Top Data & Analytics Jobs in Cincinnati, OH

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

Design, build, and operate scalable data pipelines and integrations for an RCT-driven voter platform. Extend data platform, optimize cost and reliability for election-scale loads, lead cross-system projects, ensure data quality, document systems, and participate in on-call incident response.

The SI Business Analyst will drive integration between business needs and technical delivery, focusing on insurance and healthcare systems. Responsibilities include capturing requirements, performing gap analysis, designing workflows, and leading cross-functional teams to optimize system performance and support digital transformation initiatives.

Lead architecture and reliability of the data platform, build and harden ingestion (batch and CDC), own BigQuery+dbt transformations, establish testing/observability/governance, mentor engineers, develop Python tooling and APIs, run POCs for lakehouse/open table formats, and define AI-assisted development practices.

The Senior Analyst will support the personalization algorithm by conducting analyses, testing features, managing projects, and collaborating with teams to improve food recommendations for customers.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

Lead and mentor a data engineering team building scalable customer-focused data solutions. Hands-on technical ownership, architecture guidance, and delivery of data ingestion, transformation, and reporting. Advance AI-enabled development practices, set technical standards, and foster a collaborative, accountable engineering culture to improve speed, quality, and reliability.

Lead and grow a data engineering team that builds and operates ingestion, transformation, orchestration, and serving layers and a self-serve analytics substrate. Own data quality, observability, cost efficiency, SLAs, and engineering practices while partnering with Data Science, BIE, Analytics, and Product to deliver reliable, reusable data products.

Lead and grow a team of analytics engineers while remaining hands-on with SQL, dbt, and Snowflake. Own roadmap and semantic layer development, build trusted, governed data products, partner with cross-functional stakeholders, champion data quality and observability, and hire and coach engineering talent to deliver scalable analytics and AI-ready foundations.

Analyzes cybersecurity and operational data to identify trends, anomalies, risks, and performance insights. Develops executive dashboards, scorecards, KPIs, KRIs, automated reports, and reusable analytics frameworks using tools such as SQL, Tableau, and Power BI. Applies AI-enabled analytics to improve reporting efficiency and data interpretation. Partners with security, risk, compliance, technology, and infrastructure teams to translate technical findings into actionable business recommendations.