Top Senior Analysis Reporting Jobs in Buffalo, NY

Design and develop scalable analytics tools, dashboards, evaluation frameworks, and observability systems for promotional media and marketing workflows. Partner with machine learning engineers, data scientists, product managers, and business stakeholders to monitor algorithm adoption, pipeline health, performance drift, and SLA adherence. Translate ambiguous creative and quality requirements into measurable objectives, improve recommendation and asset-generation models, and use data insights to guide business decisions.

Provides reporting, analytics, and business intelligence consulting for UKG Pro HCM and Pro WFM implementations, upgrades, migrations, and redesigns. Designs, develops, tests, deploys, and supports reports, dashboards, custom data views, business structures, and calculated fields. Translates business requirements into specifications, delivers training and go-live support, documents solutions, contributes to methodologies, mentors consultants, and supports sales and marketing initiatives. The role includes up to 10% nationwide travel and a hybrid home-office/client-site environment.

Owns data operations supporting client reporting, campaign delivery, and month-end billing. Troubleshoots data and backend issues across Salesforce, Fivetran, Snowflake, Sigma, and partner platforms; builds workbooks, views, models, dashboards, and custom analyses; manages permissions, onboarding, data feeds, and external data delivery; resolves campaign, reporting, reconciliation, attribution, and data-quality issues; automates processes and coordinates cross-functional resolution.

Build and optimize end-to-end data pipelines, production-grade dbt models, BigQuery schemas, Fivetran integrations, and Looker semantic layers. Partner with clinical, operations, finance, and growth teams to translate business questions into analytics projects focused on acquisition, attribution, conversion, and funnel performance. Establish data quality, governance, documentation, testing, and version-control practices while mentoring analysts and supporting secure, compliant data use.

Own the analytical function for a core business area, defining business questions, metrics, reports, analyses, experiments, and recommendations. Partner with product, growth, finance, and operations leaders to drive decisions and measurable outcomes. The role requires solving ambiguous problems, independently structuring and interpreting data, communicating insights persuasively, and executing initiatives end to end.

Own the analytical function for a business unit spanning growth, product, AI and automation, or strategic partnerships. Define business problems and metrics, build reports, analyze data, design experiments, generate insights, influence leadership decisions, and drive initiatives through execution. The role requires strong first-principles thinking, independent data skills, persuasive communication, and ownership of ambiguous, high-impact problems.

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.