Top Remote Data & Analytics Jobs in Cincinnati, OH

The Senior Director of Data Platform leads LINQ's Business Systems and Data Engineering functions, overseeing strategy, team development, and ensuring data governance while delivering scalable solutions aligned with business needs.

Join Kled to help build the future of human data collection and design systems that influence AI data training through a consumer application.

The Enterprise Performance Analytics Engineer builds and maintains data pipelines and models, ensuring data quality and efficient reporting for analytics teams.

Inovalon seeks candidates to join their Talent Community, emphasizing data-driven solutions to enhance healthcare outcomes and economics.

Lead a team of ~7 data scientists to deliver timely, accurate product analytics and experimentation insights; manage people, mentor staff, partner with product and engineering, and build accessible data products and measurement practices.

As an Analytics Engineer at Axios, you'll design and maintain data products and visualizations, improving decision-making through actionable insights.

Develop and maintain datasets for visualization and machine learning; automate data pipelines; partner with teams to build data products.

Design and govern integration data architectures across platforms (eCommerce, CRM, Supply Chain, third-party integrations). Define data flows and models, enforce data governance and security, produce design specs, perform architectural and code reviews, troubleshoot complex integration problems, and collaborate with engineers, product owners, and vendors to deliver scalable data integration solutions.

Lead and scale the Data & Analytics Practice, drive AI-enabled data solutions, oversee client engagements, and mentor a high-performing team. Define growth strategies and modern data architectures while ensuring alignment with business outcomes and collaboration among stakeholders.

Architect and scale a governed BI and analytics platform (Snowflake, dbt, Sigma) to deliver trusted, standardized KPI frameworks and semantic layers. Build high-quality data models, optimize SQL for large datasets, enable self-serve analytics, mentor engineers, drive governance and testing standards, and partner with Product, Data Science, Finance, and business teams. Promote AI-assisted tooling to accelerate analytics delivery and improve metric trust and reporting consistency.