Top Senior Analysis Reporting Jobs in Buffalo, NY

Lead the technical direction, architecture, and roadmap for the company’s data platform. Design reliable large-scale pipelines, establish standards for modeling, testing, observability, and orchestration, and mentor data engineers. Partner with engineering, product, analytics, and business stakeholders to align infrastructure investments. Lead incident root-cause analysis, evaluate data technologies, and support scalable analytics, machine learning, and real-time processing capabilities.

Own the digital measurement ecosystem across websites, quote engines, customer portals, and account applications. Lead Google Tag Manager tracking frameworks, data-layer standards, Google Analytics 4 configuration, conversion events, attribution, funnel analysis, experimentation measurement, data-quality audits, and analytics governance. Partner with Marketing, Product, Engineering, and Data teams to translate customer behavior into actionable insights that improve growth, conversion, and customer experience.

Design and build petabyte-scale multimodal data pipelines and infrastructure (text, audio, images, video). Own and maintain systems like Spark, Ray, and vector databases, partner with pre/post-training teams to improve data recipes through experimentation, and support large-scale dataset storage and processing for AI model training.

Designs and operates an AWS-native lakehouse supporting analytics, reporting, visualization, and AI/ML. Builds batch and streaming ingestion pipelines using Python and PySpark, Apache Iceberg data structures, governance and metadata services, quality controls, security, observability, and CI/CD automation. Optimizes performance, scalability, reliability, and cost while collaborating with cross-functional technical teams and documenting architecture, operations, and secure configurations.

Administers, designs, secures, monitors, and optimizes large-scale client/server databases. Responsibilities include database programming, performance tuning, backup and disaster recovery, access control, auditing, documentation, systems integration, and advising users. The role requires supporting database projects in large organizations, working across operating systems and cloud platforms, and maintaining sensitive data security. A Secret clearance and DoD IAT Level II certification are required.

Lead enterprise data architecture for Privia Health’s Snowflake platform, defining scalable modeling, integration, governance, security, and optimization standards. Design Medallion Architecture layers and healthcare data schemas spanning EMRs, claims, eligibility, and value-based care data. Establish dbt, Python, testing, orchestration, and CI/CD best practices; enforce HIPAA, PHI/PII masking, access controls, lineage, and cataloging. Provide technical leadership, design reviews, mentorship, and cross-functional guidance to engineering, analytics, clinical, and product stakeholders.

Own and enhance Sigma dashboards and business intelligence reporting for the Records Retrieval business. Partner with stakeholders to translate business needs into scalable data solutions, analyze complex datasets, improve data quality and governance, define reporting standards, and support Snowflake data warehouse and modeling initiatives. Deliver actionable insights, automate reporting processes, evaluate analytics tools, and independently drive analytics projects from requirements through delivery.

Build and operate trusted data products, governed datasets, scalable pipelines, and platform workflows supporting analytics, AI, and business operations. Responsibilities include data quality, validation, observability, recoverability, semantic consistency, governance controls, permissions, privacy-aware access, automation, and production monitoring. The role partners with stakeholders to resolve ambiguous data needs, drives platform improvements, provides technical direction, and adopts agentic development workflows.

Leads College Board’s Operations analytics strategy and team, developing forecasting, predictive modeling, optimization, cost assessment, and decision-support capabilities. Partners with senior leaders across Operations, Product, Program, Technology, and Finance to improve execution, customer experience, efficiency, and cost savings. Responsibilities include setting technical direction, embedding analytics into operational decisions, presenting insights, hiring and coaching analytics staff, and building future AI and data-infrastructure capabilities.

Own healthcare analytics data methodology, including episode grouping, provider attribution, cost standardization, quality measures, reference data, pipelines, QA, and production delivery. Translate feature requests into specifications and validated analytics. Reconcile results, establish recurring data-quality coverage, investigate failures, and collaborate with engineering, data science, product, clinical informatics, and customer teams. Advanced SQL and healthcare claims expertise are central to ensuring accurate, defensible customer metrics.