Top Remote Data & Analytics Jobs in Providence, RI

The Data Architect is responsible for developing and implementing data solutions, optimizing data pipelines, and providing architectural guidance on analytics projects.

The AWS Data Engineer will design, build, and optimize data pipelines on AWS, ensuring performance and data quality for analytics. Responsibilities include developing ETL processes, collaborating with teams, and monitoring data workflows.

The Enterprise Data Architect will design and lead AmeriLife's data architecture, creating scalable data models and data mapping standards for analytics and AI workloads.

The Lead Python Developer will design and develop Python-based analytics solutions, create reporting tools, collaborate with stakeholders, and mentor peers in a healthcare analytics environment.

The Sr. Insurance Analyst analyzes complex data to support growth and profitability targets, evaluates market opportunities, and collaborates cross-functionally on insurance products.

The Decision Support Analyst collaborates with customers and teams to prioritize data requirements, perform complex data analysis, and implement decision support systems for effective business choices.

The Analytics Associate Consultant delivers GE HealthCare solutions, supporting product development, data analysis, client engagement, and enhancing analytics tools while requiring significant travel.

The Lead Data Analyst analyzes audience behavior and content performance on streaming platforms, providing insights to drive product strategy and business decisions through data analysis and reporting.

The Associate Actuary conducts analysis, pricing, and risk assessment to estimate financial outcomes, supporting internal and external initiatives with innovative methodologies and data-driven insights.

The Marketing Data Analyst will analyze performance data across marketing and sales channels, develop dashboards, and provide insights to improve customer engagement and ROI.

Perform hands-on data cleansing, linking, validation, and standardization; monitor and report data quality metrics; identify recurring issues and recommend process or automation improvements; organize and streamline workflows; maintain documentation and data standards; test and provide feedback on tools and AI-assisted features; serve as a point of contact for data integrity questions.