Top Remote Analysis Reporting Jobs in St. Louis, MO

The Sales Analyst analyzes sales and marketing data to improve efficiency and revenue. Responsibilities include KPI development, market analysis, and CRM data management.

The Senior Data Analyst gathers business and analytics requirements, evaluates internal and external data sources, and ensures data quality, governance, lineage, and validation. The role maintains data dictionaries and master data, defines KPIs, reconciles dashboards, documents data transformations, identifies automation opportunities, and delivers actionable insights. It also supports privacy controls, disaster recovery requirements, platform cost optimization, stakeholder communications, and mentoring of less experienced team members.

Leads enterprise data engineering initiatives by designing and optimizing data pipelines, data lakes, warehouses, and analytics infrastructure. Uses Snowflake, Informatica, SQL, and related technologies to integrate diverse data sources, improve data quality, secure PII, and support analytics and data science teams. Establishes governance, monitoring, scalability, performance, and cost-efficiency practices while mentoring teammates and partnering with stakeholders on complex technical solutions.

Manage enterprise BI and analytics platforms, data governance, access controls, and self-service reporting. Translate business requirements into governed dashboards, KPIs, metrics, and actionable insights using Power BI, Tableau, Azure, SQL, and multiple database technologies. Ensure data quality, platform scalability, business continuity, cost efficiency, and secure information delivery. Partner with business and technology stakeholders, lead process improvements, resolve complex issues, and mentor data professionals.

Leads data and analytics engineering for Zero Trust maturity assessment and implementation measurement. Designs common data models, integrates and validates enterprise data, develops maturity and risk metrics, supports prioritization and gap analysis, and establishes data quality, lineage, traceability, and documentation practices. Partners with dashboard and automation specialists to streamline reporting and data refresh workflows.

Develop and maintain Power BI, Tableau, or comparable dashboards for the VA Zero Trust acceleration effort. Translate validated maturity and implementation data into scorecards, trend views, gap analyses, risk visualizations, and executive reports. Partner with data engineering teams to automate refreshes, ensure consistent metric definitions, validate source data, and document dashboard logic. Support enterprise-level and drill-down analysis across pillars, capabilities, and implementation waves.

Builds and maintains scalable data pipelines and cloud infrastructure supporting an AI biomedical research platform. Responsibilities include ingesting, harmonizing, validating, and documenting multimodal neurodegeneration datasets; managing cloud storage, compute, permissions, and costs; implementing de-identification and data governance workflows; supporting research data repositories and AI/ML teams; and maintaining data lineage, catalogs, and technical documentation. The role requires strong Python and SQL skills, software engineering discipline, cloud experience, and neurodegeneration research data expertise.

Design and architect robust ETL/data pipelines on cloud platforms. Build and automate data ingestion and preprocessing using Spark/Databricks, AWS Glue, Airflow. Implement warehousing architectures (EDW, DM, ODS, MOLAP/ROLAP), ensure SQL-based data quality, use version control, and deliver tested production pipelines while communicating with stakeholders.

Build and maintain product analytics dashboards, validate and QA event tracking, translate event data into analytics-ready datasets, run funnels/cohorts/retention analyses, support A/B testing, and surface actionable product insights to Product, Growth, and Data stakeholders.

Design, build, and maintain data pipelines, dashboards, and predictive models. Analyze large datasets to generate actionable insights, collaborate with stakeholders, ensure data quality and governance, and document/share analytics best practices to support Marketing, Product, Operations, and Workforce teams.

Analyze large, complex datasets to generate actionable insights; build SQL queries and data pipelines; develop predictive models (regression, clustering, forecasting); create and maintain dashboards; ensure data quality and governance; collaborate with stakeholders to translate findings into business recommendations and document work.

Analyze large datasets, build SQL queries and data pipelines, develop predictive and statistical models, create dashboards, ensure data quality/governance, and translate insights into actionable recommendations for Marketing, Product, Operations, and Workforce teams.

Build and maintain data infrastructure, integrate veterinary partner systems, develop dbt models and pipelines, provide analytics modeling, testing, reporting, and deliver actionable insights to customers and internal teams.

Design and build scalable data foundations, pipelines, semantic models, and AI-grounded tooling. Lead platform-level AE tooling, reusable dbt patterns, observability, cost strategies, and agentic AI workflows. Partner with stakeholders, guide team technical development, and drive cross-functional data initiatives from ambiguous problems to production.

Design, build, and maintain scalable AWS data pipelines, data lakes, and warehouses; develop ETL/ELT processes; create and manage AWS QuickSight dashboards (including RLS, embedding, Topics/Spaces, and QuickSight Q); ensure data quality, governance, security; optimize performance; collaborate with stakeholders and support production reporting.