Top Analysis Reporting Jobs in St. Louis, MO

Analyze, trace, reconcile, and migrate insurance policy data from legacy systems into a modern policy platform. Build source-to-target mappings and ETL transformations, perform data cleansing and validation, write complex SQL, use cloud databases (AWS RDS/S3), and collaborate with stakeholders using GitHub and Agile tools. Heavy daily use of AI tooling; mentorship provided throughout migration lifecycle.

Design, modernize, and optimize hybrid cloud and legacy relational databases (Informix, DB2, MySQL, Oracle, PostgreSQL). Collaborate with engineers and security teams to implement migrations, performance tuning, data modeling, SQL/PLSQL development, automation, and DevSecOps practices to support cloud adoption and mission-critical operations.

Design, build, and maintain Scala-based ETL and streaming data pipelines, APIs, and data microservices in Azure. Optimize LLM fine-tuning and RAG workflows, support DevOps/CI-CD practices, document standards, mentor engineers, and manage data ingestion frameworks for analytics and partner integrations.

Support national community lending initiatives by developing ROI dashboards, analyzing event and CRM data, coordinating events and partners, enabling sales teams, managing budgets/contracts, and producing program performance insights to scale community outreach and homeownership programs.

Partner with Product, Design, Engineering, and Data to define KPIs and measurement for AI-powered features, guide instrumentation, design and evaluate experiments, build dbt models, create dashboards, and influence product analytics standards and long-term strategy.

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.

The Business Intelligence / Data Analyst will optimize the data reporting pipeline using Google Big Query and Omni, work with product management, implement reports, and conduct ongoing maintenance.

Own end-to-end data pipelines for multiple clients: extract, clean, transform, model and visualize data. Build dashboards and automated workflows, apply AI where useful, and present actionable insights to non-technical stakeholders.

Lead analytics and data science initiatives to drive measurable business outcomes across various functions, including Underwriting and Claims. Oversee the full analytics lifecycle, foster stakeholder engagement, and mentor junior team members.

The DMS Data Engineer is responsible for analyzing raw data, building data systems and pipelines, conducting complex data analyses, and collaborating on projects to enhance data quality and reliability.