Top Remote Data & Analytics Jobs in San Luis Obispo, CA

As a Technical Solutions Manager, you'll onboard Enterprise customers onto Medallion's platform, focusing on data migration, integration, and automation for enhanced operational efficiency.

The Marketing Data Scientist will analyze data, onboard clients, develop data pipelines in R, and propose improvements to enhance efficiency.

Lead data strategy at Alpha, oversee data functions, build robust data systems, drive product decisions using data insights, and mentor a data team.

As a Data Scientist, you will analyze blockchain data to derive insights, develop and deploy predictive models, and improve product offerings.

Build reliable data models and semantic layer for AI-powered analyses. Support Product, Finance, and Customer Success with SQL-based analytics, BI dashboards, and stakeholder storytelling. Standardize data team processes, prioritize work, and collaborate across company to enable data-informed decisions.

Analyze player behavior and drive product innovation through data-driven insights. Collaborate with teams to design and evaluate experiments, maintaining data quality and delivering meaningful insights.

The Data Architect/Data Governance Lead supports the Medicaid Enterprise Data Warehouse by leading data architecture, metadata management, and governance processes, ensuring compliance and scalability.

Lead end-to-end digital analytics across websites, products, and campaigns: implement and govern event and conversion tracking, ensure data integrity, build dashboards, connect campaign and CRM data to full-funnel outcomes, generate actionable insights, and support AI-enabled reporting and experimentation to improve conversion and user experience.

As a Revenue Analytics Manager, you will own reporting for Sales, Marketing, and Customer Success, build dashboards, automate workflows, and ensure data quality.

Lead the technical roadmap and hands-on development of a scalable data platform, build analytics and data access solutions, ensure data quality/governance/observability, mentor and grow a team of data engineers, collaborate cross-functionally, and evaluate new big-data technologies.