Data Engineer 4 (Manager, IC)

Capital One • Richmond, VA

2d

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. The team’s platforms support Capital One’s data pipeline, including data storage and access, and are being evolved to meet the needs of AI and expand the company’s data platforms.

The Data Engineer designs, builds, and delivers cloud data solutions and applications, working with Agile teams, product managers, software engineers, developers, analysts, and data scientists. The role focuses on scalable, resilient, and operationally efficient data pipelines and platforms; data security and privacy; and reusable engineering patterns. The engineer communicates technical concepts and data outcomes to stakeholders, contributes to the data engineering community, and mentors others.

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 with Agile teams to design, develop, test, implement, and support technical solutions using full-stack development tools and technologies.
• Influence and work with developers, data analysts, and data scientists experienced in 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 such as Databricks and Snowflake.
• Stay 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 supervisory support.
• Architect and enforce common data engineering design patterns to support code quality, maintainability, and reusability across data platforms and pipelines.
• Communicate technical concepts and data outcomes clearly to internal and external stakeholders to promote 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.
• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior