Data Engineer 4 - ERM Transformation

Capital One • McLean, VA

4d • $179,400 - $245,600

About The Position

Capital One is seeking a Data Engineer to help drive a major transformation in Risk Tech. The role works with the Governance, Risk, and Compliance (GRC) team and partners across the company to build and deploy proprietary risk-management solutions powered by AI. Risk Tech develops data-driven tools, including machine-learning-enabled solutions, to prevent risks and detect issues before they affect customers, the business, or communities.

The Data Engineer collaborates across Agile teams to deliver cloud-first data solutions and applications. The role includes independently designing, building, and delivering solutions; establishing engineering patterns; designing scalable and resilient data pipelines and platforms; and implementing data security controls. The engineer also communicates data outcomes and technical concepts to stakeholders, contributes to the data engineering community, and mentors colleagues.

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 apply.
• At least 2 years of experience in distributed data.
• At least 2 years of SQL experience.
• At least 2 years of experience with 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 with 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 Python and Spark, open-source relational and NoSQL databases, and cloud-based 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 supervisory support.
• Architect and enforce common data engineering design patterns to support code quality, maintainability, and reusability across data platforms and pipelines.
• Represent the data engineering team and clearly communicate technical concepts and data outcomes to internal and external stakeholders.
• Design and build scalable, resilient, and 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 controls, to support compliance with data privacy regulations.

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