Top Analysis Reporting Jobs in Cincinnati, OH

Lead analytics engineering for Revenue, Marketing, and Finance by building scalable data solutions, data models, and dashboards; provide training and consultation, support ad hoc data needs, and translate technical findings into executive-level insights to improve operational performance.

Own and evolve the data infrastructure powering identity and fraud products. Build and operate scalable ETL/ELT pipelines, ensure data quality and observability, optimize storage and access, support product squads, respond to production incidents, mentor engineers, and drive data platform innovation.

Lead the Data Platform Engineering team owning ETL/reverse-ETL pipelines, replication, and production datastores. Define architecture, roadmap, standards (IaC, observability, schema contracts), mentor engineers, drive incident response and durable fixes, and partner on cross-team technical decisions and operational reliability for data correctness, completeness, and timeliness.

Lead revenue-cycle performance analyses, diagnose root causes, quantify value (ROI/collections lift), and translate findings into pragmatic, stakeholder-ready recommendations. Partner cross-functionally to scope analyses, validate data, and drive strategic workstreams that inform product roadmap and client outcomes.

The Senior Data Analyst gathers BI requirements, analyzes enterprise and offline datasets, writes and optimizes SQL queries, and builds dashboards using Power BI, Tableau, and Domo. The role translates business needs into technical specifications, designs dashboard wireframes, validates data, documents processes, and collaborates with engineering, product, and business teams. Knowledge of the US trust business is essential for interpreting data within financial and regulatory contexts.

Lead financial reporting delivery and data pipeline operations while improving data controls, quality, lineage, audit readiness, and regulatory compliance. Automate manual workflows using Alteryx, Python, and SAS; resolve production issues; partner with Technology on data models, testing, and enhancements; and analyze datasets for stakeholder reporting. Contribute to metadata, data dictionaries, responsible AI, and scalable data modernization initiatives.

Lead design and ownership of analytical frameworks and cross-functional reporting for finance and operations. Translate leadership goals into structured analytics projects, standardize metrics and reporting, build business cases, deliver executive-level narratives, and develop predictive models and dashboards to inform strategic decisions across insurance operations.

Lead product analytics for a B2B SaaS product: define metrics and measurement frameworks, analyze user behavior and product performance, build self-service dashboards, partner cross-functionally to link usage to business outcomes, and translate data into strategic recommendations for product roadmap and GTM decisions.

The Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

Lead design and operation of large-scale data pipelines and data APIs. Build Spark/PySpark workflows on Databricks, optimize job performance, manage data quality and observability, and develop MCP servers and AI-agent integrations. Mentor engineers, define standards, support production incidents and on-call rotations, and collaborate with stakeholders to deliver scalable data platform products.