Top Analysis Reporting Jobs in St. Louis, MO

Designs, develops, deploys, maintains, and optimizes ETL processes and cloud-based system integrations for healthcare data warehousing and business intelligence. Works with DHA and Cerner inpatient and outpatient data, develops database procedures, validates migrations, performs data mapping and modeling, and supports data science and machine learning teams. The role also documents processes, tunes performance, captures client requirements, and manages data transformation initiatives.

Leads enterprise data architecture across Federal Student Aid’s Common Data Model, CRM platforms, servicer integrations, analytics, and downstream systems. Defines canonical models, systems of record, data contracts, integration patterns, governance, lineage, quality, security, privacy, and lifecycle controls. Develops reference architectures, decision records, modernization roadmaps, and data-flow designs; evaluates architectural tradeoffs; supports leadership reviews; collaborates across engineering, integration, security, analytics, product, and business teams; and mentors technical staff.

The Lead, Advanced Analytics, Acquisition will analyze data, define metrics, conduct A/B tests, and collaborate with teams to drive growth and insights.

Designs enterprise Power BI semantic models and executive dashboards, translating ambiguous business questions into KPIs and visual narratives. Integrates data from enterprise sources, improves data quality with engineering teams, and establishes governance for accurate, auditable reporting. Mentors BI professionals, defines modeling and metric standards, optimizes performance and scalability, and influences senior stakeholders through trusted insights.

Design, build, and maintain scalable Snowflake data pipelines and workflows. Ingest, transform, and deliver data from EHRs, claims, APIs, and CRM systems into analytics-ready structures. Optimize Snowflake performance and costs through warehouse sizing, query tuning, data modeling, and security design. Implement orchestration, DevOps, version control, and CI/CD practices while collaborating with client architects and analysts. The role also involves explaining technical tradeoffs, supporting repeatable deployments, adapting to unfamiliar challenges, and mentoring others.

Own and improve high-impact data products, ingestion pipelines, and granular data feeds. Analyze large datasets to answer client and stakeholder questions, ensure data accuracy, and derive insights. Collaborate with clients and internal teams, develop new methodologies and features, and scale third-party datasets. The role includes training in SQL, PySpark, Python, and internal tools.

Lead data analysis for product and customer decision-making by defining metrics, measuring adoption, engagement, retention, and customer health, and translating complex data into actionable insights. Partner with product, engineering, customer success, and sales teams; automate and improve data processes; support product strategy and customer reviews; strengthen data governance; and mentor analysts.

Lead and develop Analytics Engineers responsible for shared data foundations, platform capabilities, semantic models, developer tooling, and AI-enabled workflows. Define the team vision, operating model, roadmap, standards, and success measures. Partner cross-functionally to prioritize initiatives, improve adoption, guide architecture, and deliver scalable data assets and automation. Drive responsible AI-assisted development, data reliability, testing, documentation, observability, and cost management while contributing hands-on when needed.

Leads enterprise data engineering and platform execution, including modernization roadmaps, cloud transformation, governance, reliability, operational excellence, budgets, vendors, and investment prioritization. Directs multiple engineering managers and teams across platform engineering, data engineering, integration, and operations. Partners with architecture, security, compliance, analytics, clinical, product, finance, and business leaders to deliver secure, scalable, resilient data capabilities supporting AI, interoperability, reporting, and digital transformation.

Transforms complex retail and consumer data into business-ready datasets, scalable pipelines, reporting solutions, automation, and client deliverables. Designs data integration solutions in Unify, troubleshoots connector issues, explores AI-driven improvements, and partners with Product, Engineering, Client Development, Operations, and business stakeholders to define requirements and deliver scalable solutions.