Top Data & Analytics Jobs in Cincinnati, OH

The Senior Analytics Engineer will develop data pipelines, build models, and create dashboards to provide actionable insights for various teams. The role requires collaboration with multiple stakeholders to meet analytical needs and ensure data governance.

Lead design and governance of enterprise-scale data architecture (warehouses, lakehouses, streaming). Define standards for data quality, lineage, observability, and AI-enabled workflows. Mentor engineers, serve as technical escalation, and translate business needs into long-term data architecture and governance strategies.

The Data Strategy & Sourcing Analyst transforms product data into a strategic asset, ensuring data integrity and driving cost savings through sourcing. Responsibilities include establishing a Single Source of Truth, conducting data analysis, and collaborating with teams for financial impact. The role requires strong analytical skills, proficiency in SQL, and experience with data management tools.

Lead statistical analysis for clinical studies in oncology and cardiology, provide project leadership, and mentor junior biostatisticians while ensuring regulatory compliance.

Lead and manage a team of data engineers to develop and maintain data pipelines and warehouses, ensuring data quality and adherence to best practices.

The Staff Analyst will design analytical frameworks, lead business case development, and provide insights across departments while mastering insurance analytics.

As a Healthcare Analyst II, you will analyze healthcare data to optimize strategies, conduct quantitative analysis, and provide insights for clinical interventions, partnering with cross-functional teams.

The Senior Data Analyst oversees performance reporting tools, automates data pipelines, conducts quantitative analyses, and communicates findings to various stakeholders.

The Product Analyst will analyze user behavior, define KPIs, lead A/B tests, and develop dashboards to improve eCommerce performance, collaborating with cross-functional teams globally.

Design, build, and optimize modular dbt pipelines and data models; translate ambiguous product requirements into production-grade data assets; diagnose query performance and lower warehouse compute costs (including migration to Snowflake); enforce code hygiene, data security, testing, and peer review; mentor junior engineers and adopt AI-assisted development tools to speed test generation and legacy discovery.