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

Capital One • McLean, VA

8d • $229,900 - $262,400

About The Position

Capital One is seeking a Data Engineer to help drive a major technology transformation by building cloud-first data solutions and applications. The role works across Agile teams and with product managers, software engineers, data analysts, and data scientists to design, develop, test, implement, and support data platforms and solutions. Technologies mentioned include Python, Spark, relational and NoSQL databases, Databricks, and Snowflake.

The engineer independently delivers cloud data solutions, makes architectural and platform decisions, and leads large-scale data initiatives from end to end. The position emphasizes scalable, resilient, and operationally efficient pipelines; reusable, maintainable engineering patterns; clear communication of technical concepts and outcomes; and mentoring other engineers. The role is listed in Chicago, Illinois; McLean, Virginia; and Riverwoods, Illinois. Capital One will consider sponsoring a qualified applicant for employment authorization.

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 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 building or supporting distributed data or compute workloads using tools such as EMR, Spark, Glue, or Databricks.
• 5 or more years designing, implementing, and operating real-time or streaming data pipelines.
• 3 or more years working on data observability, such as Monte Carlo or Splunk, or data orchestration tools, such as Airflow or Dagster.
• 5 or more years working with unstructured or semistructured data using NoSQL databases such as MongoDB, Cassandra, or DynamoDB.
• 5 or more years designing and supporting data warehousing solutions such as Snowflake or Redshift.
• 3 or more years working in an Agile development environment.
• 3 or more years 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 a team 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 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 that support customer experiences.
• Independently design, build, and deliver cloud data solutions and applications with little or no supervisor or manager support.
• Architect and enforce common data engineering design patterns to promote code quality, maintainability, and reusability across 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, and operationally efficient data pipelines and platforms that perform robustly as data volumes and business demands increase.
• Contribute hands-on technical work and innovation while 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 based on technical and business requirements.

Benefits

• Comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being; eligibility varies by employment 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