Top Remote Analysis Reporting Jobs in Buffalo, NY

Leads WWT’s Data Reliability and AI Readiness pillar, setting enterprise AI data strategy and standards for trustworthy, reusable data. Oversees certification, lineage, quality SLAs, readiness scoring, data products, dashboards, and AI-powered reporting. Coordinates IT, AI engineering, analytics, business, and data teams on enterprise initiatives. Manages a Product Owner and engineers, improves operational data, promotes certified data products, and translates internal AI and data capabilities into customer-facing value.

Lead process mining and improvement projects across business areas, develop dashboards and analyses (e.g., Celonis), apply industrial engineering practices, identify root causes, translate functional requirements to technical solutions, and drive customer experience optimizations.

Leads WWT’s Data Reliability and AI Readiness pillar, establishing enterprise AI data strategy, governance, certification, lineage, quality standards, and readiness scoring. Coordinates cross-functional data initiatives, improves operational data, owns trusted data products and AI-powered reporting, and manages a product owner and engineering team. Partners with IT, AI engineering, analytics, and business teams to deliver reliable, reusable, production-ready data while improving data economics and translating internal capabilities into customer-facing offerings.

Own the strategy, governance, architecture, modeling, and reporting foundation for People Experience data. Build governed metrics, semantic and consolidated data layers, dashboards, data catalogs, quality monitoring, and operating processes from the ground up. Manage intake, prioritization, access, privacy, enablement, and self-service adoption while partnering with stakeholders on analytics and future AI capabilities. The role combines strategic program leadership with hands-on SQL, data modeling, reporting, and team mentoring.

Owns the end-to-end architecture, development, infrastructure, data pipelines, and Power BI reporting platform supporting data governance, informatics, analytics, auditing, and data quality. Leads and mentors a data engineering team, defines schemas and engineering standards, ensures security and HIPAA compliance, oversees testing and Agile delivery, manages stakeholder requirements, and remains hands-on with backend technologies, data models, Power BI, and DAX.

Design and maintain healthcare data integration pipelines, models, and validation processes for claims, billing, clinical, pharmacy, EMR, FHIR, and value-based care data. Collaborate with clients and internal analytics, product, and customer success teams to interpret data, document business rules, support implementations, and build reusable Ursa data models. Apply healthcare domain expertise, data security practices, HIPAA compliance, and appropriate judgment when using AI-assisted tools.

Senior individual contributor who designs, develops, optimizes, and maintains complex SQL data solutions and Microsoft SQL Server instances. Leads data modeling, performance tuning, schema design, and data quality efforts; partners with engineering, ETL, BI, and product teams; drives standards, architecture, governance, and compliance for scalable, high-performance data platforms.

Lead business analysis, requirements definition, system administration, integration development, and reporting for enterprise systems and data initiatives. Partner with stakeholders to design solutions, manage user access, troubleshoot systems, support upgrades and migrations, and develop reporting in Kantata, NetSuite, Salesforce, and Power BI.

Lead design of end-to-end analytics and data solutions, produce architecture diagrams and technical designs, guide delivery teams, run client discovery and requirements sessions, support pre-sales and proposals, build reusable frameworks, and manage and mentor a delivery team to ensure quality and alignment to architecture.

Design, build, and maintain Power BI and Tableau dashboards; analyze large, complex datasets using SQL, Python, and statistical methods; partner with data engineering and business stakeholders to ensure data quality; translate findings into clear, actionable stories and executive-level presentations; support firmwide analytics growth and automation.