Top Analytics Jobs in Phoenix, AZ

Own Eve’s go-to-market data models and semantic layer for governed metrics including pipeline, conversion, CAC, win rate, ARR, and bookings. Establish certification frameworks, modeling standards, documentation, alerting, and analytical application patterns across the organization. Partner with Sales, Marketing, Customer Success, and RevOps stakeholders, resolve conflicting metric definitions, enable AI-grounded data tools, review contributions, and mentor analytics engineers.

Analyze and optimize drug pricing using data-driven models, dashboards, reports, and strategic frameworks. Partner with Sales, Operations, Marketing, Finance, Product, pharmacy partners, and data teams to identify opportunities, define success metrics, improve reporting, and balance pharmacy economics with patient affordability. Serve as a trusted analytical advisor and support backend data development and query optimization.

Supports cross-channel marketing attribution and influence modeling by documenting data sources, defining business rules, mapping and normalizing interaction data, testing reporting prototypes, and recommending implementation steps. The intern also assists with AWS marketing data organization, segmentation research, data hygiene, process documentation, and stakeholder communications. The role requires analytical thinking, thorough documentation, clear communication, and collaboration across marketing and technical teams.

Leads enterprise data governance strategy, transformation, operating model modernization, accountability, and executive engagement. Partners with senior stakeholders to improve data trust, accessibility, regulatory alignment, risk management, analytics, AI readiness, and business outcomes. Represents the Data Office in strategic and regulatory forums, establishes roadmaps and performance measures, resolves cross-functional data challenges, and drives organizational change without direct authority. The role focuses on governance and transformation rather than engineering, coding, analytics delivery, or technical administration.

Leads a client-facing healthcare analytics team that designs and maintains prior authorization decision automation programs. Consults PaaS clients, develops strategic roadmaps, translates requirements into technical blueprints, analyzes authorization and claims data, and builds KPI reports using visualization tools. Manages direct reports, improves analytics processes and AI tooling, partners with product and engineering teams, and ensures automation outcomes and client KPIs are achieved.

Designs and maintains scalable data infrastructure, ETL/ELT pipelines, canonical datasets, and AI context layers. The role improves data quality and alignment across sales and finance systems, investigates data inconsistencies, leads requirements through UAT and implementation, supports analytics self-service, and partners with cross-functional teams to deliver process improvements and reliable business data.

Own Hometap’s product and marketing event-data ecosystem, including taxonomy, tracking plans, governance, Segment Protocols, schema validation, and source management. Partner with marketing, product, and engineering to define instrumentation and event specifications. Build monitoring for schema conformance, event volume, and property completeness, while documenting standards and managing event review and deprecation processes.

Leads an eight-person analytics engineering team and sets strategy for trusted semantic models, governed data, enterprise BI, AI-powered data access, and production data applications. Oversees data modeling, evaluation datasets, telemetry, LLM routing, governance, security, and self-service capabilities. Partners cross-functionally to establish engineering standards, roadmaps, and measurable business impact while hiring, coaching, and developing team members.

Own marketing measurement from initial engagement through closed-won revenue. Manage cross-channel reporting tied to Salesforce pipeline, tracking across GA4, advertising platforms, SEO, and AI search. Validate multi-touch attribution, analyze marketing experiments, model budget impact, and recommend investment priorities. Present performance insights to marketing and sales leadership across global regions.

Build and manage HR data pipelines, ETL/ELT processes, data warehouse models, and analytics workflows across cloud platforms. Ensure data quality, governance, security, and availability for People Analytics and HR stakeholders. Lead and mentor data engineers, collaborate with business and IT teams, and support reporting and analysis using tools such as Python, R, and business intelligence platforms.

Build Deepgram’s governed decision layer above warehouse gold tables. Responsibilities include modeling cross-system entities, owning semantic and metrics definitions, creating agent-facing data tools and MCP surfaces, implementing data quality assertions and reconciliation checks, evaluating AI agent accuracy, and answering business questions while leaving reusable models behind. The role requires production-grade SQL and Python, semantic-layer ownership, strong data modeling judgment, AI-native workflows, clear writing, and comfort building infrastructure from scratch.

Leads enterprise strategy and analytics initiatives, translating complex business challenges into actionable insights and executive recommendations. Oversees reporting frameworks, forecasting, dashboards, data quality, automation, and AI adoption while partnering with product, technology, operations, and data teams. Manages and develops an analytics team, drives measurable business value, and supports operational efficiency, customer outcomes, profitability, and continuous improvement.

Designs and maintains ETL processes, data warehouses, dashboards, analytics models, and reporting solutions. Develops pricing optimization algorithms, machine learning implementations, automated data processes, and self-service analytics tools using R, Python, SQL, and cloud technologies. Leads projects, mentors developers, documents systems, develops technology roadmaps, and supports production database performance and maintenance.

Build trusted analytics models, reports, dashboards, and light predictive analyses for revenue, finance, procurement, operations, and renewable energy stakeholders. Develop dbt models in Snowflake, analyze SAP data, maintain curtailment reporting, automate workflows with Python, validate data quality, and document metrics and data assets. Partner with business stakeholders to gather requirements, prioritize requests, and communicate insights. This hybrid role requires working in the Scottsdale office three days per week.

Teaches undergraduate and graduate business intelligence and analytics courses in a blended learning environment. Responsibilities include developing syllabi and lesson plans, preparing and updating course materials, using online technologies, setting course expectations, designing and grading assessments, providing timely student feedback, and integrating responsible AI practices. Faculty also participate in required training and ongoing professional development.

Leads fraud analytics to identify fraud methodologies, root causes, compromise points, volumes, and financial or operational impacts. Develops advanced analyses and actionable remediation recommendations for internal teams and participating financial institutions. Manages compromised credential processes, including identifying exposed credentials, social tokens, and payment information, assessing risk, and applying appropriate treatments. Requires experience in analytics coding, digital banking or payments, fraud risk management, and communicating insights to influence organizational change.

Serves as a trusted analytics advisor to leadership by investigating complex business, operational, and data challenges. Conducts research, root cause analysis, experimentation, and data exploration to identify risks, trends, and opportunities. Evaluates analytical methods and emerging technologies, partners with cross-functional stakeholders, and delivers actionable recommendations. Provides subject matter expertise and supports strategic initiatives in ambiguous, data-intensive environments.

Lead pricing strategy and origination risk analytics to optimize loan pricing, forecasting, and approval frameworks. Manage and mentor analysts, build quantitative models, integrate third-party data, run experiments, and present recommendations to senior leadership to drive growth and profitability.

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.

Leads business and data analytics strategy across information management, governance, reporting, analytics delivery, and data platforms. Oversees strategic initiatives, data modernization, reporting optimization, risk controls, and cross-functional teams. Provides executive-level insights, guides architecture and technology decisions, manages priorities and capacity, and drives data-informed business outcomes. Requires substantial analytics experience in financial services, people leadership, large-scale data analysis, Agile project leadership, and strong executive communication.

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.

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.

Own the analytics data layer by translating stakeholder needs into certified dimensional models, governed metrics, semantic layers, dashboards, and reports. Build with dbt, SQL, Databricks, Power BI, and Airflow while ensuring data quality, observability, reconciliation, documentation, and dependable orchestration. Partner closely with finance, operations, and client-facing teams, automate recurring workflows, and use AI-assisted engineering practices in a fully remote environment.

Leads Navitus’s centralized analytics team, delivering client performance analysis, financial modeling, underwriting support, pricing analysis, competitive intelligence, dashboards, and strategic recommendations. Partners with Sales, Finance, Clinical, Network Strategy, Account Management, and technical teams to support client growth, retention, RFPs, renewals, and market-facing engagements. Establishes data quality and governance standards, improves analytical processes, and mentors team members.