Top Data & Analytics Jobs in Oklahoma City, OK

Develop and optimize SQL Server and SSIS billing pipelines, automate data movement and validation with Python, and use R and Python for analytics, anomaly detection, and billing decision models. Build data-quality frameworks, support internal reporting tools, integrate ERP APIs, and collaborate with finance and operations stakeholders. The role requires maintainable, tested, auditable solutions and ownership of features across databases, pipelines, analytics, and internal applications.

Designs and builds enterprise-scale Azure lakehouse platforms for claims data using Databricks, Spark, Delta Lake, PySpark, and SQL. Responsibilities include developing Medallion Architecture, ETL/ELT pipelines, data models, governance, access controls, lineage, performance optimization, CI/CD, and production support. The role translates business and data requirements into technical designs while partnering with engineers, analysts, and stakeholders.

Own marketing channel performance tracking, reporting, experimentation, dashboards, forecasting, and LTV analysis. Develop measurement frameworks, identify data issues, and translate complex findings into actionable recommendations for Marketing, Growth, Product, and Finance stakeholders. The role requires strong SQL, BI dashboarding, marketing measurement, experimentation knowledge, and effective analytical communication, with AI tools used to accelerate reporting and analysis.

Build and maintain a production data platform integrating RMS, Azure SQL, HubSpot, and vendor APIs. Develop pipelines, data models, quality gates, APIs, system-of-record write-backs, performance improvements, and explainable lineage. Build production AI-agent workflows with evaluation, guardrails, and monitoring. Own features end to end, contribute to applications, support incident response, and collaborate with product managers and traders.

Lead production of federal species range maps for marine and anadromous taxa. Aggregate, normalize, transform, and validate spatial data; analyze habitat intersects; coordinate iterative reviews with FWS and NOAA SMEs; prepare QA documentation; and secure government sign-off for map deliverables.

Design, build, deploy, and maintain AWS-based data and AI/ML infrastructure for enterprise agentic AI platforms. Develop data lake pipelines, observability systems, agent-enablement tooling, and production MLOps/LLMOps capabilities. Own features end-to-end, contribute to architecture and design decisions, optimize reliability, scalability, security, and cost, and collaborate with AWS Professional Services, client teams, and junior engineers.

Designs and maintains healthcare analytics infrastructure, production data models, semantic layers, and transformation pipelines using advanced SQL and analytics engineering practices. Integrates payer claims, EMR, clinical, attribution, quality, and operational data. Performs ad hoc analysis, defines standardized healthcare metrics, validates data quality, optimizes queries, documents data flows, and partners with clinical, operational, IT, and payer stakeholders to support value-based care and enterprise reporting.

Lead product experimentation and analytics across three product squads. Design experiments, define product metrics, analyze patient and clinician behavior, build dashboards, apply causal inference, and communicate actionable insights. Partner with product, engineering, design, clinical, operations, and data teams to improve instrumentation, data quality, and evidence-based healthcare product decisions.

Design, develop, and maintain Customer Data Platform solutions and data pipelines to create unified customer profiles, manage identity resolution, enable audience segmentation and activation, ensure data quality and governance, and support integrations with marketing, CRM, and cloud data systems.

Analyze qualitative and quantitative customer feedback, behavioral data, and operational metrics to identify user pain points and root causes. Develop dashboards, KPIs, reporting, and measurement frameworks; quantify experience impacts; and provide evidence-based recommendations. Partner cross-functionally with Product, Engineering, Marketing, Data, and Support leaders to influence prioritization, evaluate initiatives, and improve customer satisfaction, usability, loyalty, adoption, and support demand.