Top Remote Data & Analytics Jobs in St. Louis, MO

Assist in collecting, cleansing, and analyzing datasets to produce actionable insights, reports, and dashboards for informed business decisions.

Design, build, and own end-to-end data systems and pipelines powering payments, fraud response, and reporting. Implement streaming and batch integrations, APIs, and workflow orchestration. Define data correctness with monitoring, alerting, and tests. Create reusable patterns and collaborate with stakeholders to clarify requirements and tradeoffs.

As an Analytics Engineer, transform raw data into metrics, manage data pipelines, ensure data integrity, and collaborate with teams for analytics solutions.

The Data Privacy Compliance Analyst will support the development and implementation of data privacy programs, assist privacy operations, and ensure compliance with global privacy laws.

As a Senior Data Analyst, you'll provide actionable insights by analyzing product and engineering data, partnering with leadership on metrics frameworks and decision support for product delivery.

The Data Analyst will maintain KPI standards for Customer Support, build dashboards, produce analyses, and lead metric alignment conversations, ensuring accurate data interpretation and decision-making support.

Build dashboards and reports tracking enrollment, attendance, engagement, and attrition. Develop predictive models to identify at-risk students, analyze withdrawal causes, monitor early-warning indicators, partner with cross-functional teams to design retention interventions, and present insights to leadership while supporting data pipeline accuracy.

The Director of Data and Enterprise Applications oversees the architecture, development, and support of business applications and data platforms, driving digital transformation and ensuring data integrity and accessibility.

Lead design and development of web UI and REST middleware within an Agile team. Define requirements and acceptance criteria, implement Java/Spring and Angular solutions, build CI/CD pipelines, work with Product Owner and testers, and produce/update design documentation.

Design and run evaluation frameworks to assess third-party vendor data for fraud detection. Build lift analyses, backtests, and champion/challenger tests; profile vendor datasets for quality and coverage; collaborate with fraud, product, and data engineering to define ingestion and testing requirements; translate findings into actionable recommendations and maintain an internal knowledge base; support ad hoc analyses on fraud trends and model performance.