Data Engineer 4 (Manager, IC) (CaCe)
About The Position
Capital One is seeking a Data Engineer to help drive a major transformation by building cloud-first data solutions that combine data engineering with emerging technologies. The role collaborates across Agile teams and with product managers and software engineers to deliver robust data platforms and applications that support customer experiences and financial empowerment. The Data Engineer independently designs, builds, and delivers cloud data solutions, establishes reusable engineering patterns, and develops scalable, resilient, and operationally efficient data pipelines. The position also helps influence a team working across machine learning, distributed microservices, lakehouse architecture, and full-stack systems, while communicating technical concepts and outcomes to stakeholders and mentoring members of the data community. The role uses technologies such as Python, Spark, relational and NoSQL databases, Databricks, and Snowflake, and implements security controls including encryption and fine-grained access management. This is a hybrid position requiring adherence to Capital One’s policy for on-site presence at the designated office. The role is eligible for performance-based incentive compensation. Capital One does not provide new employment-authorization sponsorship or related immigration support for this position.
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 count.
- 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 a public cloud environment such as 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 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, relational and NoSQL databases, and cloud data warehousing platforms such as Databricks and Snowflake.
- Keep 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 that support code quality, maintainability, and reusability across platforms and pipelines.
- Communicate technical concepts and data outcomes clearly to internal and external stakeholders.
- Design and build scalable, resilient, and operationally efficient data pipelines and platforms.
- 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 health, financial, and other benefits supporting total well-being; eligibility varies by employment status and management level.
- Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
