Top Remote Analysis Reporting Jobs in Des Moines, IA

Own and implement the canonical data model and governance for a multi-tenant MEP SaaS platform. Architect data for AI/ML readiness (RAG, embeddings, vector search), design polyglot persistence and lake/lakehouse pipelines, lead staged modernization and migrations, produce hands-on prototypes and in-repo guardrails, and partner with platform, DB engineering, and ML teams to ensure data quality, lineage, and observability.

The Senior Analyst will design lead scoring models, automate workflows, and utilize AI tools to derive insights for sales and marketing collaboration. They will also maintain documentation to ensure team efficiency.

The Instructional Designer I creates high-quality online courses, interacts with various teams, and requires strong communication and project management skills.

Lead the analytics team for the third-party agent business, driving business strategy, product analytics, and AI-powered insights while fostering cross-functional partnerships.

The Principal Statistical Programmer manages statistical programming for clinical trial data, ensures quality and compliance for CDISC standards, and mentors junior staff.

The Forward Deployed Marketing Data Scientist will work with AI customers and engineering teams to optimize AI-driven marketing campaigns through data analysis, diagnostics, and communication of insights to stakeholders.

The Clinical Business Analyst designs, develops, and maintains reports and dashboards in the Epic EHR system, ensuring accurate data and communication with users.

The SAP IBP Analyst will optimize supply chain planning processes in e-commerce, focusing on SAP IBP modules, system integrations, and stakeholder management.

Lead data engineering for a healthcare scheduling platform rebuild: map legacy multi-TB schema, design target schema, build translation layer (views/materialized views and write mappers), ensure data consistency during parallel systems, plan per-tenant cutover with verification and rollback, tune DB performance, instrument data quality/lineage/audit, and mentor engineers.

Design, build, and scale reliable batch and real-time data pipelines and warehouses. Partner with engineering, data science, and business teams to define data models, improve observability, optimize storage and querying, and evangelize data engineering best practices across the company.