Top Analysis Reporting Jobs in Dayton, OH

Design, develop, and maintain Customer Data Platform solutions and data pipelines to create unified customer profiles, manage identity resolution, enable audience segmentation and activation, ensure data quality and governance, and support integrations with marketing, CRM, and cloud data systems.

Provides analytical support to Carrier and Sales Center teams by developing datasets, dashboards, KPIs, forecasting models, financial reports, and actionable business insights. Responsibilities include stakeholder requirements gathering, data modeling, SQL analysis, visualization, P&L reporting, performance tracking, and communicating recommendations to management. The role also supports strategic initiatives, operational improvements, and end-to-end analytics projects using BI tools.

Administer Informatica and MuleSoft data governance and quality platforms in AWS GovCloud production environments. Responsibilities include installation, integration, security, performance tuning, backup and recovery, high availability, monitoring, testing, RMF/ATO support, documentation, incident response, and technical mentoring. The role integrates data catalogs and quality services with Yellowbrick, PowerCenter, and ETL systems while maintaining secure, repeatable configurations and operational readiness.

Own weekly and monthly sales performance reporting across SPINS, Circana, retailer portals, distributor feeds, and shelf data. Diagnose velocity, distribution, pricing, promotions, and competitive share; identify risks and opportunities; build buyer-facing category and sell-in decks; evaluate promotions and trade ROI; support Costco planning; and develop dashboards and scorecards that turn sales data into actionable insights.

Support design, development, and maintenance of data pipelines and infrastructure. Assist with ETL/ELT workflows, data ingestion, cleaning, validation, modeling, and warehousing. Document processes and collaborate with data teams to support analytics use cases.

Build and operate batch and near-real-time data pipelines, semantic models, certified measures, APIs, and serving infrastructure for AI-powered decision systems. Responsibilities include data quality, observability, lineage, governance, CI/CD, infrastructure as code, performance optimization, model scoring pipelines, and AI telemetry. Partner with data scientists and business stakeholders to productionize models and deliver reusable, documented data platform capabilities.

Designs and maintains data warehouse infrastructure, semantic and dimensional models, metrics layers, dashboards, reporting, and secure data pipelines. Builds governed self-service analytics in Sigma, Power BI, and Databricks; defines KPIs and data standards; implements row-level security and Entra ID controls; supports data quality, observability, AI/ML use cases, and BI adoption. Collaborates with technical and business stakeholders in an Agile-Scrum environment.

Supports GIS data creation, editing, cleanup, maintenance, QA/QC, standardization, and map product development for NASA Johnson Space Center facilities and utilities. Collaborates with field surveyors and external parties to transform and integrate data, creates story maps and dashboards, and edits data in a branch version environment. This temporary role requires three years of GIS experience, a relevant bachelor’s degree or equivalent experience, and U.S. continental location.

Lead a small analytics team (staff data scientist + two analysts) to deliver curriculum, product, and program insights. Split time between people management (60-70%) and hands-on analysis (30-40%), advance experimentation and measurement, build AI/LLM-driven agentic workflows, manage sprints and cross-functional stakeholder relationships, and drive analytics that inform curriculum, product, and intervention strategies.

Design and build PerfectServe’s unified cloud data platform, including ingestion pipelines, integrations, data models, metrics, and consumer-facing data layers. Establish architecture, engineering, quality, and testing standards; enable safe extraction from production systems; partner with data users; and mentor data engineers. The role requires substantial cloud data platform experience, deep SQL, warehouse expertise, ELT, change-data-capture, AWS, modeling, orchestration, and data quality skills.