Data Engineer 5

Capital One • McLean, VA

5d • $209,000 - $286,200

About The Position

Capital One is seeking a Data Engineer to help drive a major transformation by combining data with emerging technologies. The role works across Agile teams and partners with product managers and software engineers to deliver cloud-first data solutions and applications. It calls for independently designing, building, and delivering data solutions, making critical architectural decisions, and balancing hands-on engineering with mentoring and technical leadership.

The engineer designs and builds scalable, resilient, and operationally efficient data pipelines and platforms; establishes and enforces shared data engineering patterns; and communicates technical concepts and data outcomes to stakeholders. The work spans distributed data, machine learning, microservices, lakehouse and full-stack systems, relational and NoSQL databases, and cloud data warehousing platforms such as Databricks and Snowflake.

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 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 with data observability tools such as Monte Carlo or Splunk, or 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.
• Influence 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 data warehousing platforms including 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.
• Partner with product managers and software engineers to deliver cloud-first data solutions and applications.
• 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 peers and junior engineers.
• Lead large-scale data initiatives end to end, make 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 employment status and management level.
• Eligible 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