Top Senior Analysis Reporting Jobs in Tucson, AZ

Provides data analytics consulting for enterprise clients using Dynatrace observability, digital experience monitoring, and business analytics data. Manages client relationships, performs ad hoc analysis, creates reports and dashboards, presents findings to technical and executive audiences, configures tools, trains users, and influences product improvements. The role translates complex performance and engagement data into strategies that improve digital experiences and business outcomes.

Leads enterprise data and analytics strategy, architecture, modernization, and delivery across cloud and on-premises environments. Oversees data platforms, engineering, integration, governance, quality, lineage, security, and compliance. Partners with executives to define roadmaps and investments, manages cross-functional teams and vendors, enables AI/ML capabilities, and mentors data professionals while driving scalable, trusted data solutions and measurable business outcomes.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.

Manages programmatic analytics and insights for client accounts, leading measurement frameworks, brand lift studies, incrementality testing, and A/B experiments. Develops media and campaign strategies, communicates performance insights to clients, and supports pitches and QBRs. Oversees, mentors, and develops analysts while partnering with Product, Engineering, Sales, and Customer Success teams to improve measurement tools, campaign optimization, and analytical capabilities.

Leads global contact center capacity planning, forecasting, headcount, and hiring strategies. Builds predictive staffing models using Python, analyzes contact flows and staffing accuracy, and develops reports with MicroStrategy, Alteryx, and Power BI. Communicates data-driven recommendations to executives, partner managers, vendors, and operations teams while managing planning initiatives and optimizing service levels, customer experience, and costs.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

Owns fraud performance analytics for depository products, including monitoring losses, approval rates, false positives, customer friction, and risk metrics. Builds data models, transformation pipelines, dashboards, scorecards, and monitoring frameworks. Conducts fraud deep dives and develops optimized rules, policies, thresholds, models, and decision strategies. Partners with Product, Engineering, Machine Learning, Data Engineering, Fraud Operations, and Risk teams to improve fraud detection and support product launches.

Leads analytics strategy and technical delivery for clients, specializing in Adobe Analytics and Customer Journey Analytics. Conducts measurement workshops, defines KPI and implementation strategies, oversees tagging and data quality, guides experimentation, and reviews dashboards and insights. Advises senior stakeholders, manages and mentors consultants, supports proposals and account growth, and contributes to Adobe Experience Platform initiatives involving Target, RT-CDP, and AJO.

Leads Toast’s analytics engineering function, owning enterprise data architecture, modeling strategy, analytics-layer development, and AI-ready semantic-layer vision. Builds and manages a team of analytics engineers, establishes roadmaps, partners with data engineering and business leaders, and drives scalable, governed data solutions across domains. Requires expertise in data modeling, dbt, SQL, stakeholder alignment, and people leadership.

Lead and grow a global data engineering team while establishing foundational data platforms and engineering standards. Design and oversee GCP-based pipelines, medallion data models, DataOps, monitoring, SLAs, CI/CD, governance, and data quality practices. Drive AI adoption across engineering workflows and build reliable, AI-ready data. Partner with business and technology stakeholders to prioritize roadmaps, deliver scalable solutions, and support data democratization.