Data Engineer

• Own Fullbay's data platform end to end, including architecture, hands-on development, and company-wide data standards
• Own certified metric and entity definitions and serve as final arbiter of cross-functional definitional disagreements
• Keep documentation current so business users can find trusted answers without assistance
• Build and extend the gold layer using SQL and transformation code in version control with automated tests
• Own identity resolution across Classic, Next, and Salesforce, including crosswalk logic, survivorship rules, and platform-of-record determination
• Own Snowflake role hierarchy, grants, row access and masking policies, warehouse sizing, cost attribution, and spend
• Design curated access paths for AI agents, including per-agent identities and grants, registration patterns, and retention policies
• Onboard new data sources, own schema drift and deletion detection, and partner with engineering on source schema changes
• Scope and direct the retained architecture consultant's work, review deliverables, and reject nonconforming work
• Provide guidance and office hours for business users and AI builders self-serving against the data platform
• Adhere to confidentiality and compliance regulations
• Perform other duties as assigned

Lead Data Engineer

• Validate HIPAA X12 transaction sets — 837 (I/P/D), 835, 834, 270/271, and 276/277 — to confirm claims, eligibility, and remittance data moves correctly between systems
• Diagnose transaction-level errors in loops, segments, elements, and qualifiers
• Partner with development teams to fix mapping or programming issues
• Investigate and resolve trading partner and vendor transaction problems in production
• Create test plans, test cases, and Companion Guide documentation
• Lead UAT, SIT, and end-to-end testing for vendor onboarding, system enhancements, and HIPAA version upgrades (4010/5010)
• Build and maintain mock 837I/837P claim test data
• Coordinate between trading partners, clients, and internal technical and non-technical teams
• Gather requirements and close the loop on issue resolution
• Log incidents and enhancement requests in JIRA
• Maintain project documentation in Confluence
• Validate ICD-9/ICD-10 and HCPCS coding during claims and EDI testing
• Participate in Agile sprint planning and sprint review

Data Engineer, EDI

• Maintain and troubleshoot ETL/ELT data pipelines for healthcare and TPA claims processing
• Use Python and SQL for data extraction, transformation, validation, and loading across on-premises and cloud environments
• Monitor pipeline performance, review logs, investigate failed jobs, and correct data discrepancies
• Handle support tickets approximately 50% of the time, responding to user requests and resolving operational data problems
• Monitor daily production processes, jobs, application performance, and application maintenance
• Support data management in PostgreSQL, Oracle, AWS RDS, and cloud-based databases
• Learn and support data workflows, healthcare data formats, file transfers, and reporting processes with senior team members
• Maintain data quality and accuracy through routine checks, documentation, and follow-up
• Collaborate with Data Analysts, Developers, and business users on data needs, reporting, and data operations
• Participate in team meetings, sprint activities, and knowledge-sharing

Data Operations Engineer

• Build and maintain ELT/ETL pipelines ingesting financial systems, ERP/CRM systems, files, and APIs into Snowflake
• Develop SQL and Python transformations in Dataiku and Databricks
• Produce trusted, reusable finance data marts and subject-area tables
• Automate and orchestrate workflows, including scheduling, dependency management, alerts, logging, and monitoring
• Publish performant Snowflake views and semantic layers for Tableau analytics
• Optimize Snowflake query patterns, warehouses, micro-partitions, caching, clustering, and cost-efficient configurations
• Implement data validation, reconciliations to source systems, lineage documentation, and access controls
• Uphold PII and financial data standards
• Gather requirements with Finance/FP&A, Accounting, and business analysts
• Translate period close, variance analysis, and forecasting metrics into data models and pipelines
• Manage code in Git, conduct reviews, write unit/integration tests, and promote changes through dev/test/prod
• Convert manual and Excel processes into automated, auditable pipelines
• Prototype utilities and frameworks in Python

Data Engineer II

• Design, build, deploy, and support scalable Databricks-based data pipelines and lakehouse solutions
• Ingest, transform, and curate observability, infrastructure, and security data from multiple enterprise sources
• Develop and optimize PySpark workloads, Structured Streaming jobs, and bronze/silver/gold data architectures
• Ensure high performance, reliability, scalability, data quality, and operational excellence
• Partner with Security, Infrastructure, Observability, and Platform Engineering teams to onboard data sources
• Establish governance standards, improve metadata management, and deliver trusted enterprise data products
• Contribute to engineering standards, code reviews, CI/CD practices, monitoring strategies, and platform optimization
• Identify and address technical debt, process gaps, and operational risks
• Promote best practices for Databricks, Delta Lake, Unity Catalog, data governance, security controls, access management, and cost-efficient operations

Senior Data Engineer, Observability Engineering

• Design, implement, maintain, and troubleshoot data backup, recovery, and cyber resiliency solutions
• Create, modify, maintain, and support infrastructure system components that enable IT services
• Balance availability, security, efficiency, and functional requirements to optimize production services
• Identify, manage, escalate, measure, monitor, and control risks from business activities
• Drive data protection transformation initiatives focused on simplification, standardization, resilience, and operational efficiency
• Develop engineering standards, implementation plans, and technical documentation
• Architect and deploy data protection solutions for virtual hosts, SQL, NoSQL, DB2, Oracle, Azure, AWS, SaaS, and Active Directory workloads
• Develop and implement Infrastructure-as-Code solutions, automated workflows, and process improvements
• Maintain regulatory compliance, business continuity, cyber resilience, data integrity, and immutability
• Design scalable IT system infrastructure and oversee system changes
• Automate service delivery, maintenance tasks, monitoring, and tooling activities
• Resolve complex technology production issues and lead end-to-end troubleshooting across multiple systems
• Analyze end-to-end system failures and recommend process and business solution improvements
• Experiment with new patterns and technologies
• Establish and improve engineering best practices with peers and the business
• Identify innovative solutions for customers and the business
• Mentor and coach engineers on system design, development, and maintenance
• Develop software-defined infrastructure in code within CI/CD pipelines

Senior Infrastructure Engineer – Data Protection

• Develop and test data pipelines that ingest, transform, and curate data within Cambridge’s cloud data platform
• Build and maintain API integrations and ETL/ELT workflows with guidance from data architects and engineers
• Support data quality and data governance processes
• Collaborate with data architects, engineers, analysts, and business stakeholders to deliver scalable data solutions
• Document data solutions
• Contribute to enterprise reporting and analytics initiatives
• Use SQL, Python, Snowflake, Azure, Event Hubs, Kafka, dbt, Git, Azure DevOps, Confluence, CI/CD, and database change management tools

Data Engineer jobs in Arizona