Top Remote Analysis Reporting Jobs in Des Moines, IA

The Senior Bioinformatics Data Engineer will build data pipelines, implement clinical data ingestion, and optimize platform infrastructure while ensuring data quality and compliance with legal standards.

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.

Own end-to-end analytics modeling for a business domain: build and maintain dbt models and business marts on Snowflake, develop the BI semantic layer, define consistent metrics, create self-serve data products, and partner with Finance, Sales, Marketing, CS, and RevOps. Validate upstream data with the Data Platform team and translate ambiguous business questions into production-ready data models.

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.

Work across data infrastructure and analytics: build and maintain dbt models, ingest and transform data, create dashboards and semantic layers in Power BI, validate AI-generated code, partner with stakeholders to deliver analyses and data products, and improve data governance and documentation.