Top Senior Data & Analytics Jobs in Cleveland, OH

This role involves leading data architecture strategies, collaborating with stakeholders, and implementing advanced data technologies to drive business growth at PwC.

This role oversees the design and evolution of finance reporting solutions using Power BI, collaborating with global finance teams to create scalable analytics and insights.

The role involves assessing fraud program effectiveness, compliance with regulations, and providing strategic insights for risk management within the financial services industry.

Design and implement scalable go-to-market intelligence workflows and automation. Define business requirements for reports and dashboards, partner with Product and Analytics, improve Data Cloud tool adoption, document processes, support integrations, and deliver automation-aligned pre-sales and client-facing materials.

Lead design and implementation of scalable, HIPAA-compliant data pipelines and platforms for healthcare ML. Build ETL, orchestration, and tooling for processing EHR, claims, pharmacy, and bioinformatics data; collaborate with data scientists to produce modeling-ready datasets and ensure data quality, reliability, and operational excellence.

As a Snowflake Data Architect, you will provide ETL support and specialize in Snowflake management, ensuring performance, governance, and optimization of data infrastructures.

Lead architect for Compliance data at Coinbase, defining data governance frameworks, designing data contracts, mapping data lineage, and quantifying data impact to enhance compliance reporting and data quality processes.

The Revenue Strategy & Planning Manager will develop actionable growth plans by analyzing data, building reports, and collaborating with Sales leaders to enhance B2B revenue strategies.

As a Senior Analytics Engineer, you will design and maintain reliable data pipelines, improve data workflows, and mentor teammates to influence analytics practices at Spark.

As a Data Architect at RapidScale, you'll lead data platform engagements, ensuring delivery quality, managing teams, and designing cloud architectures while supporting ML implementations and client interactions.