Top Senior Analytics Jobs in Tulsa, OK

Own the analytics data layer by translating stakeholder needs into certified dimensional models, governed metrics, semantic layers, dashboards, and reports. Build with dbt, SQL, Databricks, Power BI, and Airflow while ensuring data quality, observability, reconciliation, documentation, and dependable orchestration. Partner closely with finance, operations, and client-facing teams, automate recurring workflows, and use AI-assisted engineering practices in a fully remote environment.

Leads and develops a data engineering team supporting People Analytics and AI-assisted workforce analytics. Designs scalable data models, pipelines, semantic layers, testing frameworks, and governance practices. Remains hands-on with production code, reviews, and technical problem-solving while partnering with People Analytics, business, legal, and compliance stakeholders. Establishes engineering standards across Python, SQL, dbt, Airflow, Snowflake, CI/CD, data quality, observability, privacy, and security.

Manage human capital analytics and workforce metrics, including hiring-process analysis, time-in-stage, time-to-hire, yield rates, benchmarking, trend analysis, and performance measurement. Develop dashboards, visualizations, executive briefings, analytical presentations, and reports using business intelligence tools. Communicate complex findings and actionable recommendations to senior leadership while supporting federal or large organizational clients.

Lead identification and quantification of issuance leakage across acquisition, application, approval, and funding funnels. Analyze digital customer journeys and conversion gaps, run root-cause diagnostics, design experiments, build executive dashboards, and translate findings into prioritized, quantified business recommendations partnering with Product, Marketing, Credit Risk, Operations, and Engineering.

Lead complex customer analytics engagements, translating industrial business needs into scalable analytics architectures and solutions. Advise customers, drive product adoption, identify high-value use cases, mentor Analytics Engineers, resolve technical escalations, and influence product roadmaps. Apply expertise in industrial process data and sectors such as power, pharmaceuticals, and semiconductors. Collaborate with customers, partners, product teams, and account teams to deliver measurable business outcomes.

The Analytics Lead will guide data-driven decision-making, develop measurement frameworks, forecast revenue, and mentor an analytics team, ensuring effective communication of insights.

Lead independent validation and governance of credit and fraud models, build automated Python monitoring for model drift and performance, drive remediation with model developers, and liaise with audit and regulators to support Affirm Bank's model risk management.

Serve as a strategic analytics advisor to clients, designing and auditing Adobe Analytics and Customer Journey Analytics implementations. Lead measurement workshops, requirements gathering, documentation, report suite and CJA configuration, identity resolution, dashboard development, and data storytelling. Translate ambiguous business needs into actionable analytics strategies, present findings to executives, deliver training, and guide technical and non-technical stakeholders. Promote information security and data privacy practices, including GDPR and CCPA compliance.

Design and own an event-driven data architecture that moves healthcare claims data through AWS and Snowflake into fraud, waste, and abuse alerts. Establish architecture standards, approve material changes, build complex pipeline components, and ensure scalability, reliability, and compliance with PII/PHI constraints. Collaborate with data science, compliance, technical, and investigative teams while supporting government review timelines. This is a hands-on principal role requiring strong Python, SQL, AWS, Snowflake, and production streaming pipeline experience.

Lead and build a product analytics function: define KPI framework, instrumentation, and self-serve analytics using Pendo and BI tools. Partner cross-functionally to measure product performance, surface insights, influence roadmap and embed AI-powered analytics into product lifecycle.

Architects and scales enterprise data models, dimensional models, dbt transformation pipelines, semantic layers, and BI datasets. The role focuses on data quality, testing, observability, governance, metric standardization, documentation, performance optimization, and secure access controls. It partners with executives and cross-functional teams, leads analytics engineering initiatives, troubleshoots complex data issues, owns domain models, and mentors engineers while advancing the company’s analytics platform strategy.

Build and maintain governed Silver/Gold data models and semantic assets on Databricks. Translate stakeholder KPIs into testable, reusable transformations, apply enterprise modeling standards, enable domain self-service, ensure data sensitivity and governance, and review domain-contributed models for production readiness.

Build Sailor Health’s foundational data platform from scratch. Responsibilities include developing Snowflake and dbt pipelines integrating EHR, CRM, and communications data; creating data-powered web applications; automating clinical and billing workflows with Python; analyzing therapy transcripts and patient data using AI and machine learning; tracking clinical outcomes; and designing scalable data architectures in collaboration with clinicians, engineers, founders, and other teams.

Analyze sales, marketing, and customer success performance, including pipeline, funnel conversion, campaigns, retention, and revenue drivers. Build and maintain dbt models and marts using Salesforce and modern data-stack tools, while owning data quality and documentation. Contribute to Lighthouse, a BI-as-code platform, and develop AI-assisted analytics workflows. Partner with stakeholders to frame ambiguous questions, deliver recommendations, and communicate progress in two-week sprints.

Develops healthcare reporting and analytics solutions using Power BI and SQL to improve operational and clinical performance. Responsibilities include gathering requirements, validating data quality, creating dashboards and reports, partnering with data engineering and advanced analytics teams, training users, maintaining documentation, managing reporting timelines, and resolving data or logic issues.

Leads complex private banking data management and analytics initiatives, defining data strategies, quality standards, product attributes, and integrated client views. Partners with business, engineering, analytics, AI, and Wealth teams to translate needs into dashboards, KPIs, predictive models, personalized insights, and intelligent decision-making capabilities. Serves as a subject matter expert on private banking products while mentoring junior team members and enabling platform readiness.

Lead a team of analysts and data scientists embedded in sales to build rigorous pilot analyses, create executive-ready business cases, expand consultative analytics across the partner lifecycle, and publish industry insights while coaching and scaling the Solutions Analytics function.

Leads advanced analytics initiatives supporting sales, marketing, and operations strategy. Develops statistical and predictive models, dashboards, KPI reporting, and customer analyses using large datasets. Partners with stakeholders and data engineering teams to translate business questions into actionable insights, communicates findings to executives, and mentors junior analysts.

Architect and maintain NetBrain’s first cloud data warehouse, ELT pipelines, transformation layer, semantic models, and BI foundation. Own data quality, governance, lineage, access controls, and curated models supporting product usage, customer adoption, account health, and go-to-market reporting. Partner with stakeholders to establish reliable metrics and a standardized source of truth, while supporting AI-assisted analytics and natural-language reporting use cases.

Leads advanced analytics for Airbnb’s Community Blueprint Quality team. Responsibilities include diagnosing customer experience issues, designing experiments, applying causal inference, developing metrics, using LLMs to analyze unstructured data, building dashboards and analytical artifacts, and translating complex findings into recommendations for senior leadership. The role partners cross-functionally with Product, Engineering, Data Science, Data Engineering, Operations, and Finance.

Build and maintain data models, pipelines, metrics, dashboards, and analytics supporting identity graph construction, entity matching, and identity resolution. Partner with engineering and data science teams to productionize matching logic, evaluate match quality, monitor data quality and drift, and manage identity-provider relationships. Translate complex identity concepts into actionable insights for technical and non-technical stakeholders while helping define privacy-conscious product and data priorities.

Own the company’s data function by designing and optimizing Snowflake architecture, pipelines, integrations, data models, BI dashboards, metric definitions, governance, and data-quality standards. Partner with leadership and cross-functional teams to translate operational needs into trusted data products. Automate recurring reporting, troubleshoot ingestion issues, perform ad hoc analysis, and establish scalable analytics practices as the first dedicated analytics hire.

Senior analytics leader who defines analytic strategy, leads a team of SQL analysts and BI developers, architects scalable frameworks for medical economics, risk, population health and value-based care, ensures methodological rigor in DBT/Spark and Quicksight dashboards, drives automation and AI integration, presents to executive stakeholders, and mentors senior analytic talent.

Leads the design and implementation of AI-powered data pipelines, enterprise feature stores, analytics-ready repositories, and self-service data access layers. Builds automated feature engineering, data quality, observability, and governance capabilities for large-scale data science workloads. Partners with Enterprise IT, establishes data availability and quality SLAs, integrates structured and unstructured data sources, and leads teams delivering scalable analytics infrastructure.

Leads analytics for SEO, AI search, website experience, CRO, and the organic enrollment funnel. Diagnoses performance across traffic, visibility, conversions, leads, applications, and enrollments; designs measurement approaches; reconciles web, search, CRM, and platform data; defines KPI and reporting requirements; and translates findings into executive recommendations. Partners with analytics and engineering teams to build scalable Databricks-based reporting and improve measurement of organic and AEO initiatives.