Top Analysis Reporting Jobs in Cleveland, OH

Lead BI and analytics for Listings, Marketing, and Media: own performance reporting and dashboards, run ad-hoc analyses, build forecasts, define KPIs, and translate findings into executive-ready recommendations while partnering across Product, Data, Finance, Marketing and GTM teams.

Design and maintain a dbt-based business data layer for GTM metrics, centralize and reconcile multi-source data, implement automated data quality and reconciliation checks, align and document metrics with stakeholders, mentor analytics team, and lead root-cause investigations into complex data anomalies.

Lead development and scaling of financial analytics: build data models, dashboards, and reporting; translate financial data into executive insights; embed predictive analytics; partner with Finance, IT, and business units to improve data quality, governance, forecasting, and automation.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

Design and implement enterprise data architectures and lakehouse/warehouse solutions using Microsoft Fabric, Databricks or similar platforms. Lead data modeling, ETL/ELT pipelines, cloud integration, CI/CD, and stakeholder-facing consulting engagements to deliver scalable, secure analytics systems.

Own and implement the canonical data model and governance for a multi-tenant MEP SaaS platform. Architect data for AI/ML readiness (RAG, embeddings, vector search), design polyglot persistence and lake/lakehouse pipelines, lead staged modernization and migrations, produce hands-on prototypes and in-repo guardrails, and partner with platform, DB engineering, and ML teams to ensure data quality, lineage, and observability.

Lead design and delivery of enterprise BI and analytics solutions for healthcare coding and clinical data. Define BI architecture, data models, ETL pipelines, cloud-native analytics, and implement agentic/generative AI analytics for global customers while guiding engineering best practices and mentoring teams.

Analyze business performance, provide insights, improve processes, participate in continuous improvement projects, and collaborate with cross-functional teams.

Analyze customer implementation and product usage to identify drivers of adoption, retention, and expansion. Build dashboards, quantify configuration and workflow impacts, surface deployment friction, and partner with Product, Sales, and Customer Success to create evidence-based playbooks and metrics that inform roadmap and go-to-market strategies.

Develop and maintain Python-based data applications and pipelines: fetch data from databases/APIs/files, build Pandas DataFrames/NumPy arrays, write complex SQL and data transformations, handle nested JSON, and collaborate with data science teams to deliver production data platforms.