Top Data Management Jobs in Richmond, VA

Leads enterprise analytical tool validation, assessing methodology quality, risk, technical issues, and business impact. Plans validation projects, researches emerging practices, develops solutions, communicates findings through reports and presentations, and mentors junior staff. The role requires extensive data analysis, statistical or econometric modeling, open-source data technology experience, data governance knowledge, predictive analytics expertise, and people management.

Build and operate scalable cloud data platforms and pipelines for Capital One’s Risk Tech organization. Responsibilities include designing distributed data solutions, enforcing engineering patterns, implementing security controls, collaborating across Agile teams, mentoring developers and analysts, and delivering AI-powered risk management products. The role requires expertise in Python, Spark, SQL, databases, cloud data warehousing, data modeling, and pipeline development.

Leads data analysis and data product initiatives involving large databases, modeling, testing, reporting, process improvements, and product analytics. Manages and mentors junior analysts, partners with technology and business teams, supports data-system design, and develops strategic tests for products and marketing materials. Requires strong analytical, programming, business intelligence, communication, and leadership skills.

Design, build, test, and support scalable cloud data solutions and pipelines. Develop data platforms using Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases, and cloud technologies. Establish reusable data engineering patterns, security controls, and operational standards. Collaborate with product managers, engineers, analysts, and data scientists; communicate technical outcomes; mentor data professionals; and deliver resilient, high-performance financial data products.

Lead data analytics strategy, mine and analyze large-scale datasets, build and validate models, improve reporting and operational processes, evaluate products and marketing initiatives, and consult on data systems. Manage and mentor junior analysts, collaborate across business units, oversee concurrent projects, and apply business intelligence tools to support customer experience and organizational decision-making.

Leads enterprise data analysis and analytical tool validation initiatives for card data products. Responsibilities include developing validation frameworks, planning validation projects, assessing methodology quality and risk, researching emerging analytical tools, prototyping best practices, preparing reports and presentations, and coaching junior staff. The role requires collaboration with data product and technology partners, independent problem solving, and management of concurrent projects.

Leads data analysis and analytics for Capital One’s card account data products. Responsibilities include mining large databases, validating data models, improving processes and reporting, evaluating products and marketing initiatives, consulting on data systems, and collaborating across business units. The role also manages and mentors junior analysts, supports strategic product decisions, and contributes to business intelligence and project delivery.

Leads Motorsports Data Infrastructure and Data Science teams while owning the strategy, architecture, and delivery of data platforms supporting telemetry, simulation, trackside, analytics, and AI/ML use cases. Responsibilities include managing managers, designing streaming and batch pipelines, productionizing ML models, setting engineering standards, managing Azure and Databricks capacity, protecting sensitive data, and partnering with racing and executive stakeholders. The role remains hands-on in architecture, coding, code review, incident support, and technical problem-solving.

Build and architect scalable cloud data platforms, pipelines, and applications for Capital One’s Risk Tech organization. Responsibilities include distributed data engineering, data modeling, warehousing, streaming, security controls, and full-stack solution delivery using Python, Spark, Databricks, Snowflake, and databases. The role influences developers, analysts, and data scientists; collaborates with product and engineering teams; establishes reusable design patterns; communicates technical outcomes; and mentors data professionals.

Leads Enterprise Supplier Management analytics strategy, data product development, analytical tool validation, and data ecosystem improvements. Partners with senior leaders, Product, Technology, Risk, and Operations teams to evaluate business models, manage strategic priorities, assess analytical tool risk, and develop insights. Oversees and mentors a team of at least four Data Analysts while communicating findings through validation reports and presentations.

Manages data risk for Capital One’s Risk Management organization by ensuring compliance with enterprise data standards, developing governance strategies and processes, monitoring data management activities, identifying remediation needs, and reporting gaps to leadership. The role provides guidance, influences enterprise policies, collaborates with stakeholders, supports change management, and addresses risks involving complex data systems and usages.

Manage the development, implementation, governance, and adoption of enterprise ontologies and optimized data models. Partner with Finance, Technology, Product, Machine Learning, and enterprise teams to support ERP replacement, semantic technology integration, data standardization, and data harmonization. Own the ontology roadmap, establish standards using W3C technologies, communicate strategic vision, manage and mentor teams, and advocate for semantic technologies across the organization.

Build and architect scalable cloud data platforms, pipelines, and applications. Design distributed, real-time, and batch data solutions using Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases. Establish reusable engineering patterns, data security controls, and operational standards. Collaborate with product managers, software engineers, analysts, and data scientists while mentoring team members and communicating technical outcomes to stakeholders.

Manage data governance and risk programs for Global Payment Networks. Analyze large datasets, build and validate models, improve data and reporting processes, establish controls, support privacy and regulatory compliance, and lead cross-functional projects. The role also includes mentoring junior analysts, managing concurrent initiatives, and consulting on data systems, product testing, and business intelligence solutions.

Leads enterprise compliance testing analytics, tooling strategy, and analytical projects. Partners with engineering, data science, legal, compliance, risk, and executive stakeholders to identify and mitigate compliance risks. Evaluates analytical tools, researches emerging practices, communicates findings through reports and presentations, and mentors junior staff. The role requires advanced data analysis, statistical or econometric modeling, large-scale data manipulation, data governance, and people management experience.

Leads data engineering architecture and modeling initiatives, designing scalable pipelines, cloud data platforms, and reusable data solutions. Collaborates with engineering, product, analytics, and data science teams while mentoring developers and establishing common design patterns. Responsibilities include distributed data processing, relational and NoSQL systems, data security, observability, streaming pipelines, and operational excellence across platforms such as Databricks and Snowflake.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. The role requires distributed data engineering, data modeling, relational and NoSQL databases, security controls, and cloud data warehousing. Responsibilities include influencing cross-functional technical teams, enforcing engineering patterns, supporting resilient and efficient data solutions, collaborating with product and software engineering teams, communicating technical concepts, mentoring data professionals, and staying current with emerging technologies.

Design, build, deploy, and support scalable cloud data platforms, pipelines, and applications for risk management. Lead technical direction across data engineering, analytics, and machine learning teams; establish reusable engineering patterns; implement security and privacy controls; and collaborate with product managers and software engineers. The role includes mentoring, stakeholder communication, distributed data processing, data modeling, warehousing, streaming, and both relational and NoSQL database solutions.

Analyze large-scale data, build and validate models, improve processes and reporting, evaluate products and marketing initiatives, and consult on data systems. The role requires programming, strategic analysis, collaboration across concurrent projects, and leadership in a hybrid environment.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Develop distributed data solutions using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Establish reusable engineering patterns, data security controls, and operational standards. Collaborate with product, engineering, analytics, and data science teams while mentoring others and communicating technical outcomes to stakeholders. Support resilient, high-performance data workloads and contribute to Agile delivery.

Leads large-scale data engineering initiatives for Risk Tech, designing and delivering scalable cloud data platforms, pipelines, and applications. Responsibilities include architecture, data modeling, distributed processing, cloud warehousing, AI-enabled risk solutions, platform evaluation, operational resilience, and technical mentoring. The role collaborates with product managers, software engineers, analysts, and data scientists while independently driving architectural decisions and communicating technical outcomes to stakeholders.

Leads enterprise analytical-tool validation, including framework refinement, project execution, methodology risk assessment, technical research, prototyping, and validation reporting. Partners with cross-functional leaders to influence business strategy, solves complex analytical problems, and coaches junior staff. Requires substantial data analytics, open-source technology, statistical modeling, and people-management experience.

Leads people analytics initiatives that support talent strategy and workforce decisions. Responsibilities include developing analytics frameworks, managing data projects, building AI-powered data products and self-service tools, analyzing workforce data, prototyping automation solutions, and delivering insights to HR stakeholders and senior leaders. The role also involves data strategy, dashboard development, project execution, and potentially people management.

Manages data governance and pricing-related risks by overseeing processes, controls, reporting, documentation, and improvement initiatives. The role analyzes process data, identifies gaps, resolves policy-related issues, collaborates on enterprise projects, represents departmental interests, and trains junior staff and internal stakeholders. Responsibilities include maintaining procedures and diagrams, recommending process changes, supporting executive reporting, and ensuring adherence to data management and governance frameworks.

Leads a data analytics team developing AI automation strategies for card data. Responsibilities include validating analytical tools, assessing methodology quality and risk, researching emerging technologies, prototyping best practices, creating governance frameworks, communicating validation findings, and mentoring staff. The role requires expertise in data analysis, statistical and econometric modeling, large datasets, open-source data technologies, data governance, predictive analytics, and LLM-enabled strategies.