Top Senior Data & Analytics Jobs in Tucson, AZ

Design, build, and maintain scalable data pipelines, lakes, and infrastructure using Databricks, Spark, Python, and AWS. Collaborate with ML engineers, data scientists, and analysts to deliver data products, ensure data quality and governance, implement DevOps/SDLC practices, mentor junior engineers, and evaluate new data technologies.

The role involves analyzing complex pricing strategies, conducting data-driven analyses, collaborating with cross-functional teams, and translating insights into strategic business recommendations.

Lead measurement strategy for paid and performance marketing, design and interpret A/B and lift tests, run incrementality and attribution analyses, and optimize channel budgets. Build business cases, develop dashboards and BI workflows, evaluate AI-enabled analytics tooling, and enable self-serve analytics. Partner cross-functionally and present insights to leadership to drive experimentation, growth, and LTV outcomes.

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.

The Senior Data Engineer will construct and optimize data pipelines, work with cross-functional teams, ensure data quality and governance, and mentor junior engineers while leading advanced analytics initiatives.

Design, build, and operate large-scale data pipelines and ETL processes to normalize varied partner datasets. Lead technical decisions, mentor engineers, implement testing and monitoring, manage data warehouses/lakes, and collaborate cross-functionally to deliver robust data infrastructure that supports ML and product teams.

The Senior Enterprise Performance Analyst will develop dashboards, analyze performance data, support decision-making, mentor junior analysts, and improve organizational performance.

The Senior Product Analyst will automate data collection, analyze product metrics, conduct A/B tests, and support product hypothesis generation.

Own end-to-end analysis of payments, chargebacks, and user lifecycle to identify drivers of net loss. Deep-dive into fraud cases, design and evaluate A/B experiments and risk-rule changes, build monitoring dashboards, partner with Risk, Payments, and Product to convert insights into mitigations, and run ad-hoc analyses of emerging loss trends.

Lead product analytics: build and maintain dbt models and Looker dashboards, define KPIs, analyze funnel and user journeys, design and evaluate A/B tests, size roadmap impact, and deliver partner and ad-hoc insights to inform product and executive decisions.