Data Engineer 5

Capital One is hiring a Data Engineer 5 to help drive an enterprise transformation in data technology. The role centers on scalable, resilient, cloud-first data pipelines and platforms that support AI and the full data lifecycle, with ownership across architecture, delivery, and team impact.

Location

New York, NY (onsite)

Role Focus

Lead end-to-end initiatives for modern data engineering platforms and pipelines. Partner with Agile teams and cross-functional stakeholders to architect and deliver robust cloud-first solutions, mentor peers, and influence shared engineering standards across data systems.

Responsibilities

• Collaborate with and across Agile teams to design, develop, test, implement, and support technical solutions using full-stack development tools and technologies
• Influence developers, data analysts, and data scientists with deep experience in machine learning, distributed microservices, lakehouse architecture, and full-stack systems
• Use Python and Spark, along with open-source relational and NoSQL databases and cloud data warehousing platforms such as Databricks and Snowflake
• Stay current on data technology trends by experimenting with and learning new technologies; participate in internal and external technology communities; mentor members of the data engineering community
• Partner with product managers and software engineers to deliver robust cloud-first data solutions that support experiences for millions of Americans
• 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 support code quality, maintainability, and reusability across platforms and pipelines
• Design and build data pipelines and platforms with a focus on scalability, resilience, and operational efficiency to maintain performance as data volume and business demands increase
• Serve as a force-multiplier by balancing hands-on technical contribution and innovation with mentoring and skill elevation for peers and junior engineers
• Lead and execute large-scale, transformative data initiatives end to end, including critical architectural decisions and platform evaluation (for example, Snowflake versus Databricks) based on technical and business requirements
• Communicate technical concepts and data outcomes clearly to internal and external stakeholders to drive alignment
• Use strong engineering judgment to ensure end-to-end delivery of data solutions

Required Qualifications

• Bachelor's Degree or higher in Computer Science or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
• 6+ years of experience in application development (internship experience does not apply)
• 4+ years of experience in distributed data
• 4+ years of experience with SQL
• 4+ years programming with at least one of: Python, Java, or Scala
• 4+ years designing and developing data pipelines
• 2+ years of experience in data modeling and designing end-to-end data solutions using both relational and non-relational database systems

Preferred Qualifications

• Master’s Degree in Computer Science or a related field
• 8+ years of experience in data engineering
• 5+ years of hands-on experience designing, deploying, and operating data workloads in AWS
• 5+ years building or supporting distributed data or compute workloads using tools such as EMR, Spark, Glue, or Databricks
• 5+ years designing, implementing, and operating real-time or streaming data pipelines
• 5+ years working with unstructured or semistructured data using NoSQL databases (for example, MongoDB, Cassandra, DynamoDB)
• 5+ years designing and supporting data warehousing solutions (for example, Snowflake, Redshift)
• 4+ years of data modeling experience
• 3+ years of experience working in an Agile development environment
• 3+ years of experience developing user-centric reusable data products
• 3+ years of experience working on data observability (for example, Monte Carlo, Splunk) or data orchestration tools (for example, Airflow, Dagster)

Technologies

• Python, Spark
• Databricks, Snowflake
• SQL, EMR, Glue, AWS
• NoSQL: MongoDB, Cassandra, DynamoDB
• Redshift
• Airflow, Dagster
• Monte Carlo, Splunk
• Java, Scala

Compensation

USD 250,800 - 286,200 per year (New York, NY). This role may also be eligible for performance-based incentive compensation, which may include cash bonus(es) and/or long-term incentives (LTI).

Benefits

Comprehensive, competitive, and inclusive set of health, financial, and other benefits.

Education

Bachelor's Degree or higher in Computer Science or a related quantitative field.