Top Data & Analytics Jobs in Cincinnati, OH

Lead data analysis, reporting, and storytelling for Health Insurance clients. Design studies and surveys, ensure questionnaire quality, analyze results, collaborate with client teams, and produce clear, actionable deliverables on schedule.

Own and architect marketing and storefront analytics systems, including metric definitions, single sources of truth, multi-touch attribution, marketing mix modeling foundations, semantic models, and data health metrics. Partner with marketing, storefront, and leadership teams to improve acquisition measurement, budget allocation, data quality, and decision-making across international markets.

Design, build, and own scalable data pipelines and warehousing architecture from ingestion through analytics and incentive payouts. Lead data platform architecture, dbt modeling, data quality, lineage, observability, performance, and cost management. Partner with engineers and analysts on telematics accuracy and driver incentive reporting. Use AI coding tools daily to develop and review pipelines, SQL, and data models.

Analyzes complex healthcare data to identify trends, develop statistical and machine learning methodologies, and produce actionable insights. Builds dashboards and reports, supports product and business decisions, promotes data literacy, and collaborates with cross-functional teams. The role also ensures analytics practices comply with healthcare privacy, governance, security, and regulatory requirements, including HIPAA.

Convert, map, load, validate, and troubleshoot banking data for client implementations on the Lumin Digital platform. The role manages data conversion projects through launch, collaborates with clients and cross-functional teams, resolves data and system issues, identifies project risks, and improves conversion processes. Responsibilities include testing multiple file formats, ensuring data integrity, supporting vendor transitions and mergers, generating exception reports, and providing technical consultation.

Conduct quantitative and qualitative research, analyze complex business scenarios, gather requirements, and synthesize stakeholder perspectives into actionable recommendations. Evaluate data using tools such as Excel or SQL, identify process improvements, and produce clear reports, memos, business cases, and documentation. Collaborate with stakeholders through written communication and contribute analytical content to help train AI systems.

Design and manage financial systems for accurate cashback calculations, build and maintain reconciliation processes, ensure data integrity for yield-bearing instruments, collaborate with compliance, and partner with cross-functional teams to improve financial system efficiency.

Manages a data engineering team responsible for scalable, reliable data pipelines, infrastructure, and analytics tooling. Provides mentorship, performance management, and technical leadership while coordinating with product, analytics, finance, compliance, engineering, and regulators. Prioritizes cross-functional projects, supports data architecture and governance, ensures performance and reliability, and operates within strict regulatory and audit requirements.

Own the company’s data function by designing and optimizing Snowflake architecture, pipelines, integrations, data models, BI dashboards, metric definitions, governance, and data-quality standards. Partner with leadership and cross-functional teams to translate operational needs into trusted data products. Automate recurring reporting, troubleshoot ingestion issues, perform ad hoc analysis, and establish scalable analytics practices as the first dedicated analytics hire.

Owns enterprise workforce intelligence and employee listening strategy. Analyzes employee sentiment, workforce trends, talent, skills, organizational effectiveness, and work data to identify risks and opportunities. Develops dashboards, analytical methodologies, AI-enabled insight products, and self-service resources. Partners with HR leaders and data owners to translate complex quantitative and qualitative findings into actionable recommendations for workforce planning, succession, organization design, automation, and capability decisions.