Top Data & Analytics Jobs in Oklahoma City, OK

Build scalable GTM and product analytics to inform revenue, retention, and growth decisions. Partner with Sales, Customer Success, Finance, Product, and Data Engineering to deliver dashboards, models, and executive data stories; validate data quality; measure ROI; and enable self-service reporting across the lead-to-quote-to-cash journey.

Maintain and configure client coding and reporting tables, audit accruals, create and transmit standard and government reports, update coding to ensure payroll/PSL data consistency, and define client requirements for custom reporting while collaborating with clients and internal teams.

Design, build, and maintain ETL/ELT pipelines and analytics data models from structured and event-level data. Transform clickstream/telemetry into user-level journeys, implement dbt transformations in a cloud warehouse (Redshift/Snowflake/BigQuery), develop reverse ETL workflows, enforce CI/CD/version control and automated data quality tests, and partner with BI, marketing, and product to deliver dashboards and attribution reporting.

Lead healthcare payment integrity analyses, collaborate with clients and internal teams, build and maintain large-scale SQL and Spark queries, design ETL/ELT transformations, implement AI/LLM-driven analytical workflows, perform data quality monitoring, and mentor analysts while documenting lineage and audit-ready methodologies.

Design and maintain a formal analytics data layer (AWS Glue, Athena/Presto, LookML); build data models, pipelines, and automated quality checks; investigate anomalies; document metrics and lineage; mentor analytics team members and promote best practices.

Lead design and scaling of ELT pipelines and analytics schemas on Snowflake. Build Looker views, CI/CD automation, and orchestration workflows (Prefect/Airflow). Ensure production reliability with monitoring (Datadog, Monte Carlo), partner with cross-functional teams, and provide technical leadership including code reviews, testing, and end-to-end ownership of data integrity and platform trustworthiness.

Design, build, and maintain cloud-native ETL/data pipelines using Databricks and AWS Glue. Ingest and transform structured and semi-structured data with PySpark, Python, and SQL. Integrate sources including MongoDB, implement data quality checks, optimize pipeline performance, and support production deployments, monitoring, and troubleshooting while collaborating with architects and analysts.

Design, build, and operate large-scale batch and streaming ingestion pipelines, medallion/lakehouse layering, identity resolution and golden-record consolidation, data-quality gates and reconciliation, source-to-canonical mappings and crosswalks, schema enforcement, and observability for client onboarding and production data flows.

Deliver advanced SQL analyses, build and maintain dashboards and recurring reports, define KPIs, translate findings into executive-ready recommendations, partner cross-functionally, and support automation and reporting improvements.

Develop and implement data governance policies and frameworks; coordinate with IT and business stakeholders; manage enterprise data catalogs (Workday, Epic, Strata); conduct data quality assessments; maintain data dictionaries and lineage; monitor and resolve governance issues; provide training and ensure compliance with data privacy regulations.

Design, build, and maintain Power BI dashboards and data models using Snowflake and other cloud sources. Collaborate with data engineers/ETL teams, gather stakeholder requirements, implement DAX and Power Query transformations, ensure data governance and performance, and deliver analytics within Agile processes.

Support warehouse operations by analyzing performance data, building dashboards and reports, automating recurring reporting, validating data, and providing actionable recommendations to improve productivity, labor utilization, inventory accuracy, and service performance. Partner with operations leadership for staffing, forecasting, and continuous improvement through statistical analysis and ad hoc investigations.