Top Remote Data & Analytics Jobs in Tucson, AZ

Lead design and evolution of Foodsmart's data platform and pipelines, set architecture and standards for data quality, governance, and observability, enable analytics and ML at scale, mentor engineers, and drive platform-wide performance and reliability improvements.

Own end-to-end analytics modeling for a business domain: build and maintain dbt models and business marts on Snowflake, develop the BI semantic layer, define consistent metrics, create self-serve data products, and partner with Finance, Sales, Marketing, CS, and RevOps. Validate upstream data with the Data Platform team and translate ambiguous business questions into production-ready data models.

Manage the full data quality lifecycle for constituent records in Salesforce: deduplication, merges, data appends/enrichment, imports/exports, vendor management, audits, governance, reporting, staff training, and supervise a team focused on CRM data hygiene to support nonprofit fundraising operations.

Support HRIS implementation by extracting, cleansing, validating, and preparing employee data for migration. Reconcile reports, fix duplicates and missing data, create Excel transformation files, maintain data quality, document work, and assist with testing and post-go-live support.

Lead process mining and improvement projects across business areas, develop dashboards and analyses (e.g., Celonis), apply industrial engineering practices, identify root causes, translate functional requirements to technical solutions, and drive customer experience optimizations.

The Data Engineer will build and maintain scalable data pipelines, improve ETL processes, collaborate with AI/ML teams, and contribute to engineering culture.

Design and analyze experiments, measure campaign incrementality, identify customer segments and engagement opportunities, validate tracking and anomalies, surface actionable insights, and build scalable measurement infrastructure with cross-functional partners.

Design, build, and optimize scalable batch and streaming data pipelines. Model and process structured and unstructured data, ensure production reliability and observability, apply ML techniques for insight extraction, and drive data strategy while adopting modern tooling and IaC practices.

Partner with cross-functional teams to design experiments, analyze large datasets, build dashboards and metrics, identify trends and customer behaviors, improve data quality and instrumentation, and provide actionable recommendations that drive strategic decisions and operational improvements.

Work across data infrastructure and analytics: build and maintain dbt models, ingest and transform data, create dashboards and semantic layers in Power BI, validate AI-generated code, partner with stakeholders to deliver analyses and data products, and improve data governance and documentation.