Top Analysis Reporting Jobs in Des Moines, IA

Support marketing analytics by analyzing digital channel performance, building and maintaining KPI dashboards, ensuring data accuracy, and collaborating with cross-functional teams to turn insights into actionable recommendations.

Leads AlphaSense’s Financial Services channel research coverage across banking, insurance, and fintech. Develops coverage roadmaps, channel-check frameworks, expert source strategies, and structured questionnaires. Guides analysts and operations teams on project execution, expert targeting, feasibility, and quality. Acts as the primary financial services domain expert, identifies new research opportunities, maps value chains, and partners cross-functionally to improve scalable AI-enabled research workflows and deliver differentiated, actionable insights.

Own data ingestion, validation, reconciliation, and quality pipelines using Python, advanced SQL, and PostgreSQL. Design schemas, indexes, migrations, automated tests, and CI/CD workflows while handling unreliable third-party feeds. Develop test plans, track defects, establish quality metrics, conduct performance and security testing, and support compliance documentation. Preferred experience includes AWS event-driven architectures, healthcare or payer data, orchestration tools, infrastructure as code, FedRAMP environments, data quality frameworks, and anomaly detection.

Analyzes, validates, and prepares distribution data; develops dashboards, reports, and visualizations; supports warehouse management systems, labor management, configuration, testing, and continuous improvement. Partners with Distribution, IT, process teams, engineers, and vendors to provide business intelligence, troubleshoot systems, communicate data insights, and support operational and strategic decisions.

The Data Analyst position requires U.S. citizenship and that all work be performed within the continental United States, Alaska, or Hawaii. The listed annual pay range is $90,000 to $110,000.

Designs, develops, and maintains Databricks-based ETL pipelines, data lake architectures, datasets, and data quality processes supporting reporting, analytics, and AI/ML workloads. Maintains and migrates legacy SSIS and SQL Server systems, optimizes pipeline performance, and supports complex SSRS reporting. Collaborates with business users, analysts, and IT teams to define data requirements, troubleshoot issues, document solutions, and advance AI-assisted engineering practices.

Lead the design and implementation of scalable data models, transformation pipelines, semantic layers, and analytics engineering solutions. Drive dbt best practices, testing, CI/CD, data governance, quality improvements, performance optimization, and technical standards. Collaborate with analysts, engineers, stakeholders, and clients to translate business requirements into reliable data architectures. Mentor peers, lead technical implementations and delivery workstreams, reduce technical debt, and contribute to shared tooling and frameworks.

Lead design and delivery of scalable, secure cloud data pipelines and products. Translate requirements into architecture, apply advanced data modeling (Data Vault 2.0/dimensional), embed data quality, observability, security, and DataOps/CI-CD practices, optimize performance and cost, mentor engineers, and enable AI/ML-ready datasets within an agile DevSecOps model.

Lead end-to-end analytical initiatives on partner enrollment funnels, ensure data quality, build reporting pipelines with BI/engineering, use AI-assisted tools to accelerate analysis, translate insights into strategic recommendations for leadership, and coach growing analytics team members.

Leads and scales a global data engineering and analytics team while remaining hands-on with architecture, pipelines, data quality, dimensional modeling, dashboards, reporting, and customer-facing deliverables. Owns the analytical data platform supporting clinical, operational, product, financial, and customer data. Partners with cross-functional stakeholders, investigates complex data issues, improves platform reliability and security, and mentors engineers.