Data Engineer 5

Capital One • McLean, VA

1d • $209,000 - $286,200 • Hybrid

About The Position

Capital One is seeking a Data Engineer to help modernize its core banking technology and build cloud-native, secure, resilient data capabilities. The role supports critical business areas including Retail Bank, Small Business Bank, Commercial Bank, and Capital One Shopping, delivering real-time technology and cloud-first data solutions.

The Data Engineer collaborates across Agile teams and with product managers, software engineers, analysts, and data scientists to independently design, build, test, implement, and support data platforms, pipelines, and applications. The position combines hands-on engineering and architectural leadership, including evaluating platform choices, establishing reusable engineering patterns, and ensuring scalability, resilience, code quality, and operational efficiency. The engineer also communicates technical concepts and outcomes to stakeholders, mentors peers and junior engineers, and contributes to the broader data engineering community.

This is a hybrid position, with required on-site presence according to Capital One’s hybrid policy. The role is eligible for performance-based incentive compensation. The posted annual salary range varies by location: McLean, VA: $229,900–$262,400; New York, NY: $250,800–$286,200; Richmond, VA and Wilmington, DE: $209,000–$238,500.

Requirements

• Bachelor’s degree or higher in Computer Science or a related quantitative field, such as Statistics, Economics, Operations Research, Analytics, Mathematics, or Engineering.
• At least 6 years of application development experience; internship experience does not count.
• At least 4 years of experience in distributed data.
• At least 4 years of SQL experience.
• At least 4 years of programming experience with Python, Java, or Scala.
• At least 4 years of experience designing and developing data pipelines.
• At least 2 years of experience in data modeling and designing end-to-end data solutions using relational and non-relational database systems.

Nice To Haves

• Master’s degree in Computer Science or a related field.
• 8 or more years of data engineering experience.
• 4 or more years of data modeling experience.
• 9 or more years of application development experience, with demonstrated proficiency in Python, SQL, Scala, or Java.
• 5 or more years of hands-on experience designing, deploying, and operating data workloads in a public cloud environment such as AWS, Microsoft Azure, or Google Cloud.
• 5 or more years of experience building or supporting distributed data or compute workloads with tools such as EMR, Spark, Glue, or Databricks.
• 5 or more years of experience designing, implementing, and operating real-time or streaming data pipelines.
• 3 or more years of experience with data observability tools such as Monte Carlo or Splunk, or data orchestration tools such as Airflow or Dagster.
• 5 or more years of experience with unstructured or semistructured data using NoSQL databases such as MongoDB, Cassandra, or DynamoDB.
• 5 or more years of experience designing and supporting data warehousing solutions such as Snowflake or Redshift.
• 3 or more years of experience working in an Agile development environment.
• 3 or more years of experience developing user-centric reusable data products.

Responsibilities

• Collaborate with Agile teams to design, develop, test, implement, and support technical solutions using full-stack development tools and technologies.
• Influence teams of developers, data analysts, and data scientists working with machine learning, distributed microservices, lakehouse architecture, and full-stack systems.
• Use programming languages such as Python and Spark, open-source relational and NoSQL databases, and cloud-based data warehousing platforms such as Databricks and Snowflake.
• Stay current with data technology trends, experiment with new technologies, participate in technology communities, and mentor members of the data community.
• Work with product managers and software engineers to deliver cloud-first data solutions.
• Independently design, build, and deliver cloud data solutions and applications with little or no supervisory support.
• Architect and enforce common data engineering design patterns to promote code quality, maintainability, and reusability across data platforms and pipelines.
• Communicate technical concepts and data outcomes clearly to internal and external stakeholders.
• Design and build scalable, resilient, and operationally efficient data pipelines and platforms.
• Contribute hands-on technical work and innovation while mentoring and developing peers and junior engineers.
• Lead large-scale data initiatives end to end, make architectural decisions, and assess platform choices such as Snowflake versus Databricks against technical and business requirements.

Benefits

• Comprehensive health, financial, and other benefits supporting total well-being.
• Eligibility for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior