Top Data & Analytics Jobs in Cincinnati, OH

Develop and maintain analytical tools and cashflow models. Collaborate with various teams to analyze and structure financing transactions.

This role involves designing measurement strategies, analyzing campaign effectiveness, and collaborating with stakeholders to optimize marketing efforts for customer acquisition.

As an Energy Markets Intern, you'll develop quantitative risk models, analyze energy markets, and recommend risk management strategies to maximize profits for customers.

Analyze product and business data to define KPIs, build reliable analytics models and dashboards, and partner with cross-functional teams to surface insights, improve data quality, and support forecasting and experimentation.

Design, build, and support enterprise BI solutions by aggregating data from multiple sources, developing reports and dashboards (Tableau, SSRS), writing SQL and ETL (SSIS), collaborating with stakeholders, supporting data modeling and governance, and ensuring data accuracy to inform business decisions—with emphasis on behavioral health and healthcare data.

Lead GTM productivity analytics: build and maintain predictive capacity models and dashboards, analyze GTM performance and attainment, support planning and forecasting, align metrics across Ops/Finance, and partner on data infrastructure improvements to drive GTM efficiency and growth.

Support implementation of Bluesight SaaS by extracting, transforming, loading, and validating customer data. Configure infrastructure, write and debug scripts, communicate findings to customers, document processes, and collaborate with product and engineering to improve pipelines and resolve technical issues.

Design, build, and optimize distributed query solutions using Starburst/Trino. Enable secure, governed data access across cloud and on-premises sources, implement data federation and query optimization, support IAM and governance in a federal environment, develop backend services and CI/CD, and troubleshoot performance in cloud-native systems.

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.

Own end-to-end data pipelines for multiple clients: extract, clean, transform, model and visualize data. Build dashboards and automated workflows, apply AI where useful, and present actionable insights to non-technical stakeholders.