Top Analytics Jobs in Tulsa, OK

The VP of Reporting & Analytics leads the analytics function, developing strategies and insights for stakeholders, enhancing data literacy, and managing analytics teams while driving AI-enabled capabilities and BI infrastructure advancements.

As a Revenue Analytics Manager, you will own reporting for Sales, Marketing, and Customer Success, build dashboards, automate workflows, and ensure data quality.

The Manager, Analytics Services will lead a team to deliver high-quality analytics projects, manage client relationships, and integrate AI solutions in healthcare analytics, focusing on business growth and client satisfaction.

The Lead, Advanced Analytics, Acquisition will analyze data, define metrics, conduct A/B tests, and collaborate with teams to drive growth and insights.

The Senior Analyst, Revenue Operations is responsible for delivering analytical insights, enhancing forecasting accuracy, and optimizing revenue processes while collaborating with various departments.

The Sales Analytics Manager will oversee sales analytics, implement AI-driven reporting solutions, and partner with various teams to enhance data governance and performance forecasting.

The Digital Analytics Analyst will develop data strategies, implement analytics tools, and translate complex data into actionable insights for business impact.

You will analyze data, support business teams with insights, enhance dashboards, and manage stakeholder relationships to improve operational efficiency.

Lead lifecycle marketing analytics for co-brand and enterprise portfolios: identify growth opportunities, build dashboards and models, advise partners, automate scalable analytics, and promote data-driven decisioning to improve campaign ROI and customer experience.

Lead end-to-end strategic projects to optimize health plan programs, perform data-driven analyses, build impact models, present executive recommendations, advise cross-functional stakeholders, mentor junior analysts, and monitor initiative outcomes to improve efficiency and care quality.

Own and improve the dbt transformation layer, semantic layer, data observability, and pipeline orchestration while migrating the platform to AWS. Ensure data quality and governance to support clinical AI, collaborate with clinical, operations, and finance stakeholders, and audit, document, and fix existing data pipelines.

Lead individual-contributor who builds self-service analytics, metrics, experiments and scenario simulators to detect and mitigate fraud and safety risks. Partners with product, engineering, legal and ops to operationalize metrics, run causal analyses, and support audits, incident response, and executive reporting.

Owner of data platform modernization: design and migrate legacy systems to Kimball-style dimensional models and star schemas; build reliable production data pipelines; enforce data hygiene and validation; champion dbt best practices; mentor junior engineers; collaborate with analysts and stakeholders to ensure scalable, well-documented data models and platform observability.

Lead and scale the data analytics organization, guiding teams, driving product analytics strategy, and integrating AI to enhance data-driven decision-making across the company.

Lead analytics engineering: design scalable dimensional models, build modular tested transformations, replace brittle pipelines with reusable patterns, drive semantic layers and self-service analytics, partner on data architecture, and provide cross-functional technical leadership and governance.

The Data & Analytics Leader will develop a multi-year roadmap to enhance data governance and analytics capabilities, while fostering a data-driven culture throughout ERAC.

Lead web analytics strategy and implementation: define measurement, maintain data pipelines, support 1P data integrations, troubleshoot tagging/pixel/API issues, create documentation and playbooks, translate analytics for stakeholders, and drive continuous improvement and AI-assisted automation.

Lead enterprise analytics strategy and roadmap, consolidate analytics teams, deliver data products and governance, scale advanced analytics and MLOps, partner with IT and business leaders to drive data-driven decision-making and measurable financial impact.

Design, build, and support SAP Analytics Cloud dashboards, planning models, and executive reporting. Integrate SAC with SAP and non‑SAP sources, develop KPIs and forecasting models, run testing and UAT, troubleshoot issues, and support production deployments and stakeholder workshops to deliver actionable analytics for FP&A and operational teams.

Client-facing Analytics Manager who architects large-scale digital analytics (GA4/Adobe), audits implementations, builds measurement frameworks, and leads GEO (Generative Engine Optimization) test & learn programs. Uses AI tools (Claude) to automate workflows, mentor teammates, manage projects, and translate data into strategic client recommendations.

Drive marketing performance measurement and attribution by building dashboards, standardizing campaign tracking across HubSpot and Salesforce, running funnel and cohort analyses, ensuring data integrity, and delivering executive reporting to connect marketing activity to pipeline and revenue.

The Director of Growth Strategy & Analytics will lead analytics efforts, develop strategies for consumer engagement, and improve app experiences through data insights and collaboration with teams, utilizing AI tools for automation.

Lead development and scaling of financial analytics: build data models, dashboards, and reporting; translate financial data into executive insights; embed predictive analytics; partner with Finance, IT, and business units to improve data quality, governance, forecasting, and automation.

Build and maintain scalable Python-based data pipelines and backend services for analytics and ML workflows. Design software systems, support analytics/model training and deployment platforms, implement CI/CD and IaC, manage AWS/on-prem environments, monitor and improve pipeline reliability, and evaluate new AI technologies to increase platform efficiency.

Analyze business performance, provide insights, improve processes, participate in continuous improvement projects, and collaborate with cross-functional teams.