Top Remote Analysis Reporting Jobs in Des Moines, IA

Manage analytical service operations by overseeing a team, designing software for data mining, developing statistical models, and generating insights for marketing outcomes. Provide training and mentorship to direct reports.

Develop scalable analytics assets and serve as the Power BI/Microsoft Fabric expert. Build and publish trusted datasets, semantic models, and dashboards; author Databricks workflows and production SQL; apply R for statistical analysis; ensure governance, versioning, and reproducibility; partner with stakeholders and Data Engineering to deliver high-impact insights, define KPIs, and communicate executive-ready recommendations.

Analyze healthcare billing, payment, and medical review datasets; develop monitoring summaries and statistical analyses; support federal healthcare policy research; and collaborate with analysts and policy experts on project-based analytical solutions.

Design and deliver enterprise data infrastructure and architectures across on-prem and Azure cloud. Build ETL/ELT pipelines, data models, governance, security, and migrations; mentor teams and align data platforms to business, compliance, and analytics needs.

Provides Tier 1 and Tier 2 remote technical support for hardware, software, peripherals, networks, VPN, and MFA issues. Configures and ships laptops and equipment for new hires, manages accounts and permissions across Google Workspace, Microsoft 365, Slack, and internal systems, and develops knowledge-base documentation and training guides. Supports the company’s North American expansion and remote workforce.

Performs remote data entry and administrative support by entering, updating, verifying, and organizing information in digital databases and electronic records. Maintains spreadsheets, reviews documents for accuracy, identifies discrepancies, protects confidential information, responds to update requests, and completes assigned projects on schedule while communicating progress to supervisors.

Build and optimize Snowflake data transformations across staging, curated, and conformed layers. Implement dimensional models, SCD Type 2 history, member matching, payer attribution, reconciliation, and member-month outputs. Develop restartable, testable processing with audit logging, error handling, data-quality checks, alerting, and schema-drift controls. Troubleshoot production issues, maintain documentation, participate in reviews and planning, and support client demonstrations.

Own Snowflake architecture and establish staging, curated, and conformed data-layer standards. Design dimensional, historical, identity, payer attribution, member-month, and temporal models for healthcare data. Define data quality, reconciliation, lineage, audit, exception, and operational controls while protecting sensitive information. Translate requirements into engineering designs, guide engineers, conduct design reviews, manage technical risks, and support sprint planning and estimates.

Leads advanced data analytics and engineering initiatives, including data infrastructure, ETL, pipelines, warehousing, governance, visualization, and AI-enabled solutions. Supervises analysts, manages data systems and Smartsheet workflows, ensures documentation and contract compliance, and delivers insights, reports, and recommendations to improve program operations and grant management.

Analyzes claims data to identify trends, patterns, risks, and business opportunities. Retrieves, validates, and prepares data from multiple databases; creates reports, dashboards, visualizations, spreadsheets, and presentations; supports forecasting and reporting needs; collaborates with claims, underwriting, product, IT, and leadership teams; and communicates recommendations for business decisions and process improvements.

Build Python and SQL data pipelines, maintain data integrity, automate dashboards and reporting, and deliver actionable analytics. Develop AI- and LLM-assisted workflows for data enrichment, classification, summarization, and process automation. Support leadership decision-making, sales, customer success, and internal efficiency while communicating insights to nontechnical stakeholders. Evaluate, document, and monitor AI solutions for accuracy, cost, risk, and auditability.