Top Senior Level Data & Analytics Jobs in Minneapolis, MN

The Senior Data Science Consultant at DesignMind will lead client engagements, build machine learning systems, and mentor teams while communicating with stakeholders. Responsibilities include data modeling, statistical analysis, and delivering insights through documentation and presentations.

As a Senior Data Scientist, you will derive insights from large health datasets and develop AI tools for medication management. Your responsibilities include creating predictive models, assessing program impacts, and presenting findings to stakeholders.

The Data Engineer will work closely with software engineers to aggregate various data sources and develop actionable dashboards for business leaders at a premier banking client.

The Senior Data Analytics Specialist will collaborate with public safety professionals, providing expert support and developing data analytics solutions. Responsibilities include creating dashboards, training personnel, gathering feedback, and enhancing services, while ensuring clear communication between technical teams and clients.

As a Data Solution Consultant at Guidewire, you will engage with property and casualty insurance companies to advise on the Data Platform and its benefits. Your role involves technical sales support, conducting presentations, identifying platform requirements, and collaborating with clients to meet business objectives. You will leverage your data management experience and insurance domain knowledge to drive successful solutions.

The Principal Data Engineer will design and maintain ETL pipelines, collaborate with teams on data integration, implement data transformation logic, and ensure data quality. The role requires strong organizational skills, a focus on data governance, and experience working with large datasets and ETL technologies.

The Senior Analytics Engineer will drive data modeling, optimize analytics processes, and collaborate with cross-functional teams to enhance product operations at HubSpot. Responsibilities include managing high-value data assets, creating scalable data models, and defining standards for analytics practices.