Top Analysis Reporting Jobs in Richmond, VA

The Technical Enablement & Documentation Lead is responsible for developing and maintaining product documentation, creating a certification program, and collaborating with teams to improve the knowledge experience for Flosum's users.

As a Senior Marketing Data Scientist, you will conduct quantitative analyses, manage marketing tests, and provide actionable insights to enhance marketing performance and investment effectiveness.

Lead forecasting, capacity planning, and workforce insights for customer experience operations. Build and backtest forecasting models, maintain dashboards (Tableau, Databricks, Five9, Verint, Excel), manage vendor forecasts, perform scenario modeling, and deliver executive recommendations to align staffing with service and cost goals.

Lead BI partner for product teams to design and maintain scalable data pipelines, dbt transformations, data marts, and observability. Translate business needs into analytical frameworks, support A/B testing and ML feature engineering, collaborate on data governance, and champion CI/CD and best practices for analytics engineering.

The Senior Analyst will support product engineering efforts by analyzing digital solutions, enhancing user experiences, and collaborating across teams to deliver insights and improvements.

Lead and grow a software engineering team focused on RISC-V software ecosystem, collaborating with teams to establish RISC-V as the standard.

Lead definition and evolution of cloud data architecture and analytics platform using a lakehouse approach. Design secure, scalable AWS data platforms, medallion data layers, data modeling standards, ETL/ELT and streaming pipelines, governance, observability, and AI/ML enablement. Drive tool selection, cross-team architecture reviews, mentor engineers, and enforce data security, compliance, and operational excellence across the organization.

Design, implement, and manage scalable multi-cloud data platforms and ELT pipelines (AWS/Azure). Build and optimize Snowflake data models, dbt transformations, and Airflow orchestration. Define data engineering standards, enforce governance and quality, collaborate cross-functionally, and mentor global/offshore teams.

Build reliable data models and semantic layer for AI-powered analyses. Support Product, Finance, and Customer Success with SQL-based analytics, BI dashboards, and stakeholder storytelling. Standardize data team processes, prioritize work, and collaborate across company to enable data-informed decisions.

Design, build, and operate production-grade data ingestion pipelines and dbt transformations into a Databricks lakehouse. Establish data quality, monitoring, and observability. Partner with Product and Engineering to enable metrics, dashboards, and longitudinal patient analytics supporting clinical and operational decision-making.