Top Senior Analysis Reporting Jobs in Kansas City, MO

Design, build, and automate scalable batch and streaming data pipelines, implement data models and ETL, automate deployments, support ML/experimentation, maintain server architecture, and partner with stakeholders to deliver analytics and data-driven tools.

Lead and manage institutional research activities: collect, analyze, validate, and report quantitative and qualitative data; support program/course reviews, accreditation, dashboards, and decision-making; supervise a team and manage ad-hoc and ongoing reporting and assessment projects.

The Senior Analyst, Revenue Operations is responsible for delivering analytical insights, enhancing forecasting accuracy, and optimizing revenue processes while collaborating with various departments.

The ETL Architect is responsible for designing ETL architectures, development, and implementation of data models and warehousing solutions, with a focus on data quality and transformation logic.

The VP of Data will lead the data organization encompassing engineering and analytics, drive strategic insights, improve operational excellence, and integrate AI into workflows while ensuring data governance.

The Sales Analytics Manager will oversee sales analytics, implement AI-driven reporting solutions, and partner with various teams to enhance data governance and performance forecasting.

The Senior Data Modeler designs and maintains data models, supports AI-driven practices, and collaborates on data lifecycle processes. Responsibilities include translating business needs into data structures, ensuring data governance, and supporting analytics and AI use cases.

Lead strategic data evolution for Fund Administration clients, building dashboards, managing stakeholders, and translating business needs into actionable insights.

Design, build, and enhance executive Power BI dashboards and scalable data models. Develop DAX, Power Query, optimized visuals, KPIs, templates, and governance while iterating with stakeholders to deliver role-based reporting and drill-through navigation.

Own and improve the dbt transformation layer, semantic layer, data observability, and pipeline orchestration while migrating the platform to AWS. Ensure data quality and governance to support clinical AI, collaborate with clinical, operations, and finance stakeholders, and audit, document, and fix existing data pipelines.