Top Analysis Reporting Jobs in Cincinnati, OH

Leads a client’s migration from SQL Server, SSIS, and SSRS to Snowflake and Sigma while maintaining legacy reporting operations. Designs data models, ETL pipelines, reports, dashboards, source-to-target mappings, and migration plans; validates reporting parity and coordinates phased cutovers. The role also provides client-facing technical leadership, troubleshooting, documentation, peer reviews, and ongoing support for banking data and reporting systems.

Owns the health of a multi-client digital tracking ecosystem across 100+ properties. Responsibilities include managing Piwik PRO and Google Tag Manager configurations, deploying pixels and telemetry updates, monitoring and debugging conversion events, validating implementations with browser tools and APIs, supporting technical client onboarding, managing consent and UTM governance, documenting configurations, and coordinating technical escalations. The role also explains tracking behavior to nontechnical teams and improves automation and monitoring processes.

Design, develop, and operate large-scale data ingestion, transformation, enrichment, and processing pipelines across cloud, hybrid, and on-premises environments. Build distributed services using Java, Python, and Kafka; optimize data models and platform performance; deploy applications on Kubernetes; support infrastructure automation and CI/CD; troubleshoot production systems; and collaborate with customers and solution architects on implementations.

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.

Staff Data Engineer responsible for defining data architecture strategy, building fault-tolerant batch and real-time streaming infrastructure, and managing scalable lakehouse and database systems. The role processes telemetry from millions of connected devices, drives performance tuning and data quality, supports on-call operations, evaluates modern technologies, and mentors engineers. It requires strong expertise in Python, SQL, cloud data platforms, streaming systems, infrastructure as code, and enterprise-scale architecture.

Build and maintain finance data pipelines, warehouse models, dashboards, and automations for unit economics, technology budgeting, burn, runway, margins, deal desk, and sales compensation. Develop internal tools and LLM-based agents that help non-engineers access trusted financial data. Partner with finance, sales, and engineering leadership to reconcile discrepancies and improve revenue, cost, billing, payroll, cloud-spend, and accounting workflows.

Build and manage scalable ETL/ELT pipelines using Microsoft Fabric and Azure Databricks, implement medallion architecture, support data migrations and legacy SSRS decommissioning, develop Power BI data models, and maintain governance, security, and CI/CD practices. The role also supports migration events and modernizes data infrastructure for advanced analytics and reporting.

Analyze contributor performance, dataset quality, production efficiency, and client delivery metrics. Build dashboards and reports, conduct cohort and trend analysis, monitor quality benchmarks, identify data integrity issues, and translate findings into actionable recommendations. Collaborate with Data Operations, Engineering, Product, and Quality Assurance teams to define metrics and evaluate improvements.

Designs and maintains scalable data architecture, warehouses, ETL/ELT pipelines, and data platforms. Responsibilities include ensuring data quality, integrity, governance, accessibility, reliability, and performance; supporting customer onboarding and analytics tools; contributing to architecture and engineering best practices; troubleshooting complex data issues; and participating in on-call rotations. The role collaborates with engineering, product, solutions, business intelligence, and other cross-functional teams in healthcare and social care data environments.