Top Analysis Reporting Jobs in Hartford, CT

The Lead Data Platform Engineer will design and build Virtuous's data platform, ensuring it is secure, scalable, and AI-ready. Responsibilities include architecting access controls, optimizing performance, and enabling self-service analytics while maintaining governance and compliance standards.

As a Salesforce Technical Architect focused on Data & AI, you'll design and implement solutions using Salesforce Data 360, oversee technical projects, collaborate with various stakeholders, and mentor junior staff to drive AI innovations.

The Lead Partner Success Implementation Engineer drives partner success through strategic implementation, technical guidance, and collaboration, ensuring optimal platform usage and partner enablement.

Lead and expand the company's BI and analytics capabilities: set data strategy with senior leadership, manage analytics pipeline and BI stack, implement data governance, mentor and grow BI and data engineering teams, partner cross-functionally to drive data-informed decisions, and manage vendors and processes for scalability.

Oversee technical SEO work for clients, manage teams, define objectives and measure success against KPIs, while mentoring team members and ensuring high-quality delivery.

The Data Scientist will enhance statistical models for forecasting, analyze datasets for operational planning, and communicate insights to stakeholders.

The Data Engineer will develop and maintain data pipelines, build data models, ensure data quality, and support cross-functional teams while improving internal data platform infrastructure and documentation.

The Sr. FWA Analyst identifies fraud, waste, and abuse in medical billing through data analysis, supporting engineering and data science teams with validation and tuning of detection algorithms.

The Business Intelligence / Data Analyst will optimize the data reporting pipeline using Google Big Query and Omni, work with product management, implement reports, and conduct ongoing maintenance.

Lead enterprise data strategy and governance to establish semantic architecture, metadata, lineage, stewardship, and standards enabling trusted data for AI, analytics, and operations. Define operating model, policies, reference architectures, and roadmaps while partnering with Legal, Compliance, Security, Technology, and business leaders to ensure regulatory compliance and enterprise-wide adoption.