Top Analysis Reporting Jobs in Tulsa, OK

Analyze, clean, and validate data from multiple sources; build reports, dashboards, and visualizations; identify trends and opportunities; track KPIs; investigate discrepancies; document analytical processes; and communicate actionable insights to cross-functional business and product stakeholders.

Build and maintain a regulatory reporting data platform using SQL and dbt, including models, automated validations, data pipelines, and testing. Investigate data issues end to end, validate submissions, support audits and legal data requests, monitor upstream changes, collaborate with product, engineering, compliance, and leadership, and participate in on-call coverage. WOOF WOOF

Develop and execute a rare disease data strategy for the U.S. Commercial organization. Translate business priorities into data roadmaps, establish governance and metadata standards, coordinate data sourcing and vendor evaluations, manage delivery risks and dependencies, drive adoption of new ways of working, and report progress to senior stakeholders. Ensure data strategy activities align with privacy policies and regulations while optimizing data assets, products, and lifecycle management.

Analyzes healthcare claims, pharmacy, laboratory, financial, utilization, and risk-adjustment data. Develops suspect, targeting, tracking, QA, and reporting systems for Medicaid, Marketplace, Medicare, and MMP interventions. Performs predictive modeling, statistical analysis, forecasting, anomaly detection, data validation, root-cause analysis, and risk-score tracking. Communicates analytical findings to technical and nontechnical stakeholders while supporting cross-functional projects and regulatory or business requests.

Performs accurate data entry, verification, updating, cleanup, organization, and maintenance across databases, spreadsheets, and digital systems. Maintains electronic records, converts documents into digital formats, prepares basic reports, retrieves information, tracks assignments, and supports scanning and filing. The role requires confidentiality, attention to detail, strong organization, dependable independent work, effective time management, and reliable remote-work capabilities.

Support design, development, and maintenance of data pipelines and infrastructure. Assist with ETL/ELT workflows, data ingestion, cleaning, validation, modeling, and warehousing. Document processes and collaborate with data teams to support analytics use cases.

Performs advanced data extraction, statistical analysis, modeling, forecasting, and visualization using enterprise data sources. Builds and maintains Power BI dashboards and Snowflake data models, supports data governance and regulatory reporting, and translates business needs into actionable insights. Serves as a trusted analytics consultant, leads reporting and analysis projects, presents findings to technical and non-technical stakeholders, and improves reporting through automation and standardized methodologies.

Build and maintain ETL pipelines that collect, transform, and integrate disparate data sources, including biological, genomic, and proteomic datasets. Design, develop, test, and improve maintainable software across the SDLC while collaborating in an Agile environment. Support Azure data services, CI/CD, architecture decisions, code management, and developer guidance for NIH scientific applications.

Provide administration and application support for mainframe DB2 databases across development, test, and production environments. Responsibilities include database creation, backup and recovery, performance and SQL tuning, security, reorganizations, disaster recovery, production troubleshooting, environment changes, client migrations, documentation, and vendor coordination. The role supports multiple DB2 subsystems and clients, follows ITSM change controls, and participates in 24/7/365 on-call coverage.

Leads healthcare data architecture, modeling, normalization, transformation, and integration across FHIR, OMOP, STDM, HL7, and related standards. Oversees data pipelines, terminology mapping, deduplication, quality initiatives, interoperability strategies, and technical documentation. Collaborates across technical and non-technical teams to resolve complex data issues and explore AI and machine learning applications. Requires deep expertise in clinical informatics, healthcare data standards, SQL, Python, R, dbt, ETL, cloud platforms, and version control.