Top Analysis Reporting Jobs in St. Louis, MO

Perform enhancements and upgrades to USPS programs, develop and maintain database schemas, monitor databases for performance, and ensure software compliance standards are met.

Leads WWT’s Data Reliability and AI Readiness pillar, setting enterprise AI data strategy and standards for trustworthy, reusable data. Oversees certification, lineage, quality SLAs, readiness scoring, data products, dashboards, and AI-powered reporting. Coordinates IT, AI engineering, analytics, business, and data teams on enterprise initiatives. Manages a Product Owner and engineers, improves operational data, promotes certified data products, and translates internal AI and data capabilities into customer-facing value.

Short-term paid project to export your personal financial data from at least seven apps (including five mandatory categories), create two financial artifacts (spreadsheets or trackers), redact PII locally using a provided tool, and submit matching, recent historical datasets for quality review and final approval. Paid per approved dataset; expected completion within the project timeline.

Short-term, paid project to export and submit personal financial data from multiple apps. Participants must provide exports from specific financial categories, create two financial artifacts, redact PII on-device using a provided tool, and follow quality and timeline requirements until Final Approval.

The Data Operations Analyst at Revecore will manage data operations, troubleshoot issues, and optimize data pipelines while collaborating with Data Engineering.

Design, build, and optimize enterprise ETL/ELT pipelines, cloud data lakes, lakehouse solutions, data models, and semantic layers across AWS and Azure. Ensure data quality, security, performance, reliability, and governance while supporting analytics, reporting, and AI initiatives. Collaborate with product, engineering, IT, and business stakeholders to translate requirements into scalable data solutions, monitor pipeline health, troubleshoot issues, and improve data architecture and practices.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

Support analytics by cleaning, validating, and transforming data; build dashboards and KPI reports; perform trend, cohort, and A/B testing analyses; improve data pipelines and forecasting; present insights to cross-functional teams.

Leads advanced data analysis to guide product and business decisions, develops metrics, dashboards, data products, and AI-enabled reporting systems, and translates findings into strategic recommendations. Partners with data engineers on dimensional data models, data governance, and quality controls. Mentors analysts, aligns roadmaps with business priorities, improves problem framing, and communicates insights effectively to technical and business stakeholders.

Lead infrastructure analytics for compute, storage, and networking: design scalable data pipelines, define core metrics, build dashboards and APIs, ensure data quality and governance, drive instrumentation fixes, and advise executives on capacity, utilization, and readiness.

Leads enterprise analytics engineering by establishing statistical standards, validation and QA practices, measurement frameworks, reporting standards, governance, reusable data pipelines, automation, and production-grade analytics solutions. Connects Commercial, Medical, Market Access, and other teams to enable scalable, trusted, compliant, and decision-focused analytics. Requires strong technical and cross-functional leadership, analytics infrastructure expertise, and the ability to build enterprise capabilities from ambiguity.