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Leads Card Risk Management for Capital One’s Business Cards & Payments segment. Oversees Vertical Risk Managers and Risk Specialists, partners with executives and business lines, validates risk processes, develops risk frameworks, performs gap assessments, drives process improvement and governance, and manages cross-functional risk execution. The role requires significant financial services risk management, program management, internal consulting, and people leadership experience.

Manage article publishing operations across Capital One and Discover content hubs. Coordinate contractors and cross-functional partners, maintain process documentation, improve workflows, perform quality assurance, support platform and tool updates, and ensure governance and compliance. Balance publishing capacity and priorities while keeping projects on track and delivering consistent, high-quality content marketing execution.

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.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. Collaborate with data scientists, analysts, product managers, and software engineers while applying distributed data, lakehouse, streaming, database, and security technologies. Establish reusable engineering patterns, ensure performance and resilience, communicate technical outcomes, mentor team members, and deliver secure data solutions with minimal supervision.

Develops and enhances B2B trade credit product capabilities and customer servicing platforms. Partners with operations stakeholders, engineers, designers, data scientists, and business teams to identify opportunities, build business cases, define acceptance criteria, prioritize roadmaps, and deliver platform improvements. Breaks epics into features and user stories, validates solutions through data and research, manages risk, and communicates product strategy to stakeholders in an agile environment.

Lead full-stack software engineering projects and developer teams building cloud-based, distributed microservices and full-stack systems. Responsibilities include designing APIs and service boundaries, developing with multiple programming languages and databases, using AWS and container orchestration, improving performance, reducing technical debt, establishing engineering standards, mentoring engineers, and influencing technology strategy across Agile teams.

Designs, builds, and supports scalable cloud data platforms, pipelines, and applications. Responsibilities include data modeling, distributed processing, lakehouse architecture, data warehousing, security controls, observability, and real-time data solutions. The role collaborates with product and engineering teams, influences technical standards, communicates data outcomes, and mentors data professionals while delivering resilient solutions using technologies such as Python, Spark, Databricks, and Snowflake.

Lead software engineering projects and developers building cloud-based, full-stack solutions with distributed microservices. Design service boundaries, APIs, reusable frameworks, and scalable systems while establishing engineering standards, improving performance, reducing technical debt, and evaluating emerging technologies. Collaborate with product managers, architects, and Agile teams, mentor engineers, and contribute to long-term technology strategy using modern programming languages, cloud platforms, databases, containers, CI/CD, and observability practices.

Lead applied AI and data science work for recommendation and personalization systems. Partner with data scientists, engineers, and product managers to deliver customer-facing products; develop, train, evaluate, validate, and implement machine learning models; analyze large-scale numeric and textual datasets; apply statistical modeling, deep learning, causal inference, reinforcement learning, and recommender-system techniques; and communicate technical findings to business stakeholders while developing team talent.

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.