Top Remote Data & Analytics Jobs in Phoenix, AZ

Own and maintain data products end-to-end for sector teams, analyze large datasets, refine methodologies, automate workflows using AI and tooling, collaborate with Research, Engineering, and Product, and deliver accurate, client-facing insights and improvements.

Own production data ingestion pipelines for healthcare and benefits data, including normalization, mapping, validation, reconciliation, observability, and secure handling of sensitive information. Unify census data processes, scale the platform, improve identity matching, and build automation and TypeScript tooling. The role requires strong Python and SQL skills, production orchestration experience, reliable testing and data-quality practices, and effective communication with technical and nontechnical stakeholders.

Designs and implements Google Cloud database migration strategies, including architecture, data models, database objects, pipelines, orchestration, security, and data transfers. Advises customers on Cloud Spanner and Cloud SQL implementations, translates SQL and stored procedures, configures Google Cloud components, performs performance testing, resolves performance issues, and delivers technical workshops and architectural recommendations.

Develop and optimize marketing reports, monitor product and advertising campaign KPIs, integrate and visualize data, investigate performance deviations, analyze campaigns, and recommend improvements. The role requires strong Google Sheets skills, basic SQL, A/B testing, mathematical statistics, and English proficiency. Python, web analytics, Google Ads, and BI experience are preferred. This is a fully remote position.

Build and maintain Databricks data pipelines, ETL/ELT processes, SQL transformations, and Power BI dashboards for HR and People Analytics. Ensure data quality, optimize models and reporting performance, troubleshoot issues, document solutions, and collaborate with HR stakeholders and technical teams to deliver scalable workforce analytics.

Manage end-to-end data operations, including collection, processing, delivery, pipeline optimization, and data quality monitoring. Collaborate with engineering, product, and operations teams to improve data workflows, identify inefficiencies, and implement scalable solutions. Analyze data to provide actionable insights for decision-making and AI model development while communicating technical issues to non-technical stakeholders.

Designs, develops, and maintains scalable ETL pipelines, data lakes, warehouses, and integrated data solutions. The role includes requirements analysis, Agile collaboration, testing, debugging, documentation, cloud data engineering, and optimization of reliable and performant systems. It requires strong Python, SQL, database, distributed processing, data engineering, software development, and cloud platform skills, along with effective communication and problem-solving abilities.

Processes and validates financial, payroll, ledger, time-tracking, expense, project, and operational data. Converts information into accurate workbooks, templates, reports, and organized deliverables while maintaining employee, project, and expense records. Researches relevant company and industry information, organizes audit documentation, supports interview qualification summaries, and manages multiple data requests and deadlines in a remote environment.

Leads supply chain analytics, reporting, automation, and Power BI solutions while managing product readiness portfolios for launches and product changes. Coordinates cross-functional deliverables, governance forums, SKU readiness, risk escalation, documentation, and milestone tracking across commercialization projects. Translates business needs into scalable analytics and digital solutions, communicates insights, and drives stakeholder adoption and operational improvements.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.