Top Analysis Reporting Jobs in Tucson, AZ

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.

Leads secure, scalable customer data architecture across Adobe Experience Platform and cloud environments. Designs data strategies, models, governance frameworks, ingestion and integration solutions, migrations, and real-time customer profiles. Partners with data engineers, Adobe, and clients to address data quality, security, privacy, performance, and regulatory requirements. Uses SQL, scripting, APIs, and Postman to support implementation and automation while providing technical leadership across multiple customer projects.

Architect and build cloud-native data platforms, Snowflake pipelines, and datalakes for complex financial datasets. Lead legacy modernization, ETL/ELT, reconciliation, data quality, governance, and regulatory reporting initiatives. Drive technical product roadmaps, backlog prioritization, requirements, testing, cutovers, and releases. Coordinate Agile delivery through Jira and Confluence while aligning business, analytics, and engineering stakeholders across the full data and software lifecycle.

Maintains and monitors Azure Databricks data pipelines, troubleshoots data issues, collaborates with stakeholders on data requirements, documents processes, performs backups and disaster recovery drills, and supports data access and usage. The role requires extensive data engineering experience, Azure expertise, incident management knowledge, and strong problem-solving skills.

Leads a data integration team responsible for onboarding, loading, monitoring, and securing client and vendor data feeds. Oversees Boomi, SQL, and Python integrations; manages feed quality, scheduling, exports, documentation, SLAs, and issue resolution. Partners with clients, vendors, client success, and internal data teams while mentoring analysts and improving automation, transparency, scalability, and anomaly detection across SQL Server and Snowflake environments.

Own end-to-end customer data migrations from legacy databases and files into GovPilot. Use AI coding agents to profile data, create mappings, generate transformation and loading scripts, automate validation, and build reusable migration pipelines. Review and correct AI-generated SQL and scripts, identify data quality issues, maintain auditable migration logic, manage multiple client migrations, and support evening or weekend production go-lives.

Designs, builds, and optimizes scalable Databricks-based data platforms, pipelines, integrations, and reusable engineering tools. The role focuses on platform performance, reliability, cost efficiency, security, governance, automation, and cloud infrastructure. It collaborates with engineering, analytics, security, and other stakeholders on architecture, technical planning, production support, and continuous improvement. The position requires hands-on data engineering experience with Databricks, cloud services, Python, SQL, Spark, infrastructure-as-code, and CI/CD practices.

Build and operate a governed Snowflake enterprise data warehouse, including ingestion, orchestration, dbt transformations, infrastructure as code, security controls, reliability checks, and CI/CD. Partner with data owners and BI teams during migration from legacy systems, ensure compliance for sensitive financial data, support reconciliation and incident resolution, and mentor engineers. The role requires hands-on ownership of pipelines and platform components across development and production environments.