Data Engineer 5 (Senior Manager, IC) (CaCe)
About The Position
Capital One is seeking a senior-level Data Engineer to help drive a major technology transformation by combining data engineering with emerging technologies. The role works across Agile teams to create and support cloud-first data solutions, applications, platforms, and pipelines that support customer experiences and financial empowerment. The engineer will independently deliver complex solutions, guide architectural decisions, establish reusable engineering patterns, and help ensure data platforms are scalable, resilient, maintainable, and operationally efficient. The position balances hands-on technical contribution with mentoring peers and junior engineers, while communicating technical concepts and outcomes to stakeholders. This is a hybrid position subject to Capital One's hybrid policy and required on-site presence at the designated office.
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 in 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 data engineering experience.
- 4 or more years of data modeling experience.
- 9 or more years of application development experience with demonstrated proficiency in Python, SQL, Scala, or Java.
- 5 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.
- 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.
- 3 or more years of experience with data observability tools such as Monte Carlo or Splunk, or data orchestration tools such as Airflow or Dagster.
- 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.
- 3 or more years of experience working in an Agile development environment.
- 3 or more years of experience developing user-centric reusable data products.
Responsibilities
- Collaborate with Agile teams to design, develop, test, implement, and support technical solutions.
- Influence a team of developers, data analysts, and data scientists working with machine learning, distributed microservices, lakehouse architecture, and full-stack systems.
- Use Python and Spark, open-source relational and NoSQL databases, and cloud-based data warehousing platforms such as Databricks and Snowflake.
- Keep current with data and technology trends, experiment with new technologies, participate in technology communities, and mentor members of the data community.
- Partner 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 improve code quality, maintainability, and reusability across data platforms and pipelines.
- Communicate technical concepts and data outcomes to internal and external stakeholders.
- Design and build scalable, resilient, and operationally efficient data pipelines and platforms.
- Contribute hands-on while mentoring and developing peers and junior engineers.
- Lead large-scale data initiatives end to end, make architectural decisions, and evaluate platform choices such as Snowflake versus Databricks against technical and business requirements.
Benefits
- Comprehensive health, financial, and other benefits supporting employee well-being.
- Eligibility to earn 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
