Data Engineer 5 (Senior Manager, IC)

Capital One • McLean, NY

9d

About The Position

Capital One’s Enterprise Data Tech (EDT) manages data technology, platforms, and experiences across the company. Its mission is to make data easy to find, use, understand, govern, and protect throughout the data lifecycle, with a focus on evolving capabilities for AI and expanding Capital One’s data platforms.

The Data Engineer 5 (Senior Manager, IC) independently designs, builds, and delivers cloud data solutions and applications, while contributing hands-on technical expertise and mentoring others. The role leads large-scale data initiatives, makes architectural and platform decisions, and develops scalable, resilient, operationally efficient data pipelines and platforms. It also promotes common engineering patterns, code quality, and alignment among technical teams and stakeholders. The position collaborates with Agile teams, product managers, software engineers, data analysts, and data scientists to create cloud-first data solutions.

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 apply.
• 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 or more years of data engineering experience.
• 5 or more years of hands-on experience designing, deploying, and operating data workloads in AWS.
• 5 or more years building or supporting distributed data or compute workloads using tools such as EMR, Spark, Glue, or Databricks.
• 5 or more years designing, implementing, and operating real-time or streaming data pipelines.
• 5 or more years working with unstructured or semistructured data using NoSQL databases such as MongoDB, Cassandra, or DynamoDB.
• 5 or more years designing and supporting data warehousing solutions such as Snowflake or Redshift.
• 4 or more years of data modeling experience.
• 3 or more years working in an Agile development environment.
• 3 or more years developing user-centric reusable data products.
• 3 or more years working with data observability tools such as Monte Carlo or Splunk, or data orchestration tools such as Airflow or Dagster.

Responsibilities

• Collaborate across Agile teams to design, develop, test, implement, and support technical solutions using full-stack development tools and technologies.
• Influence a team 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 including Databricks and Snowflake.
• Keep up 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 supervisory support.
• Architect and enforce common data engineering design patterns to promote code quality, maintainability, and reusability across platforms and pipelines.
• Communicate technical concepts and data outcomes to internal and external stakeholders to build alignment and shared understanding.
• Design and build scalable, resilient, operationally efficient data pipelines and platforms that perform reliably as data volumes and business demands grow.
• Balance hands-on technical contributions and innovation with mentoring and developing peers and junior engineers.
• Lead large-scale data initiatives end to end, independently making architectural decisions and evaluating platforms such as Snowflake and Databricks against technical and business requirements.

Benefits

• Comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being.
• Eligibility for 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