Data Engineer 4

Capital One • McLean, VA

1d

About The Position

Capital One is seeking a Data Engineer to help drive a major technology transformation by building cloud-first data solutions that support customer experiences and financial empowerment. The role works across Agile teams and collaborates with product managers, software engineers, developers, data analysts, and data scientists. It involves independently designing and delivering data solutions, applying common engineering patterns, and communicating technical concepts and outcomes to stakeholders.

The Data Engineer builds scalable, resilient, and operationally efficient data pipelines and platforms, using technologies such as Python, Spark, relational and NoSQL databases, Databricks, Snowflake, and cloud-based data warehousing platforms. The role also emphasizes data security, including encryption and fine-grained access control, and requires staying current with technology trends, learning new tools, participating in technology communities, and mentoring others.

The position may be remote or hybrid. Hybrid employees must follow Capital One's on-site policy for their designated office; remote employees must follow the company's Remote Worker Expectations. The role is eligible for performance-based incentive compensation. Salary ranges vary by location: $179,400–$204,700 for remote work regardless of location, and $197,300–$225,100 in McLean, Virginia.

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 4 years of application development experience; internship experience does not count.
• At least 2 years of experience in distributed data.
• At least 2 years of SQL experience.
• At least 2 years of experience with one programming language: Python, Java, or Scala.
• At least 2 years of experience in data pipeline design and development.
• At least 1 year of experience in data modeling and designing end-to-end data solutions using relational and non-relational database systems.

Nice To Haves

• 7 or more years of application development experience with demonstrated proficiency in Python, SQL, Scala, or Java.
• 4 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.
• 4 or more years of experience building or supporting distributed data or compute workloads using tools such as EMR, Spark, Glue, or Databricks.
• 4 or more years of experience designing, implementing, and operating real-time or streaming data pipelines.
• 2 or more years of experience with data observability tools such as Monte Carlo or Splunk, or data orchestration tools such as Airflow or Dagster.
• 4 or more years of experience with unstructured or semistructured data using NoSQL databases such as MongoDB, Cassandra, or DynamoDB.
• 4 or more years of experience designing and supporting data warehousing solutions such as Snowflake or Redshift.
• 2 or more years of experience working in an Agile development environment.
• 2 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.
• Influence and work with developers, data analysts, and data scientists across machine learning, distributed microservices, lakehouse architecture, and full-stack systems.
• Use programming languages, databases, and cloud-based data warehousing platforms to build data solutions.
• Keep current with data and technology trends, experiment with 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.
• Communicate technical concepts and data outcomes to internal and external stakeholders.
• Design and build scalable, resilient, and operationally efficient data pipelines and platforms.
• Implement data security standards, including encryption at rest and in transit and fine-grained access control, while supporting compliance with data privacy regulations.

Benefits

• Comprehensive health, financial, and other benefits supporting total well-being.
• Performance-based incentive compensation, potentially including cash bonuses and/or long-term incentives.

What This Job Offers

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