Top Remote Analysis Reporting Jobs in Tucson, AZ

Leads the design, development, governance, and productization of enterprise data models, pipelines, data products, and ontologies. Builds machine-learning-ready datasets, implements security and lineage controls, optimizes Databricks and Delta Lake environments, establishes data-product standards, collaborates with architects and data scientists, and mentors engineering talent. The role also defines product roadmaps, service levels, usage metrics, and continuous-improvement processes for enterprise data assets supporting AI agents, analytics, and applications.

Collect, process, and analyze large datasets using statistical methods to answer business questions. Develop dashboards, narratives, KPIs, and actionable insights; maintain data processes and analytics capabilities; ensure data quality; resolve data issues; and translate business problems into functional and technical requirements. Coordinate key tasks and resources to meet cost and schedule objectives while collaborating across departments.

Develop and maintain ETL pipelines, data integration workflows, backend services, and optimized database operations. Build scalable Java and Spring Boot applications, write complex SQL, implement validation and error handling, perform unit testing, troubleshoot pipeline and application issues, and support production deployments. Collaborate with data engineers, developers, QA teams, and business stakeholders while documenting processes and following software development best practices.

Develop and maintain data pipelines using Python, SQL, ETL, and Apache Spark. Extract, transform, validate, and load data from databases, APIs, and files; monitor pipeline performance and quality; support batch and streaming workflows; document processes; and collaborate with analytics and business intelligence teams.

Build and maintain scalable data transformation pipelines, analytics-ready datasets, data warehouses, data marts, and reusable data models using SQL and dbt. Implement data quality testing, validation, monitoring, documentation, and governance while optimizing warehouse performance and integrating multiple data sources. Partner with analysts, product managers, and business stakeholders to support dashboards, KPI reporting, business intelligence, and strategic analytics initiatives.

Owns lead and hotel account data quality for a sales organization. Responsibilities include CRM audits, deduplication, enrichment, lead qualification, routing, SLA development, escalation resolution, data workflow documentation, vendor evaluation, SQL-based validation, and responsible use of AI for quality assurance and anomaly detection.

Owns a six-month analytics engineering engagement covering scalable Snowflake architecture, marketing and sales ingestion, finance automation with LLM agents, data reconciliation, and governed Tableau reporting. Builds production-ready pipelines, data models, quality monitoring, human-in-the-loop controls, and row- and column-level security. Delivers documentation and knowledge transfer while coordinating independently with finance and publishing stakeholders.

Analyzes and interprets data, evaluates results, and provides actionable insights to support strategic decision-making across departments.

Administers and optimizes enterprise Oracle databases supporting global consular applications. Responsibilities include database operations, backup and recovery, replication, disaster recovery, performance tuning, security remediation, SQL and PL/SQL development, upgrades, migrations, monitoring, governance, documentation, and operational support for Oracle RAC, Data Guard, GoldenGate, and Oracle Linux environments.

Lead production of marine and anadromous species range maps under FWS standards. Aggregate, normalize, transform, and validate spatial data; analyze habitat intersections; coordinate review cycles with FWS and NOAA Fisheries subject matter experts; document quality assurance; and obtain government sign-off for deliverables.

Designs secure, scalable database and data-warehouse architectures; develops logical and physical data models, schemas, metadata, interfaces, and integration standards. Coordinates clustering, backup, recovery, load balancing, and availability strategies. Plans upgrades, recommends infrastructure and configuration improvements, and tests database structures and programs. Collaborates with architects, analysts, and project teams to align database solutions with business and technical requirements.

Design, build, and operate end-to-end data pipelines and models for healthcare claims and payer data. Responsibilities include ingestion, canonical and analytical data modeling, legacy process migration, data quality, testing, CI/CD, deployment, operations, incident response, tool integration, documentation, and onboarding new data sources. The role partners across ML, product, analytics, platform, operations, and customer teams while critically evaluating AI-generated engineering work.

Own end-to-end client data migrations: reverse-engineer legacy ETL, design and build Airflow DAGs and Spark jobs, validate and reconcile data at scale, document architecture and decisions, and coordinate cross-functional teams through go-live and hypercare.

Build and operate scalable batch and streaming data pipelines, dimensional models, ELT/ETL workflows, and trusted data products. Improve data quality, observability, reliability, performance, security, and cost efficiency across the cloud data platform. Partner with analytics, risk, finance, product, data science, and machine learning teams to support business and AI use cases. Lead technical initiatives, influence architecture, mentor engineers, and establish engineering best practices.

Own Mux’s end-to-end data platform, including ingestion, Snowflake administration, dbt transformations, orchestration, data quality, observability, and reverse ETL. Build semantic models supporting AI agents, establish governance and reliability standards, and partner with Engineering, Finance, Product, and GTM. Use AI coding tools to develop and maintain pipelines while validating output quality. The role requires strong SQL, Python, data modeling, cloud warehouse administration, and production data engineering experience.