Top Senior Analytics Jobs in Phoenix, AZ

Leads the design and implementation of AI-powered data pipelines, enterprise feature stores, analytics-ready repositories, and self-service data access layers. Builds automated feature engineering, data quality, observability, and governance capabilities for large-scale data science workloads. Partners with Enterprise IT, establishes data availability and quality SLAs, integrates structured and unstructured data sources, and leads teams delivering scalable analytics infrastructure.

Leads analytics for SEO, AI search, website experience, CRO, and the organic enrollment funnel. Diagnoses performance across traffic, visibility, conversions, leads, applications, and enrollments; designs measurement approaches; reconciles web, search, CRM, and platform data; defines KPI and reporting requirements; and translates findings into executive recommendations. Partners with analytics and engineering teams to build scalable Databricks-based reporting and improve measurement of organic and AEO initiatives.

Own the Finance and Operations data layer, integrating systems into Snowflake and Omni, building reusable data models, dashboards, governed metrics, validation checks, and reconciliation workflows. Partner with Finance, Operations, and Data teams to translate business processes into trusted analytics, improve data quality, automate reporting, and support leadership decision-making.

Develops and manages enterprise business intelligence products, including semantic models, governed definitions, metadata, dashboards, and AI-assisted analytics capabilities. Translates complex business needs into measurable analytic solutions, advises stakeholders, supports adoption and training, and manages product lifecycle, quality, performance, and business impact. Partners with product managers and business leaders to prioritize work, manage risks, communicate value, and guide less experienced team members.

Own and evolve the analytics platform, including data models, pipelines, metric definitions, semantic layers, dashboards, and self-service data products. Partner with cross-functional stakeholders to translate business needs into reliable technical solutions. Lead data initiatives, improve ELT reliability and governance, resolve quality issues, mentor teammates, and shape the Analytics team’s technical roadmap. The role requires strong SQL, dbt, modern data-stack experience, business communication, and independent execution in an ambiguous environment.

Leads enterprise analytical modeling and semantic architecture, establishes standards for testing, documentation, governance, lineage, observability, and access, and develops reusable governed data products. Partners across Product, Engineering, Finance, Clinical, Research, Marketing, and AI teams; guides architecture reviews, data quality ownership, semantic layer design, and governance adoption. Mentors engineers and drives modernization of analytical environments.

Develop and maintain Tableau dashboards, analyze HR and workforce data using SQL, and translate findings into actionable stories for executives and business leaders. The role defines HR metrics and reporting frameworks, supports data architecture and warehousing practices, collaborates on data pipelines, and ensures HR data accuracy, governance, integrity, and privacy. It partners with HR, Finance, IT, data engineering, and other stakeholders to improve workforce strategy, operational efficiency, and employee experience.

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.

Senior business analyst role focused on IBM Cognos ICM. Requires deep Cognos ICM experience, payment and compensation processing domain knowledge, and expertise with Agile and Waterfall delivery. Involves product management exposure for sales performance, payments, BI/analytics, and customer/B2B processes.

Senior business-analyst role requiring 8–10 years experience with 3–5 years in IBM Cognos ICM. Must understand Cognos ICM business functionality, have payments and compensation processing domain knowledge, and be proficient with Agile and Waterfall methodologies. Product management experience in ICM sales performance, payments, compensation, BI/analytics is a plus.

Lead Cognos ICM business analysis and architecture for sales performance, payments, and compensation processing. Translate business needs into ICM solution designs, apply Agile and Waterfall methods, and support BI/analytics and product strategy for compensation systems.

Lead end-to-end analytical initiatives on partner enrollment funnels, ensure data quality, build reporting pipelines with BI/engineering, use AI-assisted tools to accelerate analysis, translate insights into strategic recommendations for leadership, and coach growing analytics team members.

Lead product analytics for Guest or Host App: analyze large datasets, design and evaluate experiments, build metrics and reporting tools, apply statistical and causal methods, partner cross-functionally, present insights to stakeholders, and leverage AI to automate tasks.

Transforms enterprise IT operations, monitoring, KPI, and performance data into executive dashboards, heatmaps, trend analyses, forecasts, anomaly detection, and narrative insights. Integrates data from monitoring, ticketing, incident, and operational systems; develops standardized reporting templates; documents analytical workflows; and collaborates with engineering, service desk, cybersecurity, and PMO teams to support federal reporting and leadership decisions.

Designs and maintains executive dashboards, reports, semantic models, and automated analytics solutions using Power BI. Defines enterprise KPIs, data standards, and business rules while integrating data from ERP, CRM, manufacturing, SQL, and cloud systems. Partners cross-functionally with senior leaders to deliver insights, improve data quality, support master data management, and enable scalable self-service analytics and data-driven decision-making.

Leads analytics engineering and analytics-ready data architecture for government clients. Designs standardized data models, transformation logic, validation frameworks, and reusable integration patterns. Governs data quality, QA, backfills, and release processes while resolving complex data issues. Integrates web, email, social, survey, and client-system data to support dashboards, reporting, measurement, and insights. Advises stakeholders, influences enterprise standards, coordinates with product teams, and supports client-facing analytics workstreams.

Build and operate reliable ETL and ELT pipelines across lakehouse and transactional systems. Develop data models, enforce governance, integrate diverse data sources, and support analytics, APIs, and predictive tools. Monitor performance, data quality, lineage, cost, and uptime while collaborating with data science, AI, product, and innovation teams.

Partners with product, business, content operations, and data teams to define measurement frameworks, analyze media production workflows, build prototypes and dashboards, strengthen data models, and provide data-driven insights for strategic decisions. The role focuses on measuring GenAI-enabled production improvements, operational efficiency, creative flexibility, and innovation impact while influencing stakeholders and leadership.

Leads web analytics strategy, GA4 and GTM implementations, tagging specifications, dashboards, reporting, experimentation, and data-quality monitoring. Analyzes user behavior, conversion funnels, feature adoption, and A/B test results to provide actionable recommendations for product and business decisions. Partners with product, engineering, UX, and business stakeholders and communicates analytical findings to technical and non-technical audiences.

Leads the Business Intelligence and Revenue Analytics function, balancing team management with hands-on analysis. Develops dashboards, KPI frameworks, forecasts, and reporting for Sales, Marketing, Revenue Operations, and executives. Partners with Data Engineering on data models, pipelines, governance, and quality. Translates business needs into analytical solutions, delivers actionable insights, manages the analytics roadmap, prioritizes initiatives, and coaches a small team.

Leads the enterprise analytics organization, managing Analytics Engineers, Data Analysts, and BI Developers. Defines analytics strategy, delivers executive dashboards and operational reporting, establishes KPI and governance standards, builds semantic models and curated datasets, and drives self-service BI adoption. Partners with Engineering, Product, Finance, Operations, Customer Success, and executives to translate business needs into trusted analytical solutions while developing talent and prioritizing analytics initiatives.

Leads the enterprise analytics organization, managing Analytics Engineers, Data Analysts, and BI Developers. Defines analytics strategy; delivers executive dashboards, KPI scorecards, operational reporting, semantic models, and self-service BI. Establishes data governance and metric standards, ensures data quality, partners with Engineering, Product, Finance, Operations, and executives, and translates business questions into actionable analytical solutions. Mentors analytics talent and drives prioritization, adoption, and measurable business outcomes.

Own and scale client-facing data transformation pipelines for financial and operational data. Build reliable analytical models, data integrity tests, and integrations across diverse sources. Partner with Customer Success and Engineering to troubleshoot data issues, optimize critical pipelines, establish engineering standards, and improve developer experience. Use dbt, SQL, Python, BigQuery, and AI-enabled workflows to deliver performant, well-documented FP&A data products while mentoring teammates and contributing to hiring and onboarding.

Lead analytical support for performance marketing by evaluating CAC, ROAS, LTV, conversion, retention, and acquisition efficiency. Conduct deep-dive analyses, experimentation, incrementality studies, and marketing measurement using SQL, Python, and visualization tools. Build scalable reporting frameworks, improve data quality, communicate insights to senior stakeholders, influence channel and budget decisions, and mentor junior analysts.

Own Marketing’s reporting and analytics layer by building executive dashboards, defining KPIs, modeling data across GTM systems, and establishing governance and trusted sources of truth. Partner cross-functionally to measure funnel performance, attribution, campaigns, pipeline, and ROI. Help evaluate and develop AI-powered analytics, conversational reporting, automated insights, and intelligent visualization while translating complex data into actionable recommendations for leadership.