Top Remote Data & Analytics Jobs in Oklahoma City, OK

Own Silver and Gold medallion-layer modeling and semantic layer governance using dbt and SQL. Improve warehouse performance and cost, standardize BI tooling, collaborate with stakeholders to produce trusted metrics, troubleshoot data quality, and evolve analytics platform.

Manage analytical service operations by overseeing a team, designing software for data mining, developing statistical models, and generating insights for marketing outcomes. Provide training and mentorship to direct reports.

Drives KPI reporting, dashboard and scorecard development, forecasting, trend analysis, data collection, and executive presentations. Partners with cross-functional teams and leadership to identify analytical needs, improve data quality and reporting processes, investigate metric fluctuations, and support operational decision-making. Builds standardized tools and templates, automates reporting, maintains data integrity, communicates findings, and develops work plans to meet deadlines.

Design and build integrations connecting legacy practice management systems to an AI platform. Responsibilities include AI-assisted schema mapping, data modeling, ETL pipelines, REST/SOAP APIs, data cleansing, quality monitoring, production troubleshooting, documentation, and collaboration across architecture, product, engineering, and operations teams. The role requires applying LLM tools to schema inference, field reconciliation, anomaly detection, and data-quality automation while handling healthcare data securely.

Designs and builds data models, schemas, and ETL/ELT pipelines for analytics, reporting, and machine learning workloads. Optimizes query performance and cloud data infrastructure, establishes data quality and governance practices, integrates datasets with BI and machine learning tools, documents workflows, and mentors junior engineers. Collaborates with data scientists, analysts, and product owners to translate business requirements into scalable technical solutions.

Leads PBM data integration delivery across claims, eligibility, accumulator, and encounter systems. Profiles and validates data, writes advanced SQL, resolves reconciliation discrepancies, investigates production issues, and partners with engineering on scalable healthcare integration architecture. Owns timelines, risks, vendor dependencies, testing, UAT, migration, rollback planning, executive reporting, and post-deployment support. Requires deep PBM claims-processing expertise, data architecture experience, and hands-on knowledge of ETL, secure file transfer, job scheduling, and healthcare data standards.

The Data Governance Analyst administers data governance platforms and processes, including enterprise data catalogs, Databricks Unity Catalog, metadata, lineage, data stewardship, MDM, privacy compliance, and data quality initiatives. The role leads catalog roadmaps, supports Osano, coordinates with data owners and engineering teams, monitors governance adoption and metrics, and develops scalable governance standards and procedures.

Leads actuarial financial data systems and marts, developing data solutions, extracts, reports, analyses, automations, validations, and executive reporting support. Manages data mart maintenance, audits, business requirements, technical specifications, user acceptance testing, and system implementations. Provides technical leadership, coordinates vendors and departments, trains junior staff, and advises project teams on data architecture, business rules, configuration, and financial data quality.

Analyzes customer loyalty data to generate actionable insights, recommendations, visualizations, and narratives for clients. Builds audience lists, applies segmentation and statistical modeling techniques, develops test plans, and collaborates with CRM, Data Engineering, Business Intelligence, Strategy, and Client Services teams. The role is client-facing and manages request prioritization while supporting loyalty marketing initiatives and measurable business outcomes.

Build and operate a large-scale enterprise data platform spanning batch and streaming processing, lakehouse storage, Kafka infrastructure, APIs, observability, and agentic MCP interfaces. Develop reusable ingestion and data-quality frameworks, improve scalability and resilience, establish engineering practices, support production operations, and mentor engineers. The role requires deep ownership of distributed data infrastructure and practical use of AI coding agents.