Data Engineer 5

Capital One • McLean, VA

2d • $209,000 - $286,200 • Hybrid

About The Position

Capital One is seeking a Data Engineer to help transform its technology foundation and build an internal, integrated, modernized core banking platform. The role focuses on creating modular, resilient, cloud-native, and secure engineering solutions for critical business lines, including Retail Bank, Small Business Bank, Commercial Bank, and Capital One Shopping.

The Data Engineer designs, develops, tests, implements, and supports cloud-first data solutions and applications. The position works with Agile teams and product managers, software engineers, data analysts, and data scientists, using technologies such as Python, Spark, relational and NoSQL databases, Databricks, and Snowflake. The role independently delivers solutions, establishes data engineering patterns, leads large-scale initiatives and architectural decisions, and mentors peers and junior engineers.

This is a hybrid position requiring adherence to Capital One's hybrid policy for on-site presence at the designated office. The role is eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.

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 apply.
• 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 experience in data engineering.
• 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 at least one public cloud environment: AWS, Microsoft Azure, or Google Cloud.
• 5 or more years of experience building or supporting distributed data or compute workloads using 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 working 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 and collaborate with developers, data analysts, and data scientists with experience in 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 including Databricks and Snowflake.
• Stay current with data trends and emerging technologies, participate in technology communities, experiment and learn, and mentor members of the data community.
• Collaborate with product managers and software engineers to deliver robust cloud-first data solutions that support customer experiences.
• 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 to promote alignment and shared understanding.
• Design and build scalable, resilient, operationally efficient data pipelines and platforms that perform reliably as data volumes and business demands grow.
• Balance hands-on technical contribution and innovation with mentoring and developing peers and junior engineers.
• Lead large-scale data initiatives from end to end, make critical architectural decisions, and evaluate platform choices such as Snowflake versus Databricks against technical and business requirements.

Benefits

• Comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being.
• 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