Top Remote Senior Level Data Management Jobs

The Solutions Consultant will conduct product demos, assist in sales cycles, and provide evidence of product value while maintaining product knowledge and client relations.

The Senior Data Analyst will conduct quantitative analysis, optimize products through data insights, and lead A/B testing in collaboration with various stakeholders.

Lead design and management of data infrastructure and architecture, implement ETL and data quality standards, partner with stakeholders to align data systems with business strategy, and evaluate emerging data technologies to drive scalable, secure data platforms.

The role involves defining data requirements for insurance customers, managing data integration, and providing solutions for complex data issues. It requires collaboration with customers and internal teams while ensuring effective implementation of solutions.

The Senior Claims System Analyst will lead technical projects related to claims processing, ensuring system integration and compliance while optimizing vendor relationships and documentation.

The Director of Data will lead the data organization, drive data strategy, oversee the data platform, and ensure high-quality data products across OneImaging, contributing to decision-making in operations and product development.

The Power BI Analyst will analyze large datasets, design workflows, create Power BI dashboards, and ensure accurate data transformation processes to support operations and performance tracking.

As a Salesforce Business Analyst, you will drive Salesforce innovation, optimize automation, and support change management processes in a fast-paced environment.

As a Data Analyst, Growth at Kraken, you will analyze complex marketing data to provide actionable insights that enhance client acquisition, retention, and decision-making through data engineering, reporting, and experimentation.

As a Senior Data Engineer, you'll design and build data pipelines, automate processes, and collaborate with business teams to support analytical needs, ensuring data quality and optimization for ML workloads.