Top Data & Analytics Jobs in St. Louis, MO

The Experienced Data Administration Coordinator will manage and analyze contract data, ensure data integrity in the CCS, and generate reports.

The Data Analytics Quality Engineer at Boeing is responsible for data analysis, process monitoring, quality metrics development, and ensuring compliance with regulations. The role involves collaboration with teams and stakeholders, and addressing non-conformance trends to suggest corrective actions.

This role involves applying advanced data science and machine learning methods to healthcare data, leading teams, managing client relations, and developing analytics products for payers, while mentoring junior staff.

Provide analysis in support of geospatial intelligence missions, manage data dissemination, and develop workflows for data analysis and visualization.

The Senior Analytics Engineer will design and implement scalable data solutions for Finance Analytics, reconcile data accuracy, and collaborate across teams to enhance financial datasets and reporting workflows.

The Senior Portfolio Lead will oversee data management services for clients, ensuring quality deliverables, strategic oversight, and optimal clinical data flows, while leading cross-functional teams and managing complex client relationships.

The role involves assessing fraud program effectiveness, compliance with regulations, and providing strategic insights for risk management within the financial services industry.

As a Senior Data Engineer, you will design and maintain scalable data models, build production data pipelines, and support analytics across various business functions.

The Director, Product Analytics & Strategy will lead market expansion, define product strategy, analyze market opportunities, and drive cross-functional collaboration to shape product direction and company growth.

The Senior Data Analyst collaborates with People teams to transform HR data into insights, conducts analyses, and builds dashboards to improve workforce effectiveness.

Lead design and implementation of shared, reusable analytics data models and pipelines. Drive standardization, governance, observability, and CI/CD for analytics; partner with Data Science, Infrastructure, and Product to certify metrics, modernize orchestration, and integrate AI-native tooling.