Top Data & Analytics Jobs in St. Louis, MO

The Surveillance Analyst analyzes trading activity for potential violations, validates output from surveillance systems, and communicates with member firms. They conduct research, assist in survey design, and document investigative processes.

Own forecasting and capacity planning for contact center and pharmacy operations. Build short- and long-term forecasts, translate them into staffing plans, analyze trends and variances, develop dashboards, support scenario modeling, and partner with Operations, Finance, HR, and Engineering to align hiring and optimize systems.

Serve as a subject matter expert for data requests and system updates: design database structures, build and maintain data pipelines, write and optimize SQL, clean and transform data with SQL and Python, create dashboards and visualizations, analyze large datasets, and automate processes to improve data quality and workflows.

As a GTM Engineer, you will improve the efficiency of Marketing and SDR teams through automation, AI, and workflow design. You will partner with various teams and take ownership of the solution lifecycle from discovery to delivery.

The role involves acting as a subject matter expert in Data and AI, providing technical expertise during sales processes for Azure-based solutions, and mentoring team members to enhance client experiences.

The Business Analyst will gather requirements from clients, support project lifecycles, conduct demos, and manage change, while utilizing ServiceNow. Responsibilities include documentation and relationship building.

As a Senior Analytics Engineer, you'll build data pipelines and dashboards, partner with stakeholders for KPI development, and ensure data quality across the company.

This role involves designing measurement strategies, analyzing campaign effectiveness, and collaborating with stakeholders to optimize marketing efforts for customer acquisition.

As a Business Intelligence Analyst, you will drive analytics, optimize processes, and generate insights to enhance customer success, developing dashboards and predictive models while collaborating with leadership and cross-functional teams.

Perform hands-on data cleansing, linking, validation, and standardization; monitor and report data quality metrics; identify recurring issues and recommend process or automation improvements; organize and streamline workflows; maintain documentation and data standards; test and provide feedback on tools and AI-assisted features; serve as a point of contact for data integrity questions.