Top Analysis Reporting Jobs in Richmond, VA

Performs data analysis, business intelligence, data management, and data quality work. Builds data solutions, dashboards, metrics, and self-service tools; analyzes complex datasets; partners with business and technology teams; manages metadata, lineage, access governance, and data quality monitoring. Requires quantitative education and experience with scripting, visualization, data querying, Snowflake, and related analytics platforms.

Design, build, and lead scalable cloud data pipelines, platforms, and end-to-end data solutions. The role involves distributed data engineering, data modeling, relational and NoSQL databases, real-time processing, warehousing, architecture decisions, and evaluating platforms such as Snowflake and Databricks. Responsibilities also include mentoring engineers, enforcing engineering standards, collaborating across Agile teams, and delivering transformative data initiatives independently.

Analyze and optimize credit line increase valuations infrastructure, develop financial and credit risk models, identify business opportunities, and provide strategic recommendations. Partner with leadership, model developers, and cross-functional teams to support lending products, pricing, marketing, customer experiences, and credit performance. Translate complex analyses into clear narratives, influence stakeholders, test hypotheses, lead implementation, and establish monitoring plans.

Own supervisory data management and governance as a subject matter expert. Define data quality rules, metrics, monitoring, issue remediation, taxonomies, metadata, and cataloging practices. Partner with business, IT, and platform teams to improve data reliability, accessibility, lifecycle management, and future AI readiness. Translate business needs into technical requirements, facilitate adoption of governance practices, and communicate recommendations to technical and executive audiences.

Owns go-to-market funnel reporting and analytics from lead generation through pipeline, bookings, and ARR. The role analyzes conversion, pipeline quality, CAC, LTV, marketing ROI, and sales efficiency across segments and channels. It builds dashboards, forecasts, and executive insights while partnering with Marketing, Sales, Revenue Operations, and Finance on attribution, data integrity, budgeting, and investment decisions.

Lead development and execution of data-driven strategies, manage project planning and delivery, mentor and coach team members, ensure quality and ethical standards, design cloud data architectures and governance frameworks, analyze complex datasets to produce actionable insights, and drive continuous improvement using advanced analytics and modern data technologies.

Lead marketplace analytics for Affirm’s two-sided ecosystem by optimizing monetization, supply, demand, pricing, profitability, and user experience. Build KPI dashboards, analyze marketplace and affiliate performance, conduct experiments, evaluate incentives and placement, and perform loan-level profitability analyses. Partner cross-functionally with Product, Engineering, Sales, Client Success, and Marketing to improve merchant value, consumer engagement, conversion, and sustainable GMV growth.

Build Affirm’s narrative intelligence function by analyzing audience, behavioral, brand, media, survey, and business data. Develop narrative measurement frameworks, dashboards, analytical standards, and feedback loops to assess how effectively communications resonate. Design rigorous research studies, integrate qualitative and quantitative evidence, identify meaningful patterns, and translate findings into positioning, messaging, campaign, content, and communications strategy. Partner across analytics, research, marketing, creative, and communications teams to turn complex evidence into actionable decisions.

Lead Toast’s global People Analytics function across data engineering, analytics, data science, and employee listening. Set the people data and reporting strategy, establish trusted metrics and governance, partner with executives on workforce decisions, measure People program impact with Finance, and build scalable self-service analytics. Manage a distributed US and India team, oversee executive and board-level workforce analysis, and collaborate with privacy, legal, People technology, AI, and data engineering teams.

Performs data profiling, quality analysis, governance, metadata and lineage management, and Master Data Management support. Collaborates with data owners, stewards, business stakeholders, and technical teams to resolve issues and improve governance processes. Maintains glossaries, dictionaries, controls, metrics, and reports; supports enterprise data initiatives, communications, training, and stakeholder adoption. Provides guidance to less experienced analysts while applying governance standards and documenting business requirements.

Lead enterprise data architecture across warehouses, data lakes, downstream applications, and AI workloads. Design data models, Medallion Architecture, ELT standards, semantic layers, APIs, access controls, and reusable datasets. Partner with business and BI teams to drive adoption, conduct architecture reviews, establish platform standards, and mentor data and analytics engineers. The role requires deep Snowflake, dbt, data modeling, pipeline, and semantic-layer expertise, with experience in AI data patterns and cloud infrastructure preferred.

Conduct qualitative and quantitative consumer research to identify needs, trends, and product opportunities. Analyze consumer feedback, translate findings into actionable recommendations, and support product roadmaps, positioning, experience design, and go-to-market planning. Collaborate with Product Development, Marketing, and Design teams while managing research partners and multiple projects.

Leads enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, transactional, AI, and vector-store use cases. Establishes data quality, governance, lineage, observability, access-control, and reliability standards. Defines AI-enabled data engineering patterns, mentors engineers, resolves complex architecture issues, and influences cross-functional roadmaps. Requires hands-on technical leadership and 15+ years of software, data engineering, or architecture experience.