Top Senior Data & Analytics Jobs in Oklahoma City, OK

Lead design and implementation of data architecture for healthcare systems, influence stakeholders, mentor teams, ensure regulatory compliance, enable data integration and transformation, and apply AI techniques to improve healthcare operations and reporting.

Lead workforce management operations and analytics for multi-brand contact center portfolio. Manage WFM and real-time teams, forecasting, capacity planning, scheduling, intraday operations, and WFM technology optimization (Genesys, Intradiem). Drive process standardization, continuous improvement, reporting, and stakeholder partnerships to meet service levels and business objectives.

Lead contact center analytics by analyzing large datasets, building predictive models, ensuring data quality, and creating dashboards. Collaborate with cross-functional teams, identify AI/automation opportunities, present insights to executives, and maintain data privacy and repeatable reporting processes to improve customer experience and operational efficiency.

Lead Retail GTM analytics and territory operations: build reporting, automate workflows, design territory models, manage data quality/enrichment, and deliver decision-support tools across Salesforce, Snowflake, and Sigma to scale sales coverage and drive growth.

The Senior Analytics Engineer will design and implement scalable data solutions for Finance Analytics, reconcile data accuracy, and collaborate across teams to enhance financial datasets and reporting workflows.

Lead the design and operation of automated compliance monitoring for consumer lending: translate regulations into rule-based data checks, own exam and audit data requests, triage exceptions, drive automation, steward compliance data, and provide technical mentorship to a supporting analyst. Produce audit-ready, reproducible evidence for regulators, bank partners, auditors, and senior leadership.

Lead KPI reporting, funnel analysis, forecasting, and business diagnostics for Enterprise Growth across Coursera + Udemy. Build unified definitions and durable self-serve reports, support experiments and inside-sales/outsourced partner evaluation, and provide analytical inputs for target setting and prioritization to drive growth and conversion improvements.

Design, build, and maintain scalable dimensional data models, semantic layer, and production transformation pipelines. Collaborate with Data Engineering, Data Science, BI, DevOps, and Product to enable AI-ready data, ensure data quality and governance, support ML feature delivery, and coach analytics team members for robust, production-ready data products.

Lead and grow a data engineering team that builds and operates ingestion, transformation, orchestration, and serving layers and a self-serve analytics substrate. Own data quality, observability, cost efficiency, SLAs, and engineering practices while partnering with Data Science, BIE, Analytics, and Product to deliver reliable, reusable data products.

Lead design and scaling of the data platform and analytics pipelines. Build and maintain ETL/ELT workflows, integrations, serverless data services, and reliable data models. Mentor teammates, improve automation, observability, and data governance to support analytics and operational insights.