Data Engineer 4

Capital One • McLean, VA

6d • $179,400 - $225,100

About The Position

Capital One is seeking a Data Engineer to help drive a major transformation by combining data with emerging technologies. The role involves designing, building, and supporting cloud-first data solutions and applications that deliver customer experiences at scale. The engineer works across Agile teams and collaborates with product managers, software engineers, developers, data analysts, and data scientists.

The position independently delivers cloud data solutions, establishes and enforces reusable data engineering patterns, and builds scalable, resilient, and efficient data pipelines and platforms. It also involves communicating technical concepts and data outcomes to stakeholders, keeping current with data technology trends, participating in technology communities, and mentoring others. Data security responsibilities include applying encryption at rest and in transit, fine-grained access controls, and applicable privacy requirements.

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 at least one public cloud environment: 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 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 trends and technologies, experiment with new technologies, participate in internal and external technology communities, and mentor members of the data community.
• Collaborate with product managers and software engineers to deliver robust 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 that support code quality, maintainability, and reuse across 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 increase.
• 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, competitive, and inclusive health, financial, and other benefits supporting total well-being.
• Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives; incentives may be discretionary or nondiscretionary depending on the plan.

What This Job Offers

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