Top Senior Data Management Jobs in Tucson, AZ

Oversee data ingestion and transformation, manage finance data sources, ensure scalability and reliability of data platforms, and collaborate with stakeholders to align data engineering with organizational goals.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

Build and lead the Compliance data function, owning data products for Customer Risk, Transaction Monitoring, AML, Sanctions. Design and implement data risk frameworks (quality, usage, lineage), enforce data contracts between producers and consumers, maintain end-to-end lineage with Engineering, and drive a risk-based data roadmap aligned with compliance requirements.

Lead and manage a team of data engineers to develop and maintain data pipelines and warehouses, ensuring data quality and adherence to best practices.

Lead a team of data and infrastructure engineers to develop cloud-based data systems and solutions for analytics, ensuring best practices in data management and MLOps.

The Senior Manager will oversee operations across technology functions, lead strategic initiatives in Data & AI, and ensure effective management of technology projects and resources. Responsibilities include engaging stakeholders, identifying risks, and fostering continuous improvement within the team.

Lead a team of data engineers and analysts, define the data engineering roadmap, oversee data infrastructure, ensure analytics delivery, and establish data governance standards.

Manage client-facing data and analytics projects: define data strategy, build automation and ETL, develop visualizations and predictive analytics, drive transformation, manage scope/budget, and communicate findings to non-technical stakeholders while leveraging finance knowledge.

Lead a global, hands-on data engineering team building large-scale Databricks/PySpark pipelines, production datasets, customer-facing data feeds, AI-ready analytical datasets, and data quality/observability systems. Partner cross-functionally, use AI coding tools to accelerate delivery, and ensure operational excellence, reliability, and documentation for investor-facing products.

Administer and maintain CRM and data systems (EveryAction, Salesforce, BigQuery, CARTO), respond to staff and affiliate data/tech requests, ensure data quality and de-duplication, create and deliver training materials, build network capacity, manage vendor/consultant relationships, and travel occasionally for in-person trainings and support.

Manage a team of analysts for PBM analytics, driving insights and scalable data products. Partner with business leaders for effective reporting and analysis.

The Senior Manager, Data Steward (AI) will lead data stewardship initiatives in Pharma R&D, ensuring data quality and governance while mentoring a team and collaborating across departments to enhance data usability for insights and compliance.

The Clinical Data Manager will manage research databases, oversee EHR to EDC automation, ensure data quality, and perform statistical analyses.

Lead a team of ~7 data scientists to deliver accurate product analytics and experimentation insights. Manage people, mentor staff, partner with product and engineering to build accessible data products, and improve measurement, reporting, and experiment practices to inform product decisions.

Lead a team of ~7 data scientists to deliver timely, accurate product analytics and experimentation insights. Manage people, partner with product and engineering, improve measurement and data products, and ensure rigorous large-scale analysis to inform product decisions.

Manage a team of ~7 data scientists to deliver experimental design, product analytics, and actionable insights. Partner with product, engineering, and research to build measurement, data products, and reporting; ensure data accuracy, timely delivery, and best practices.

Lead a Product Analytics data science team to deliver timely, accurate insights for product decisions. Manage ~7 data scientists, mentor teammates, partner with product and engineering, develop experimentation and measurement best practices, and build accessible data products and reports.

Lead a team of ~7 data scientists to deliver timely, accurate product analytics and experiments; support product teams, build accessible data products, mentor staff, and improve measurement and reporting practices.

Lead a team of ~7 data scientists to deliver timely, accurate product analytics: guide experimentation, partner with product and engineering, build accessible data products, mentor staff, and improve measurement and reporting practices to inform product decisions.

Lead a team of product analytics data scientists to deliver timely, accurate quantitative insights for product strategy, experimentation, and measurement. Manage people, mentor staff, partner with product and engineering to build accessible data products, and improve experimentation and reporting practices to inform decisions across the Foundation and movement.

The Data Product Manager (DPM) leads the lifecycle and development of data-centric products, aligning them with business goals and collaborating with cross-functional teams to deliver value and insights from data products.

Manage CVS Health's data partnerships and monetization strategy, source new partnerships, ensure operational success, and drive data product adoption across analytics teams.

As Senior Manager of IT Commercial Data Management, you will lead data initiatives, manage product ownership, and develop data governance strategies in collaboration with stakeholders, ensuring high data quality and analytics capabilities.

The Senior Manager, Performance Data Operations leads the Population Health Impact team, driving performance improvement initiatives, building complex datasets, and communicating insights to stakeholders. Responsibilities include data analytics, program management, and fostering cross-functional relationships.

Lead a team of ~7 data scientists to deliver timely, accurate product analytics and experimentation insights; manage people, mentor staff, partner with product and engineering, and build accessible data products and measurement practices.