Top Analytics Jobs in Richmond, VA

Lead a team of analysts and data scientists embedded in sales to build rigorous pilot analyses, create executive-ready business cases, expand consultative analytics across the partner lifecycle, and publish industry insights while coaching and scaling the Solutions Analytics function.

Leads advanced analytics for Airbnb’s Community Blueprint Quality team. Responsibilities include diagnosing customer experience issues, designing experiments, applying causal inference, developing metrics, using LLMs to analyze unstructured data, building dashboards and analytical artifacts, and translating complex findings into recommendations for senior leadership. The role partners cross-functionally with Product, Engineering, Data Science, Data Engineering, Operations, and Finance.

Own the company’s data function by designing and optimizing Snowflake architecture, pipelines, integrations, data models, BI dashboards, metric definitions, governance, and data-quality standards. Partner with leadership and cross-functional teams to translate operational needs into trusted data products. Automate recurring reporting, troubleshoot ingestion issues, perform ad hoc analysis, and establish scalable analytics practices as the first dedicated analytics hire.

Senior analytics leader who defines analytic strategy, leads a team of SQL analysts and BI developers, architects scalable frameworks for medical economics, risk, population health and value-based care, ensures methodological rigor in DBT/Spark and Quicksight dashboards, drives automation and AI integration, presents to executive stakeholders, and mentors senior analytic talent.

Leads the design and implementation of AI-powered data pipelines, enterprise feature stores, analytics-ready repositories, and self-service data access layers. Builds automated feature engineering, data quality, observability, and governance capabilities for large-scale data science workloads. Partners with Enterprise IT, establishes data availability and quality SLAs, integrates structured and unstructured data sources, and leads teams delivering scalable analytics infrastructure.

Leads analytics for SEO, AI search, website experience, CRO, and the organic enrollment funnel. Diagnoses performance across traffic, visibility, conversions, leads, applications, and enrollments; designs measurement approaches; reconciles web, search, CRM, and platform data; defines KPI and reporting requirements; and translates findings into executive recommendations. Partners with analytics and engineering teams to build scalable Databricks-based reporting and improve measurement of organic and AEO initiatives.

Build and maintain production-grade dbt data models and pipelines across ingestion, transformation, and visualization. Develop reliable datasets supporting reporting, analysis, finance, revenue recognition, and company decision-making. Collaborate with Finance, Engineering, Sales, Product, and Marketing to understand data needs, improve manual workflows, validate data accuracy, and create scalable solutions using modern data tools.

Build and own analytics for Provider Operations, including funnel, coverage, quality, tiering, retention, forecasting, and rate strategy. Develop dashboards, recurring reports, quality frameworks, and forecasting models that support operational decisions. Partner with Recruiting, Calendar Operations, Product, Engineering, and Data teams to improve tools and instrumentation. Present actionable insights to leadership and drive adoption.

Design, build, and maintain end-to-end data models and pipelines to support CX operations, compliance reporting, and customer insights. Create dashboards and metrics, enforce data quality and governance, automate recurring workflows, and partner with stakeholders and broader data teams to deliver self-serve analytics solutions.

Lead Insights & Analytics for Enterprise Risk Management: develop and execute data-driven risk strategies, manage delivery and requirements, mentor analysts, run rigorous monitoring and analysis, partner with business leaders, and communicate executive-level insights to drive risk outcomes.

Own the Finance and Operations data layer, integrating systems into Snowflake and Omni, building reusable data models, dashboards, governed metrics, validation checks, and reconciliation workflows. Partner with Finance, Operations, and Data teams to translate business processes into trusted analytics, improve data quality, automate reporting, and support leadership decision-making.

Own the architecture and hands-on development of Tala’s shared data and analytics platforms. Build semantic layers, governed business metrics, data models, pipelines, services, tooling, and AI-enabled analytics workflows. Lead cross-functional technical initiatives spanning Product, Engineering, Analytics, Finance, and Accounting; improve governance, reliability, usability, and documentation; resolve architectural bottlenecks; and mentor engineers through technical leadership.

Develops and manages enterprise business intelligence products, including semantic models, governed definitions, metadata, dashboards, and AI-assisted analytics capabilities. Translates complex business needs into measurable analytic solutions, advises stakeholders, supports adoption and training, and manages product lifecycle, quality, performance, and business impact. Partners with product managers and business leaders to prioritize work, manage risks, communicate value, and guide less experienced team members.

Leads data analysis, reporting, performance evaluation, and dashboard development for federal transition assistance programs. Manages data integration, modeling, quantitative and qualitative analysis, program evaluations, executive reporting, and performance measurement. Translates complex data into actionable recommendations supporting operational effectiveness, strategic planning, modernization, and continuous improvement. Collaborates with government leaders, program managers, technical teams, and stakeholders. The role is remote with occasional travel for in-person meetings.

Leads enterprise analytical modeling and semantic architecture, establishes standards for testing, documentation, governance, lineage, observability, and access, and develops reusable governed data products. Partners across Product, Engineering, Finance, Clinical, Research, Marketing, and AI teams; guides architecture reviews, data quality ownership, semantic layer design, and governance adoption. Mentors engineers and drives modernization of analytical environments.

Lead a team in data analytics to identify and mitigate compliance risks in lending using statistical modeling and machine learning. Oversee fair lending reviews and collaborate with cross-functional teams to implement data-driven strategies in compliance operations.

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.

Lead end-to-end analytical initiatives on partner enrollment funnels, ensure data quality, build reporting pipelines with BI/engineering, use AI-assisted tools to accelerate analysis, translate insights into strategic recommendations for leadership, and coach growing analytics team members.

Transforms enterprise IT operations, monitoring, KPI, and performance data into executive dashboards, heatmaps, trend analyses, forecasts, anomaly detection, and narrative insights. Integrates data from monitoring, ticketing, incident, and operational systems; develops standardized reporting templates; documents analytical workflows; and collaborates with engineering, service desk, cybersecurity, and PMO teams to support federal reporting and leadership decisions.

Leads analytics engineering and analytics-ready data architecture for government clients. Designs standardized data models, transformation logic, validation frameworks, and reusable integration patterns. Governs data quality, QA, backfills, and release processes while resolving complex data issues. Integrates web, email, social, survey, and client-system data to support dashboards, reporting, measurement, and insights. Advises stakeholders, influences enterprise standards, coordinates with product teams, and supports client-facing analytics workstreams.

Build and operate reliable ETL and ELT pipelines across lakehouse and transactional systems. Develop data models, enforce governance, integrate diverse data sources, and support analytics, APIs, and predictive tools. Monitor performance, data quality, lineage, cost, and uptime while collaborating with data science, AI, product, and innovation teams.

The Senior Manager will lead cross-functional analytics projects, validate analytical tools, and mentor junior staff while driving data-based business strategies.

Leads web analytics strategy, GA4 and GTM implementations, tagging specifications, dashboards, reporting, experimentation, and data-quality monitoring. Analyzes user behavior, conversion funnels, feature adoption, and A/B test results to provide actionable recommendations for product and business decisions. Partners with product, engineering, UX, and business stakeholders and communicates analytical findings to technical and non-technical audiences.

Leads the Business Intelligence and Revenue Analytics function, balancing team management with hands-on analysis. Develops dashboards, KPI frameworks, forecasts, and reporting for Sales, Marketing, Revenue Operations, and executives. Partners with Data Engineering on data models, pipelines, governance, and quality. Translates business needs into analytical solutions, delivers actionable insights, manages the analytics roadmap, prioritizes initiatives, and coaches a small team.

Leads the enterprise analytics organization, managing Analytics Engineers, Data Analysts, and BI Developers. Defines analytics strategy, delivers executive dashboards and operational reporting, establishes KPI and governance standards, builds semantic models and curated datasets, and drives self-service BI adoption. Partners with Engineering, Product, Finance, Operations, Customer Success, and executives to translate business needs into trusted analytical solutions while developing talent and prioritizing analytics initiatives.