Top Remote Data & Analytics Jobs in Minneapolis, MN

The Senior Data Scientist will develop and deploy analytical models supporting marketing strategies, assess campaign effectiveness, and optimize customer engagement strategies, employing advanced data-driven methods and partnering with marketing stakeholders.

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.

Lead design and implementation of a unified event-tracking data platform. Collaborate with Data, Product, and Engineering to build reusable event-publishing frameworks, monitoring/alerting, and deprecate legacy systems. Coach mid-level engineers, influence roadmap and processes, manage cloud/SaaS spend, create documentation, and participate in on-call rotations to ensure platform reliability and data trustworthiness.

The Senior Data & Analytics Consultant will lead data insights for the pet business's pricing and performance, manage analytics initiatives, and ensure accurate data reporting.

The Lead Data Governance Specialist manages data integrity and accessibility across platforms, driving best practices, collaboration, and compliance in healthcare analytics.

The Client Analyst will manage client-facing data and analytics requests, align objectives with campaign performance, provide insights, and optimize campaigns for key accounts.

The Senior Data Engineer will architect, build, and maintain scalable data pipelines and infrastructure, mentor junior engineers, and collaborate on data solutions for Openly's insurance platform.

The Clinical Data Analyst will design and implement analytics for payer-grade outcomes, integrate claims and clinical data, and support business metrics analysis tailored for client relationships. They will ensure data quality and governance while translating clinical data into economic value across various healthcare analytics initiatives.

The Senior Data Engineer will own and evolve data pipelines, collaborate with teams for data models and pipelines, optimize ETL processes, and implement data storage solutions.

Analyze auction data to identify performance drivers and risks, collaborate with commercial teams, and provide actionable insights to optimize DSP outcomes.