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.
