Data Engineer 5

Capital One • McLean, VA

1d • $229,900 - $286,200

About The Position

Capital One’s Enterprise Data Tech (EDT) manages data technology, platforms, and experiences across the company. Its mission is to make data easy to find, use, understand, govern, and protect throughout the data lifecycle. EDT’s platforms support data pipelines, storage, and access, with a focus on evolving capabilities for AI and strategically expanding data platforms.

The Data Engineer 5 designs, builds, and delivers cloud data solutions and applications, leading large-scale data initiatives and making architectural and platform decisions. The role combines hands-on engineering with technical leadership, mentoring, and communication with stakeholders. The engineer works with Agile teams and product managers to create scalable, resilient, and operationally efficient data pipelines and platforms that support customer experiences.

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 experience in application development; internship experience does not apply.
• At least 4 years of experience in distributed data.
• At least 4 years of experience with SQL.
• At least 4 years of programming experience with at least one of 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 experience in data engineering.
• 5 or more years of hands-on experience designing, deploying, and operating data workloads in AWS.
• 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.
• 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.
• 4 or more years of data modeling experience.
• 3 or more years of experience working in an Agile development environment.
• 3 or more years of experience developing user-centric reusable data products.
• 3 or more years of experience working with data observability tools such as Monte Carlo or Splunk, or data orchestration tools such as Airflow or Dagster.

Responsibilities

• Collaborate across Agile teams to design, develop, test, implement, and support technical solutions using full-stack development tools and technologies.
• Influence and collaborate with developers, data analysts, and data scientists working 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 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.
• 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 foster alignment and shared understanding.
• Design and build scalable, resilient, and operationally efficient data pipelines and platforms that perform reliably as data volumes and business demands grow.
• Contribute hands-on technical work and innovation while mentoring peers and junior engineers.
• Lead large-scale data initiatives from 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.
• 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