and drive data strategy while adopting modern tooling and IaC practices.