Top Remote Analysis Reporting Jobs in Minneapolis, MN

In this role, you'll build and scale high-throughput data pipelines, ensure data quality, and leverage AI for improved data operations. You'll also lead initiatives in a fast-paced environment focused on real estate data engineering.

The Data Engineer will help build modern data infrastructure, designing ETL pipelines, modeling datasets, and collaborating with teams for improved data quality and accessibility.

The Manager, Data Success Management oversees Data Success Managers, manages data operations, supports partners, improves workflows, and ensures data quality.

Design and manage financial systems for accurate cashback calculations, build and maintain reconciliation processes, ensure data integrity for yield-bearing instruments, collaborate with compliance, and partner with cross-functional teams to improve financial system efficiency.

As a Senior Business Data Product Owner, you'll drive data enablement across product teams, ensuring effective data use for business and clinical outcomes while collaborating with IT and stakeholders.

As a Data Operations Engineer, you will manage partner data lifecycle, ensuring data quality and performance while collaborating with teams to enhance data integration processes and guide architecture decisions.

Lead design, operation, and reliability of production data pipelines in a multi-terabyte Snowflake environment. Translate client requirements into dimensional models, debug and remediate pipeline failures, evolve the data mart for event-driven and AI workflows, and improve pipeline trustworthiness via testing, CI/CD, audit logging, and documentation.

Lead end-to-end marketing and customer analytics: provision and clean data, explore and model (clustering, forecasting, driver models), build ML models and experiments, create dashboards and visualizations, present recommendations to clients, document data architecture, and mentor junior analysts.

The Data Engineer is responsible for developing data infrastructure, building data pipelines, managing data quality, and integrating analytics tools to support design and project operations.

Collect, clean, normalize, and analyze complex datasets; develop data collection systems; maintain databases; create queries, views, and reports; generate ongoing reports and identify process improvement opportunities.