Data Engineer 4

Capital One • Plano, TX

2d • $179,400 - $204,700

About The Position

Capital One is seeking a Data Engineer for its Refi Tech team, which simplifies auto refinancing by building and optimizing the end-to-end digital Auto Refi App experience. The team bridges pre-qualification and agent funding with features that help customers check savings, select offers, and complete applications. The role calls for a collaborative, empathetic team member who listens to stakeholders, builds trust, and grounds business decisions in customer impact.

The Data Engineer will develop and support cloud-first data solutions and applications, working with Agile teams and collaborating with product managers, software engineers, analysts, and data scientists. The position involves independently delivering data solutions, establishing reusable engineering patterns, communicating technical concepts to stakeholders, and building scalable, resilient, efficient pipelines and platforms. It also includes implementing security controls such as encryption and fine-grained access control to support data privacy compliance.

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 apply.
• 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 data pipeline design and development experience.
• 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 working 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 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 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.
• 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 supervisory support.
• Architect and enforce common data engineering design patterns to improve code quality, maintainability, and reusability across data platforms and pipelines.
• Communicate technical concepts and data outcomes clearly to internal and external stakeholders to foster 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.
• Implement data security standards, including encryption at rest and in transit and fine-grained access control, to support compliance with data privacy regulations.

Benefits

• Comprehensive, competitive health, financial, and other benefits supporting employee well-being.
• Eligibility 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