Top Data & Analytics Jobs in Oklahoma City, OK

Leads and scales a global data engineering and analytics team while remaining hands-on with architecture, pipelines, data quality, dimensional modeling, dashboards, reporting, and customer-facing deliverables. Owns the analytical data platform supporting clinical, operational, product, financial, and customer data. Partners with cross-functional stakeholders, investigates complex data issues, improves platform reliability and security, and mentors engineers.

Conduct quantitative research for traders by testing market hypotheses, building and maintaining documented signals, performing event studies and backtests, and validating results against leakage, survivorship bias, transaction costs, and regime dependence. Organize reproducible point-in-time datasets, communicate findings clearly, track signal performance, and use AI tools to accelerate research and coding while verifying outputs.

Lead cross-functional analytics initiatives across product, marketing, audio development, and business operations. Build data models, dashboards, self-serve reporting tools, and actionable company-wide metrics. Analyze activation funnels, A/B tests, growth KPIs, and product health while optimizing the analytics infrastructure. Partner with executives, engineering, product, marketing, and data engineering teams to improve governance, data adoption, and decision-making.

The Analytics Architect will design and implement cloud-based analytics solutions, maintain data pipelines, ensure data quality, and collaborate with teams to enhance operational efficiency.

Own enterprise analytics audits, implementations, data quality, attribution, segmentation, SQL analysis, BI dashboards, experimentation measurement, and client reporting. Serve as the senior analytics partner for major accounts, present insights to executives, train client and internal teams, and help define analytics and experimentation services. The role requires hands-on Adobe expertise, autonomous prioritization, technical collaboration, and the ability to connect organic and AI search performance to pipeline and revenue.

Design, build, deploy, and operationalize scalable data engineering solutions for customers. Responsibilities include developing pipelines, transforming and cleansing data, analyzing sources, implementing governance, security, and quality practices, testing data movement and transformation code, resolving defects and incidents, and advising customers on technical decisions. The role also supports AI, machine learning, business intelligence, and data product adoption while collaborating across teams and educating end users.

Provides data analytics and business intelligence support for government customers. Responsibilities include gathering requirements, analyzing and transforming enterprise data, designing data models, developing dashboards and visualizations, documenting governance processes, improving workflows, and training users. The role requires government acquisition, finance, or financial management experience, preferably within DoD or Air Force systems, and supports financial office operations.

Own the companys end-to-end data graph, ensure data quality and lineage, and deliver Sigma dashboards and client-facing reports. Act as single owner for triage, prioritization, and translation of business questions into actionable insights, partnering closely with data engineering and business leaders to set standards for production-ready reporting.

Manage daily Data team operations, research and analyze datasets, build internal and external reports (including an annual Startup Ecosystem Ranking), and maintain/improve Python scripts and data infrastructure to deliver actionable insights.

Design and build Snowflake transformation layer and migrate legacy SQL to versioned dbt models. Define conformed dimensions/facts using Kimball principles, set modeling and CI/CD standards, implement data quality/lineage, support BI migration and AI/ML readiness, and mentor future hires. Hands-on work in dbt, SQL, and Python with stakeholder collaboration and governance for financial and operational data.