Data Engineer 5 (Cloud Operations Resilience Engineering) (MEG)

Capital One • McLean, VA

3d • $209,000 - $286,200

About The Position

Capital One is seeking a Data Engineer to build and pioneer cloud-first data solutions and applications. The role involves leading large-scale data initiatives, making architectural decisions, and evaluating platforms such as Snowflake and Databricks based on technical and business requirements. The engineer will work across Agile teams and collaborate with product managers, software engineers, data analysts, and data scientists to deliver data platforms and pipelines that support customer experiences and financial empowerment.

The position combines independent, hands-on technical work with technical leadership and mentoring. The engineer will design and build scalable, resilient, and operationally efficient data pipelines and platforms; establish and enforce reusable engineering patterns; and communicate technical concepts and outcomes to stakeholders. The role uses technologies including Python, Spark, relational and NoSQL databases, 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 count.
• At least 4 years of distributed data experience.
• 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+ years of data engineering experience.
• 4+ years of data modeling experience.
• 9+ years of application development experience with demonstrated proficiency in Python, SQL, Scala, or Java.
• 5+ 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+ years of experience building or supporting distributed data or compute workloads using tools such as EMR, Spark, Glue, or Databricks.
• 5+ years of experience designing, implementing, and operating real-time or streaming data pipelines.
• 3+ years of experience with data observability tools such as Monte Carlo or Splunk, or data orchestration tools such as Airflow or Dagster.
• 5+ years of experience working with unstructured or semistructured data using NoSQL databases such as MongoDB, Cassandra, or DynamoDB.
• 5+ years of experience designing and supporting data warehousing solutions such as Snowflake or Redshift.
• 3+ years of experience working in an Agile development environment.
• 3+ 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 teams 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 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.
• Independently design, build, and deliver cloud data solutions and applications with little or no support from supervisors or managers.
• 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 to internal and external stakeholders to drive alignment and shared understanding.
• Design and build scalable, resilient, and operationally efficient data pipelines and platforms.
• Balance hands-on technical contributions and innovation with mentoring and developing peers and junior engineers.
• Lead large-scale data initiatives from end to end, drive architectural decisions, and evaluate platform options such as Snowflake and 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