Top Senior Data & Analytics Jobs in Cleveland, OH

The Data Architect will design and optimize data warehouses, analyze business intelligence data, and lead integration efforts to ensure solutions meet quality standards.

Lead the development and implementation of data architecture strategies, mentor a team to deliver solutions, and collaborate with stakeholders to enhance resources while ensuring project success and quality outcomes.

Develop and productionize safety and performance metrics for autonomous driving by analyzing large-scale vehicle, simulation, and external data. Build scalable data pipelines, interactive visualizations, and analytics infrastructure to support safety assessment, monitoring, investigations, and stakeholder decision-making. Apply engineering judgment, signal processing, and statistics to derive analysis-ready features and evidence for launch readiness.

The Analytics Manager will manage credit strategies for the Shopify portfolio, leveraging data analytics, collaborating with cross-functional teams, and enhancing underwriting frameworks.

The Senior Manager of Analytics will lead analytics on enterprise accounts, drive strategic insights for clients, oversee analytics delivery, and innovate offerings while collaborating with internal teams to enhance analytics capabilities.

Lead the data engineering team, develop and maintain data models and pipelines for risk domains, and manage data quality and governance.

Analyze auction data to identify performance drivers and risks, collaborate with commercial teams, and provide actionable insights to optimize DSP outcomes.

The Lead Data Analyst will define KPIs, analyze data trends, build dashboards, and support new product launches, requiring strong SQL and Python skills.

Lead analytics implementations using Google Analytics and Tag Manager for clients, providing mentorship and managing projects with a focus on enhancing clients' digital strategies.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.