Data Engineer 4 (Risk Tech)

Capital One National Association; McLean (VA); On-site; USD 110,000 - 150,000; Full time

Job summary

Capital One is seeking a Data Engineer to join Risk Tech in McLean, VA. You will collaborate across Agile teams to design and deliver cloud-native data solutions, leveraging Python, Spark, and modern data platforms.

You will mentor peers, advance lakehouse architectures, and ensure scalable pipelines that empower AI-driven risk management and financial empowerment for millions of Americans. The role emphasizes building robust data pipelines, partnering with product managers, and communicating

Qualifications

• Experience with ML and data engineering in cloud environments.
• Strong understanding of data pipelines and architecture patterns.
• Ability to mentor data team members.

Responsibilities

• Collaborate with Agile teams to design, develop, test, implement, and support data solutions using full-stack tools.
• Influence a team of developers, data analysts and data scientists in ML, microservices, lakehouse architectures, and full-stack systems.
• Build and deploy data pipelines and platforms with focus on scalability, resilience, and operational efficiency.

Skills

Machine learning

Big data concepts

Mentoring

Tools

Python

Spark

Databricks

Snowflake

NoSQL databases

Relational databases

Job description

Do you love building and pioneering in the technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who solve real problems and meet real customer needs. We are seeking Data Engineers who are passionate about marrying data with emerging technologies. In this role, you’ll be at the forefront of driving a major transformation across Capital One.

In Risk Tech, we provide the foundation for Capital One to thrive in an uncertain world. Our engaged, empowered, and intelligent people produce outstanding products, working toward the common goal of transforming risk management with technology. We build data-driven tools that use machine learning to prevent risks & automatically detect issues before they impact our customers, our business, or our communities.

In this role at Risk Tech, you will work with our GRC team and partners across the company to build and deploy proprietary solutions for Risk management that are powered by state-of-the-art AI technology. Our products, enhanced with the transformative power of AI, are central to our business and deliver tremendous customer value.

What You’ll Do:

• Collaborate with and across Agile teams to design, develop, test, implement, and support technical solutions in full-stack development tools and technologies
• Influence a team of developers, data analyst and data scientists with deep experience in machine learning, distributed microservices, lakehouse architecture, and full stack systems
• Utilize programming languages such as Python and Spark, along with open-source relational and NoSQL databases, and cloud-based data warehousing platforms including Databricks and Snowflake
• Share your passion for staying on top of trends in data, experimenting with and learning new technologies, participating in internal & external technology communities, and mentoring other members of the data community
• Collaborate with product managers and software engineering to deliver robust cloud-first data solutions that drive powerful experiences to help millions of Americans achieve financial empowerment
• Independently design, build and deliver world-class cloud data solutions and applications with little or no support from supervisors or managers
• Architect and enforce common data engineering design patterns to ensure code quality, maintainability, and reusability across data platforms and pipelines.
• Serve as an ambassador for the data engineering team, communicating technical concepts and data outcomes clearly to both internal and external stakeholders to drive alignment and shared understanding.
• Design and build data pipelines and platforms with a focus on scalability, resilience, and operational efficiency, ensuring robust performance