Data Engineer 5, Marketing and Messaging (Enterprise Platform Technology)

Capital One • McLean, VA

7d

About The Position

Capital One is seeking a Data Engineer 5 for Marketing and Messaging within Enterprise Platform Technology. The role involves building cloud-first data solutions and applications that support customer experiences and enterprise transformation. The engineer will work across Agile teams and collaborate with product managers, software engineers, data analysts, and data scientists. The position combines independent, hands-on engineering with technical leadership, architectural decision-making, mentoring, and communication with stakeholders.

The role focuses on designing, building, and operating scalable, resilient, and efficient data pipelines and platforms. It includes establishing reusable data engineering patterns, evaluating platform choices such as Snowflake versus Databricks, and supporting robust data solutions as data volumes and business demands grow. The engineer is expected to contribute to a collaborative data community, keep current with technology trends, and lead large-scale data initiatives from end to end.

This is a full-time role with location-dependent annual salary ranges: $209,000–$238,500 in Chicago, IL and Riverwoods, IL, and $229,900–$262,400 in McLean, VA. The role is also eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives. Capital One offers health, financial, and other benefits; eligibility varies by employment and management status.

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 distributed data experience.
• 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 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 across Agile teams to design, develop, test, implement, and support technical solutions using full-stack development tools and technologies.
• Influence and work with developers, data analysts, and data scientists experienced 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 such as Databricks and Snowflake.
• Stay current with data technology trends, experiment with and learn new technologies, participate in technology communities, and mentor members of the data community.
• Collaborate 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 to support alignment and shared understanding.
• Design and build scalable, resilient, and operationally efficient data pipelines and platforms.
• Balance hands-on technical contribution and innovation with mentoring and developing peers and junior engineers.
• Lead large-scale data initiatives end to end, make critical architectural decisions, and evaluate platform choices such as Snowflake versus Databricks against technical and business requirements.

Benefits

• Comprehensive health, financial, and other benefits supporting total well-being; eligibility varies by full-time or part-time status, exempt or non-exempt status, and management level.
• 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