Top Analytics Jobs in Phoenix, AZ

Leads enterprise internal fraud analytics, monitoring, and response initiatives. Uses SAS and SQL to analyze complex data, develop fraud rules, identify anomalous activity, and design proactive monitoring solutions. Partners with executives, risk teams, audit, and control functions to deliver defensible, well-governed analytics that reduce fraud risk and withstand regulatory scrutiny. Provides subject matter expertise, consultative guidance, and executive-level communication in an ambiguous, high-scrutiny environment.

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.

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.

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.

Leverage data analytics, business intelligence, and data science techniques to deliver strategic insights and solutions for clients. Build reports, dashboards, data pipelines, visualizations, and data architecture using tools such as Oracle BI, QlikView, Spotfire, and BIRT. Ensure data quality, manage stakeholder relationships, improve analytics processes, interpret complex datasets, and mentor junior team members while contributing to client projects and business strategy.

Lead programmatic analytics for clients and internal teams by analyzing campaign and model performance, designing tests, developing measurement solutions, recommending optimizations, and presenting actionable insights. Partner with Sales, Customer Success, Product, Engineering, and clients to align marketing strategies, reporting requirements, and optimization efforts with business goals.

Leads the strategy, architecture, implementation, and evolution of enterprise analytics platforms. Responsibilities include designing scalable data platforms, integrating digital and enterprise data, establishing governance and security standards, optimizing analytics and BI capabilities, evaluating cloud technologies, managing platform performance and risks, and mentoring technical teams. The role also requires close collaboration with executives, clients, Product, Engineering, Data Science, Marketing, and business stakeholders.

Lead and scale Runpod’s analytics and business intelligence function, managing analysts and BI practitioners while setting the analytics roadmap. Own core company metrics, consumption and marketplace analytics, self-service reporting, experimentation practices, and executive and board-level insights. Partner with Product, Finance, Engineering, Security, Supply, and Go-to-Market leaders to improve data quality, modeling, decision-making, and strategic outcomes.

Analyze healthcare, financial, and utilization data to identify inefficiencies and support data-driven decisions. Build models, tools, and operational dashboards; communicate insights clearly to technical and non-technical stakeholders; monitor initiative performance; and collaborate across teams to improve healthcare affordability and quality. The role also requires compliance with applicable laws and regulations and may support strategic projects.

Design and maintain analytical data models, transformation frameworks, data-quality checks, semantic definitions, and metric consistency across analytics and customer-facing products. Partner with engineering, product, and analytics teams to document data, improve lineage, and enable trustworthy self-service data usage. The role requires advanced SQL, analytics engineering, dimensional modeling, testing, and collaborative translation of business concepts into precise data definitions.

Leads analytics for Pricing and Supply, managing a team of approximately eight analysts. Partners with cross-functional leaders to prioritize analytical work, build dashboards and decision frameworks, improve metric quality, and generate insights into pricing, supplier coverage, marketplace performance, conversion, margin, and customer behavior. Coaches analysts, establishes scalable analytics processes, and collaborates with data engineering teams to improve data models, tooling, and self-service capabilities.

Leads business and data analytics for P&C digital experience transformation. Oversees data strategy, governance, analytics platforms, reporting, information delivery, and advanced AI-enabled solutions. Guides cross-functional teams, influences architecture and prioritization, develops strategic insights, manages risk, and delivers scalable data capabilities supporting member and insurance professional experiences.

Analyze multi-channel fraud trends, develop data-driven insights and recommendations to prevent and detect fraud, evaluate controls and authentication strategies, support testing and implementation of fraud controls, and present executive-ready analytics to inform enterprise fraud strategy and prioritization.

Analyze complex datasets, build dashboards and reporting solutions, ensure data quality, automate processes, and present actionable insights to business leaders. The role partners with stakeholders across Operations to solve business challenges, improve performance, and support strategic decisions using SQL, DOMO, Alteryx, and related analytics technologies.

Leads long-range workforce and resource planning for contact center operations. Owns demand forecasting, staffing, capacity, headcount, cross-skill, and scenario models across business lines and channels. Develops advanced predictive and prescriptive analytics, performance metrics, and decision-support tools using complex data sets. Advises executives on resource allocation, service levels, costs, and business performance while governing data quality and analytical processes. Leads capacity planning analysts and drives continuous improvement through automation, machine learning, and improved forecasting methodologies.

Analyzes complex property and casualty insurance data to optimize underwriting strategies, assess risk, and support profitable growth. The role integrates disparate data sources, applies statistical and quantitative methods, conducts exploratory analysis, develops visualizations, documents methodologies, and presents actionable recommendations to business leaders. It collaborates with cross-functional teams, influences strategy, and ensures analytical activities comply with risk and regulatory requirements.

Leads architecture and engineering standards for Curaleaf’s data and analytics ecosystem. Designs scalable Snowflake platforms, ingestion and transformation pipelines, data models, governance, testing, CI/CD, security, observability, and cost optimization. Partners with engineering, analytics, data science, governance, and business teams to deliver trusted data products. Provides technical direction, mentorship, incident leadership, and cross-functional alignment as a hands-on principal individual contributor.

Reviews, develops, validates, and refines dashboards, reports, visualizations, calculations, data sources, KPIs, and analytical workflows across BI platforms. The role documents findings, provides structured feedback, troubleshoots reporting accuracy, and evaluates AI-powered agents and workplace connectors used in analytics processes.

Own weekly and monthly sales performance reporting across SPINS, Circana, retailer portals, distributor feeds, and shelf data. Diagnose velocity, distribution, pricing, promotions, and competitive share; identify risks and opportunities; build buyer-facing category and sell-in decks; evaluate promotions and trade ROI; support Costco planning; and develop dashboards and scorecards that turn sales data into actionable insights.

Build and own commercial data models in Snowflake and dbt for ARR, bookings, pipeline, account scoring, ICP fit, upsell, and whitespace analysis. Establish semantic layers, governance, documentation, testing, monitoring, and CI/CD practices. Partner with Sales, Marketing, Finance, and enterprise data teams to deliver trusted self-service analytics, improve data infrastructure, and develop AI-powered automation. This senior individual contributor will own the roadmap and mentor analysts on data modeling and dbt.

Build scalable data foundations, analytics tooling, analysis pipelines, and trusted datasets for product and data science teams. Establish instrumentation and experimentation standards, improve data quality and discoverability, document metrics and workflows, and collaborate across Product, Engineering, Data Science, Data Engineering, and Business Intelligence. Use AI to accelerate engineering while validating outputs through testing, data validation, source-checking, and peer review.

Own client relationships and analytics strategy across digital measurement and GEO initiatives. Architect, audit, and QA GA4 and Adobe Analytics implementations; develop measurement frameworks, dashboards, experimentation plans, and causal analyses. Present insights to executive stakeholders, manage analytics workstreams, mentor teammates, and build AI-assisted workflows, automations, and scalable systems. The role combines traditional analytics with measurement of AI-powered search visibility and customer behavior.

Designs scalable ETL/ELT pipelines, analytics-ready data models, and executive dashboards using Python, SQL, Tableau, Power BI, and cloud tools. Performs data mining, modeling, validation, documentation, and reporting while collaborating with technical and business stakeholders. Provides mentorship and technical guidance on analytics engineering, workflow automation, and data best practices. The role is fully remote, requires U.S. citizenship, and requires or permits obtaining Public Trust and Top Secret eligibility.