Top Data & Analytics Jobs in Oklahoma City, OK

Build and maintain scalable batch and streaming data pipelines on GCP, develop API-driven integrations, design data models, ensure data quality and performance, and deliver end-to-end solutions in a client-facing healthcare data environment.

Partner with Product, Design, Engineering, and Data to define KPIs and measurement for AI-powered features, guide instrumentation, design and evaluate experiments, build dbt models, create dashboards, and influence product analytics standards and long-term strategy.

Run funnel audits and synthesize multiple datasets into exec-ready narratives. Recommend and help launch A/B tests, benchmark against genres and market data, partner with game analysts and ETL engineers to validate and maintain clean data, and provide actionable UA, monetization, and live-ops recommendations to customers.

Design, build, and maintain Scala-based ETL and streaming data pipelines, APIs, and data microservices in Azure. Optimize LLM fine-tuning and RAG workflows, support DevOps/CI-CD practices, document standards, mentor engineers, and manage data ingestion frameworks for analytics and partner integrations.

Design, build, and maintain scalable ETL/ELT pipelines and Lakehouse solutions; ingest and transform diverse data formats; optimize Databricks and SQL Server environments; implement data quality, lineage, and governance; support batch and streaming ingestion; collaborate with analysts and stakeholders to enable analytics (fraud/anomaly detection, financial oversight); troubleshoot and improve pipelines. Candidates must be willing to undergo a U.S. government background investigation.

Design and implement scalable cloud data platforms and end-to-end ETL/ELT pipelines, migrate legacy infrastructure to Snowflake/AWS, optimize data warehouses, build transformations with dbt and Dagster orchestration, develop data processing with Python and SQL, implement IaC and CI/CD, and ensure data observability and governance while collaborating with stakeholders.

Analyze state-level business performance, market trends, and competitive intelligence to inform pricing, product, underwriting, and risk management strategies. Partner with cross-functional teams, lead small projects, monitor performance against plans, and communicate actionable insights to drive profitable growth and continuous improvement.

Design, build, and operate end-to-end production data pipelines on GCP/BigQuery at scale. Optimize partitioning, clustering, query performance, and cost/slot management. Integrate and maintain ETL/ELT processes, legacy SQL Server reporting (stored procedures, SSRS), data validation/monitoring, and data modeling. Use intermediate Python for automation and work with BI tools (Power BI, Looker, Tableau). Champion AI tool adoption and mentor teammates while ensuring delivery and client alignment.

Own and build scalable analytics models, metric definitions, and dashboards (customer-facing and internal). Partner with engineering to design data flows and instrumentation (BigQuery, S3, PostHog). Translate stakeholder needs into analytics plans, triage requests, and surface insights via Looker/Observable. Set analytics stack direction and ensure data quality.

Lead a data engineering team building and operating NPR's data warehouse, pipelines, BI, and personalization systems. Responsibilities include people leadership, hiring, cross-functional collaboration, Agile delivery, technical design and code review, automation for developer experience, incident response, and vendor management.