Top Data & Analytics Jobs in Tulsa, OK

The Technical Enablement & Documentation Lead is responsible for developing and maintaining product documentation, creating a certification program, and collaborating with teams to improve the knowledge experience for Flosum's users.

Design and own end-to-end data platform architecture, build ingestion and transformation pipelines, model multi-tenant datasets, optimize Postgres and ClickHouse performance/cost, implement orchestration and observability, define data quality and reconciliation standards, and mentor data/product engineers.

Lead and run Alpaca's data governance and data security program across the lakehouse and analytics stack. Build policies, data classification, entitlement reviews, access controls, metadata/lineage, data quality standards, vendor and use-case reviews, audit evidence for SOC2/ISO27001, and support cross-border sensitive data requests while partnering with Data Engineering, Data Science, Security, Privacy, and Legal.

Build and maintain production dbt models on Databricks as the organizations source of truth. Ensure data quality via testing, CI/CD, and documentation. Create Databricks dashboards and semantic metrics, support external reporting, and partner with engineering and analytics teams to turn business problems into durable data products. Drive AI-native workflows using tools like Cursor and Claude Code.

The Senior Data Engineer will build and maintain ETL/ELT pipelines, optimize SQL queries, and collaborate on machine learning workflows, driving data engineering best practices.

As a Senior Forward Deployed Data Engineer, you will design and implement data pipelines for media agency clients, manage technical delivery, and ensure data security while mentoring junior engineers and communicating effectively with stakeholders.

The Data Engineer is responsible for designing data workflows and pipelines, implementing systems engineering principles, and deploying data streaming technologies while ensuring compliance with industry regulations and best practices.

The Marketing Data Scientist will analyze data, onboard clients, develop data pipelines in R, and propose improvements to enhance efficiency.

As a Data Scientist, you will analyze blockchain data to derive insights, develop and deploy predictive models, and improve product offerings.

Build reliable data models and semantic layer for AI-powered analyses. Support Product, Finance, and Customer Success with SQL-based analytics, BI dashboards, and stakeholder storytelling. Standardize data team processes, prioritize work, and collaborate across company to enable data-informed decisions.