Top Analysis Reporting Jobs in Tucson, AZ

Build data foundations and end-to-end pipelines, research and implement quantitative pricing, market-making, and risk models for prediction markets, model cross-market dependencies and parlays, develop backtesting/simulation frameworks, monitor model performance and collaborate closely with traders to improve pricing and trading outcomes.

Own Hercules’ full data stack, including ETL from third-party providers into BigQuery, data modeling with dbt and medallion architecture, semantic-layer metric definition, and self-service business intelligence in Omni. Build reliable, maintainable systems and dashboards while applying strong software architecture, API design, code quality, and AI-assisted development practices.

Build and expand the company’s analytics data stack using Looker, LookML, dbt, BigQuery, and Airbyte. Develop data models, integrate multiple business systems, create KPI dashboards, and analyze product usage, customer health, adoption, and revenue data. The role empowers business teams with self-service analytics, actionable insights, and data-informed decision-making while developing a strong understanding of business metrics.

Conduct open-ended research on markets, companies, products, funding, competitors, technologies, people, and regulations. Transform findings into clear, sourced matrices, briefs, landscape maps, and one-pagers with actionable points of view. Use AI tools and lightweight scripts or scrapers to accelerate research, manage multiple shifting requests, and communicate concise conclusions to leadership.

Design, build, deploy, and maintain AWS-based data and AI/ML infrastructure for enterprise agentic AI platforms. Develop data lake pipelines, observability systems, agent-enablement tooling, and production MLOps/LLMOps capabilities. Own features end-to-end, contribute to architecture and design decisions, optimize reliability, scalability, security, and cost, and collaborate with AWS Professional Services, client teams, and junior engineers.

Designs and maintains healthcare analytics infrastructure, production data models, semantic layers, and transformation pipelines using advanced SQL and analytics engineering practices. Integrates payer claims, EMR, clinical, attribution, quality, and operational data. Performs ad hoc analysis, defines standardized healthcare metrics, validates data quality, optimizes queries, documents data flows, and partners with clinical, operational, IT, and payer stakeholders to support value-based care and enterprise reporting.

Lead product experimentation and analytics across three product squads. Design experiments, define product metrics, analyze patient and clinician behavior, build dashboards, apply causal inference, and communicate actionable insights. Partner with product, engineering, design, clinical, operations, and data teams to improve instrumentation, data quality, and evidence-based healthcare product decisions.

Designs scalable data models, optimized SQL queries, analytics infrastructure, semantic layers, and data applications. Partners with data science, product, engineering, and business teams to enable dashboards, self-service analytics, reporting, and enterprise data-driven decision-making. The role also establishes analytics engineering best practices, improves developer experience, and supports secure, compliant data warehousing.

Lead business intelligence strategy across product, clinical, commercial, finance, and engineering teams. Build measurement frameworks, semantic layers, reporting solutions, and visualizations that demonstrate business value and support data-informed decisions. Own the data lifecycle from ideation through client-facing delivery, ensure data governance and integrity, and translate complex datasets into actionable insights. Collaborate on scalable data architecture and apply statistical methods, advanced analytics, and emerging AI-driven BI capabilities.

Build and maintain automated ETL/ELT data pipelines, improve data quality and monitoring, optimize Postgres and BigQuery schemas and queries, develop internal tooling, support data governance (HIPAA), participate in on-call rotation, and collaborate across engineering teams to scale data systems.