Top Analysis Reporting Jobs in Cincinnati, OH

Design, build, and maintain scalable Snowflake data pipelines and workflows. Ingest, transform, and deliver data from EHRs, claims, APIs, and CRM systems into analytics-ready structures. Optimize Snowflake performance and costs through warehouse sizing, query tuning, data modeling, and security design. Implement orchestration, DevOps, version control, and CI/CD practices while collaborating with client architects and analysts. The role also involves explaining technical tradeoffs, supporting repeatable deployments, adapting to unfamiliar challenges, and mentoring others.

Document and reverse-engineer the client's ServiceNow data model and ITSM/CMDB processes. Design API-based incremental extraction into a Bronze layer and specify Bronze->Silver->Gold mappings. Define DNA crosswalk keys, data-quality gates and KPI confidence scoring, and support pipeline development, observability, and automated testing for ServiceNow as a source system.

Lead Wpromote’s Reporting Solutions team and strategy, overseeing client-facing BI and analytics products from requirements through production. Drive the shift from bespoke dashboards to scalable, reusable reporting experiences across client verticals. Partner with Data Engineering, Product, Analytics, Media, and clients on data models, metrics, visualization, UX, quality, and roadmap decisions. Guide innovation in self-service analytics, interactive applications, AI-enabled reporting, and conversational analytics while managing team development, prioritization, standards, and stakeholder communication.

Leads data engineering for LillyDirect’s patient analytics platform, designing privacy-first ingestion pipelines, tokenization, identity resolution, consent governance, semantic layers, and analytics-ready data products. Integrates pharmacy and prescription data across enterprise systems while ensuring HIPAA compliance, data quality, governed access, and consent revocation. Partners with product, pharmacy, privacy, legal, cybersecurity, and analytics teams to translate requirements into scalable solutions supporting BI, reporting, self-service analytics, and agentic AI.

Lead data analysis for product and customer decision-making by defining metrics, measuring adoption, engagement, retention, and customer health, and translating complex data into actionable insights. Partner with product, engineering, customer success, and sales teams; automate and improve data processes; support product strategy and customer reviews; strengthen data governance; and mentor analysts.

Lead and develop Analytics Engineers responsible for shared data foundations, platform capabilities, semantic models, developer tooling, and AI-enabled workflows. Define the team vision, operating model, roadmap, standards, and success measures. Partner cross-functionally to prioritize initiatives, improve adoption, guide architecture, and deliver scalable data assets and automation. Drive responsible AI-assisted development, data reliability, testing, documentation, observability, and cost management while contributing hands-on when needed.

Leads enterprise data engineering and platform execution, including modernization roadmaps, cloud transformation, governance, reliability, operational excellence, budgets, vendors, and investment prioritization. Directs multiple engineering managers and teams across platform engineering, data engineering, integration, and operations. Partners with architecture, security, compliance, analytics, clinical, product, finance, and business leaders to deliver secure, scalable, resilient data capabilities supporting AI, interoperability, reporting, and digital transformation.

Design, build, and maintain data pipelines, ingestion and transformation workflows, SQL-based processes, data models, and cloud data infrastructure for a B2B SaaS analytics platform. Monitor pipeline performance and data quality, troubleshoot failures, document data architecture and business logic, and collaborate with engineering, product, and analytics stakeholders.

Transforms complex retail and consumer data into business-ready datasets, scalable pipelines, reporting solutions, automation, and client deliverables. Designs data integration solutions in Unify, troubleshoots connector issues, explores AI-driven improvements, and partners with Product, Engineering, Client Development, Operations, and business stakeholders to define requirements and deliver scalable solutions.

Analyzes editorial and operational data to produce performance reporting, KPI insights, trend and variance analyses, executive presentations, and visualizations. The role reconciles data sources, defines metrics, supports AI-assisted content quality measurement, improves recurring reporting, and partners with editorial, finance, product, technology, and analytics teams to inform strategic decisions and leadership priorities.