Top Remote Analysis Reporting Jobs in St. Louis, MO

Manage and optimize HubSpot and related CRM platforms, build workflows, automations, properties, lists, and reporting. Support HubSpot Membership modules, integrate paid media with CRM for lead capture and attribution, develop UTM and attribution frameworks, partner with hub teams on data collection standards, support user training, and improve platform adoption and audience segmentation.

Support large-scale voter contact programs by building and maintaining clean data tables, creating audiences and A/B test groups, prioritizing sites, delivering timely analysis and reports, and automating dashboards and processes to surface real-time insights for program teams.

Design and implement enterprise data architectures and lakehouse/warehouse solutions using Microsoft Fabric, Databricks or similar platforms. Lead data modeling, ETL/ELT pipelines, cloud integration, CI/CD, and stakeholder-facing consulting engagements to deliver scalable, secure analytics systems.

Own and implement the canonical data model and governance for a multi-tenant MEP SaaS platform. Architect data for AI/ML readiness (RAG, embeddings, vector search), design polyglot persistence and lake/lakehouse pipelines, lead staged modernization and migrations, produce hands-on prototypes and in-repo guardrails, and partner with platform, DB engineering, and ML teams to ensure data quality, lineage, and observability.

Lead design and delivery of enterprise BI and analytics solutions for healthcare coding and clinical data. Define BI architecture, data models, ETL pipelines, cloud-native analytics, and implement agentic/generative AI analytics for global customers while guiding engineering best practices and mentoring teams.

Analyze business performance, provide insights, improve processes, participate in continuous improvement projects, and collaborate with cross-functional teams.

The Sr Analyst will analyze and integrate datasets to support commercial strategy, providing tools and insights to leadership teams through data visualization and modeling.

Analyze customer implementation and product usage to identify drivers of adoption, retention, and expansion. Build dashboards, quantify configuration and workflow impacts, surface deployment friction, and partner with Product, Sales, and Customer Success to create evidence-based playbooks and metrics that inform roadmap and go-to-market strategies.

Develop and maintain Python-based data applications and pipelines: fetch data from databases/APIs/files, build Pandas DataFrames/NumPy arrays, write complex SQL and data transformations, handle nested JSON, and collaborate with data science teams to deliver production data platforms.

Remote entry-level Big Data Engineer role building data acquisition and munging pipelines. Requires strong Python3 (NumPy, Pandas), advanced SQL, scalable algorithm and data-structure implementation on distributed clusters, converting code to functional paradigms, and the ability to learn PySpark or Kafka quickly. Product-development, startup experience and real-world pipeline projects preferred.

Lead data-focused Salesforce initiatives: engage stakeholders, assess and map legacy data, define migration and governance strategies, execute migrations using ETL/Data Loader tools, validate data through testing and reconciliation, and support reporting, analytics, and user enablement in Agile environments.

Analyze business processes, gather stakeholder requirements, identify improvement opportunities, and deliver data-driven recommendations. Collaborate with cross-functional teams to ensure solutions align with business goals and support implementation through clear communication and presentations.

Analyze datasets to generate reports and visualizations that inform business decisions. Produce regular reporting, create data visualizations, and work cross-functionally to support data-driven strategies with attention to accuracy.

Design and deliver reusable supply-chain analytics products and persona-based dashboards using the modern data stack. Partner with operations and customers to prioritize requests, leverage AI to accelerate analysis, and tie insights to measurable business outcomes. Support data maturity, integration, and agile product delivery.