Top Remote Senior Level Data & Analytics Jobs in Tulsa, OK

Design, build, and optimize Snowflake-based data warehouses and dbt transformation pipelines. Develop scalable ETL/ELT ingestion, tune SQL and warehouse performance, implement data quality/testing (dbt tests), and maintain CI/CD and version-controlled data workflows while collaborating with analysts, data scientists, and stakeholders.

This role involves writing user stories, supporting agile team processes, and driving customer discovery to enhance technology solutions for clients.

Embedded, senior technical engineer who designs and builds AI-ready, cloud-native data architectures using Alteryx One and major cloud data platforms. Convert analytics workflows into production solution patterns, deliver pilots-to-production quickly, enable partner-led delivery, and create documentation and playbooks for scalable deployments.

The role involves designing and owning the architecture of a workforce intelligence platform on Snowflake, leading architectural decisions, and ensuring compliance for a Marketplace listing.

The Analytics Manager analyzes complex datasets, builds models, conducts scenario analyses, and provides strategic recommendations to guide decision-making for leadership.

The Actuarial Specialist produces analyses, manages projects, and oversees team workload to enhance actuarial processes in the organization, ensuring business growth and profitability.

Design and deploy scalable data infrastructure and models, maintain CDC pipelines, build reporting tools and dashboards, write high-performance SQL, partner with product and engineering to enable enterprise-wide analytics and data-driven decision making.

The Data Engineer III will lead the development and maintenance of data infrastructure and tools, ensuring data integrity while driving strategic data initiatives.

Responsible for designing, developing, and maintaining scalable data pipelines and infrastructure, implementing data governance, and collaborating with teams to meet data needs.

The role involves building and optimizing data pipelines, modeling datasets, applying machine learning techniques, and supporting data strategies. Candidates should have experience with Python, data lakes, warehousing, and related technologies.