Top Remote Data & Analytics Jobs in San Antonio, TX

Lead and grow a casino analytics team to measure, diagnose, and optimize Casino revenue performance. Provide strategic analytics support to Revenue Operations, run deep-dive customer and promotion analyses, build scalable monitoring/alerting frameworks, translate insights into actionable recommendations, and manage an analytics roadmap prioritizing short-term wins and long-term AI-enabled strategies.

Support sales intelligence by collecting, preparing, and analyzing sales and marketing data; maintain dashboards and data quality; run pipeline and win/loss analyses; translate findings into clear visualizations and reports for stakeholders.

As a Senior Data Analyst, you will analyze marketing performance, implement multi-touch attribution, and provide insights for strategic decisions.

The Senior Data Scientist will develop and deploy analytical models supporting marketing strategies, assess campaign effectiveness, and optimize customer engagement strategies, employing advanced data-driven methods and partnering with marketing stakeholders.

Maintain and improve eligibility and claims data feeds, analyze feed performance and errors, troubleshoot discrepancies, work with external feed providers to map and test integrations, and help improve internal tools, documentation, and processes.

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

The Senior Data Engineer II will design and maintain scalable data pipelines, collaborate with teams on machine learning workflows, and ensure data accessibility across systems.

The Analytics Engineer will manage BI modeling, create dashboards, ensure data quality, and collaborate with customers and teams for reporting needs.

The Market Risk Analyst will monitor SBL lines of credit, assess market risks, manage collateral, develop risk models, and prepare reports.

Lead the ML Data Enablement team to define strategy and execution for accelerating third-party and internal data onboarding, enforce data quality and SLAs, build metadata and lineage standards, and deliver scalable data infrastructure and workflows to reduce time-to-production for ML models.