Top Senior Analysis Reporting Jobs in Cleveland, OH

Operationalize enterprise data governance and privacy controls by supporting PII discovery, DSAR fulfillment, outbound data-sharing oversight, workflow/UAT, automation, governance analytics/reporting, documentation, training, issue triage, remediation coordination, and stakeholder engagement with Legal, Risk, InfoSec, and supplier teams.

Lead media analytics for Marriott Media Network by designing measurement methodologies, running A/B tests and MMM, analyzing audience and campaign performance, translating data into executive-ready insights, and partnering cross-functionally to optimize monetization and media strategy.

Use statistical modeling and data analysis to generate actionable insights that improve loan origination and production. Collaborate cross-functionally to design, test, and implement solutions, build dashboards and visualizations, maintain data integrity, and communicate findings to stakeholders to inform business strategy.

The Senior Quantitative Analyst will analyze marketing performance to drive business strategies, provide insights on campaign metrics, and collaborate with various teams for decision-making processes.

The IT Senior Analyst will gather requirements, lead testing, manage integration projects, and mentor team members in a high-paced environment.

Develop and maintain ETL solutions, optimize processes, validate data, and support production issues. Collaborate with analysts for efficient data integration.

Design, develop, and maintain ETL solutions using Informatica PowerCenter. Collaborate with business analysts, optimize ETL processes, and ensure data quality.

Own and operate Databricks production pipelines for Sugar Predict: monitor, alert, incident response, SLA reporting, performance and cost optimizations, migrate legacy ETL, onboard tenants, enforce Delta Lake architecture and multi-tenant isolation, apply security and governance, enable observability, maintain runbooks, and support CI/CD and on-call rotations.

Own day-to-day Snowflake administration and the Sigma semantic layer; manage ingestion (Fivetran), dbt modeling scope, reverse-ETL to Salesforce/HubSpot/NetSuite, data quality SLAs, reporting and dashboards, partner with managed data vendors, design automation and reproducible month-end financial analytics, and drive analysis and experiments for AdOps, Sales, and Finance.

Own and operate the Snowflake warehouse and Sigma semantic layer; manage ingestion (Fivetran), dbt model scoping and review, reverse-ETL, data quality SLAs, dashboards, month-end financial analytics, vendor management, and automation to support AdOps, Sales, Finance, and executive reporting.

Lead a small data engineering team partnering with Engagement Marketing to build scalable ETL/ELT pipelines, ensure data quality, define marketing metrics, integrate trusted data with ad platforms, and drive the data roadmap to enable measurement and growth.

Develop queueing and discrete-event simulation models and predictive/statistical models to identify bottlenecks, forecast operational outcomes, and support decision-making for personnel vetting, industrial security, and counterintelligence. Validate and document models, run scenario and sensitivity analyses, collaborate with data engineers, present results to technical and non-technical stakeholders, and recommend process improvements while working in a classified environment.

Formulate and solve large-scale mathematical optimization and resource-allocation problems (LP, IP, MIP). Build and validate optimization models and decision-support tools, apply heuristics and constraint programming, perform trade-space and sensitivity analyses, and present actionable recommendations to senior leadership in secure government contexts.

Join the BizOps & Analytics team embedded in a core business area to define problems, build analyses and reports, design experiments, surface insights, and drive data-informed decisions end-to-end to increase customer growth and business scale.

Embedded on a core business unit, own end-to-end analytics: define problems, design metrics and experiments, pull and analyze data, build reports, surface insights, and drive recommendations to inform product, growth, AI, or partnership decisions. Influence stakeholders and execute on outcomes.