Top Analysis Reporting Jobs in Richmond, VA

Leads enterprise analytics strategy, data integration, governance, reporting, dashboards, forecasting, and advanced business analysis. Manages analysts and partners with Sales, Pricing, Finance, Product Management, Operations, and executive leadership to translate complex data into actionable recommendations. Oversees Power BI dashboards, KPI frameworks, data quality, predictive modeling, and reporting automation to improve growth, profitability, pricing, and operational performance.

The Senior Pharmacy Business Analyst monitors pharmacy performance, identifies trends and root causes, and translates operational and financial data into actionable recommendations. The role builds Tableau dashboards, queries enterprise data warehouses with SQL, prepares executive reporting, and partners with pharmacy, Finance, Operations, and Clinical leaders. Responsibilities include recurring report ownership, quality assurance, planning support, ad hoc analysis, and communicating insights to senior stakeholders.

Develops business intelligence applications and data models using Qlik, SQL, and related tools. Performs ETL, data validation, analytics process improvement, and impact analysis for reporting changes. Provides data-driven insights, supports audits and legislative inquiries, leads stakeholder meetings, and mentors teammates as a business intelligence subject matter expert.

Leads integration of healthcare payer feeds and internal enterprise data into WellBe’s data platforms. Responsibilities include data mapping, transformation, validation, normalization, quality monitoring, governance, metadata and lineage management, requirements gathering, and supporting ingestion pipeline delivery. The role partners with engineering and analytics teams, serves as a subject matter expert, and ensures reliable data for value-based care, reporting, and operational use cases.

Leads the architecture, implementation, and evolution of enterprise data platforms, including Master Data Management, data lakes, lakehouses, and cloud data services. Manages data engineers and developers, establishes engineering standards, oversees Azure-based integrations and pipelines, and owns data applications, databases, security, governance, and production support. The role also directs technical strategy, manages multiple initiatives, and communicates architecture and delivery plans to stakeholders.

Design, implement, and optimize large-scale data ingestion, cleansing, transformation, loading, replication, and display systems. Develop data pipelines, profiling, deduplication, matching logic, and custom data-source solutions. Maintain data-layer documentation, collaborate with data scientists on AI/ML workflows, and use Java technologies, REST APIs, Airflow, ETL, SQL, and Linux in a Big Data environment.

The Forward Deployed Data Engineer will manage AI-automated data platform migration projects, ensuring timely delivery and communication with clients while utilizing proprietary tools.

The Data Governance Specialist develops and enforces data governance, quality, privacy, risk, and compliance standards across organizational data assets. Responsibilities include managing metadata, data dictionaries, lineage, classification, retention, PII controls, DPIAs, risk assessments, audits, dashboards, and data-quality remediation. The role partners with engineering, security, legal, and business teams, provides stakeholder training, investigates data integrity issues, supports incident escalation, and automates governance processes through scripting.

Lead the data engineering strategy and architecture for distributed pharmaceutical data systems. Design and scale pipelines, APIs, and data platforms while ensuring reliability, governance, quality, and PII/PHI protection. Drive technical decisions, improve developer efficiency, deliver business impact, influence cross-functional roadmaps, mentor engineers, communicate with executives, and promote organizational adoption of new technologies and processes.

Design and own a unified data model for an AI-driven pipeline risk platform. Responsibilities include lakehouse and medallion architecture, geospatial modeling, entity resolution, ingestion patterns, governance, lineage, data quality, access controls, and traceability. Partner with data engineers, application engineers, and data scientists to implement schemas, pipelines, serving layers, and optimization strategies while maintaining clear architecture documentation.