Top Analytics Jobs in Dayton, OH

As the Director of Analytics, you'll lead analytics for MyPrize, optimizing performance across teams, developing reporting tools, and informing data infrastructure.

Analyze and reconcile enrollment and member data across Medicaid, Medicare, Marketplace, and Duals. Build reports and dashboards, fix data issues, apply statistical techniques, consolidate disparate sources, and partner with operations to ensure accurate coverage and regulatory compliance.

Lead combined Analytics Engineering and Data Engineering teams owning the end-to-end data roadmap from ingestion to trusted metrics. Manage hiring and performance, set architecture and platform investments, enforce testing/monitoring/documentation standards, review technical decisions, and partner with product and business stakeholders to translate needs into prioritized deliverables.

Design, build, and maintain advanced Workday reports, dashboards, and analytics to inform workforce planning and talent strategy. Partner with HR, HRIS, IT, and business leaders to define metrics, validate and govern data, perform deep-dive analyses, support Prism integrations, and deliver executive-ready insights, documentation, and training to enable data-driven HR decisions.

Design, implement, and maintain dbt/Snowflake ELT transformations to convert raw clinical and multimodal health data into trusted, analysis-ready models; enforce data quality, lineage, testing, CI/CD, and documentation; collaborate with clinical and ML teams to harmonize ontologies and support de-identification and ETL across health systems.

Lead measurement and insights strategies across B2B client programs. Analyze earned/social/web data, conduct secondary research, build measurement frameworks and dashboards, deliver client-facing recommendations, mentor junior staff, and advance agency analytics capabilities.

The Director, Analytics Infrastructure & Pipeline Operations is responsible for creating automated data pipelines and centralized feature stores for data science, leveraging AI technologies to optimize workflows and ensure data quality.

Lead implementations and analytics for community health centers: run stakeholder meetings, extract/clean/analyze EHR data, write SQL, build reports and visualizations, provide consultative support and training, manage data initiatives, and recommend product improvements.

Lead implementations and ongoing analytics consulting for community health centers: extract, clean, and analyze EHR data (heavy SQL), build reports and visualizations, manage projects and stakeholders, train client data teams, and inform product improvements based on customer feedback and domain expertise.

Lead implementations and provide consultative analytics for community health centers: extract, clean, and analyze EHR data (heavy SQL), build reports and visualizations, manage projects and stakeholders, train health center teams, and inform product improvements based on customer feedback and domain expertise.

The Staff Product Analytics Engineer will enhance product data usage across teams by owning data infrastructure, creating tools for self-service data, and ensuring data-driven decisions. Responsibilities include collaborating with various teams to improve data insights and maintain data pipelines.

Manage performance, reliability, and accountability for high-volume messaging programs. Build dashboards and alerting, run ad hoc analyses, coordinate incident response and vendor testing, conduct post-incident reviews, and maintain documentation and compliance tracking to inform program decisions and improvements.

Design, support, and improve data analytics solutions (OAC and Power BI). Manage data ingestion, ETL, data quality, access, and documentation. Liaise with stakeholders, migrate dashboards from OAC to Power BI, and follow SDLC and project management practices.

Own and evolve the analytics platform: build and maintain dbt data models, govern semantic metrics in Lightdash, ensure data quality and observability, and partner with analysts and stakeholders to enable reliable self-service reporting.

Build end-to-end data and analytics features: ingest and model data, build transformations and pipelines, create client-facing dashboards and interfaces, and develop LLM-powered workflows to accelerate analysis and app development. Partner with stakeholders, perform exploratory analyses, and ensure data quality, observability, and maintainability while influencing data engineering strategy.

Manage end-to-end analytics projects, lead cross-functional teams, ensure client satisfaction, and utilize AI tools to enhance data insights.

Lead end-to-end product analytics for New Content Experiences, partnering with Product, Design, Data Science, and Engineering to analyze large datasets, build tools, ensure data quality, and inform product strategy and decision frameworks.

Analyze commercial data for sales, marketing, service and support to create metrics and insights for optimization. Collaborate with cross-functional teams to provide data-driven recommendations and manage business intelligence tools.