Top Remote Data & Analytics Jobs in Phoenix, AZ

Leads enterprise data strategy, platform architecture, Master Data Management, federated governance, data quality, compliance, and real-time analytics. Builds scalable data infrastructure, modernizes legacy pipelines, establishes operational data hubs, and enables secure data products across the enterprise. Oversees data engineering, architecture, MDM, governance, and embedded analytics teams while partnering with executives and business units to drive transformation, operational efficiency, AI readiness, and acquisition integration.

Build and maintain data models, pipelines, metrics, dashboards, and analytics supporting identity graph construction, entity matching, and identity resolution. Partner with engineering and data science teams to productionize matching logic, evaluate match quality, monitor data quality and drift, and manage identity-provider relationships. Translate complex identity concepts into actionable insights for technical and non-technical stakeholders while helping define privacy-conscious product and data priorities.

Design and lead scalable, secure, highly available database infrastructure for multi-terabyte SQL Server environments. Set technical direction for data architecture, performance, real-time pipelines, security, and growth. Troubleshoot complex production issues, optimize queries and indexes, guide cross-functional projects, mentor engineers, review changes, reduce technical debt, and evaluate emerging technologies. Monitor database health using Splunk, Datadog, and Grafana while influencing engineering and Site Reliability strategy.

Own and maintain the data catalog, vendor and licensing records, dataset lineage, field definitions, and calculation documentation. Write SQL to trace, join, reconcile, and validate data; investigate discrepancies; QA derived metrics; prepare data extracts; and troubleshoot issues with engineering, offshore data teams, PMO, and customer service. Use AI tools to accelerate data work and maintain clear documentation for technical and non-technical stakeholders.

Lead data analysis, reporting, and storytelling for Health Insurance clients. Design studies and surveys, ensure questionnaire quality, analyze results, collaborate with client teams, and produce clear, actionable deliverables on schedule.

Leads stop loss claims data coordination for primary clients, identifies claimants, prepares claim-entry data, builds dashboards, presents insights to leadership, and drives continuous improvement initiatives. Requires at least three years of claims data analysis or operations experience, specific stop loss claims expertise, and a bachelor’s degree in Business or Analytics or comparable experience.

Develops and maintains data pipelines, datasets, schemas, models, databases, and data flows. Analyzes raw and structured data, improves data quality, builds reporting and data dictionaries, and collaborates with analysts, scientists, and stakeholders. The role supports data security, compliance, visualization, and reporting while modernizing legacy technologies. Requires a flexible schedule supporting 24/7/365 operations and the ability to obtain and maintain a security clearance.

Own and architect marketing and storefront analytics systems, including metric definitions, single sources of truth, multi-touch attribution, marketing mix modeling foundations, semantic models, and data health metrics. Partner with marketing, storefront, and leadership teams to improve acquisition measurement, budget allocation, data quality, and decision-making across international markets.

Design, build, and own scalable data pipelines and warehousing architecture from ingestion through analytics and incentive payouts. Lead data platform architecture, dbt modeling, data quality, lineage, observability, performance, and cost management. Partner with engineers and analysts on telematics accuracy and driver incentive reporting. Use AI coding tools daily to develop and review pipelines, SQL, and data models.

Develops Tableau and ServiceNow dashboards, KPIs, reports, and actionable data insights. Analyzes complex datasets, investigates root causes, applies machine learning and AI for forecasting and anomaly detection, supports data governance and quality, and recommends process improvements. Collaborates with cross-functional stakeholders and communicates findings to technical and non-technical audiences.