Data Engineer 5 (Cloud Operations Resilience Engineering) (MEG)

Capital One • Richmond, VA

1d

About The Position

Capital One is seeking a Data Engineer to help drive a major transformation by building cloud-first data solutions. The role collaborates across Agile teams and with product managers, software engineers, developers, data analysts, and data scientists to design, develop, test, implement, and support technical solutions. The engineer works with technologies including Python, Spark, relational and NoSQL databases, Databricks, Snowflake, and cloud-based data warehousing platforms.

The position independently designs, builds, and delivers cloud data solutions and applications, with a focus on scalable, resilient, operationally efficient data pipelines and platforms. It also establishes and enforces data engineering patterns, makes architectural and platform decisions, leads large-scale data initiatives, communicates technical concepts and outcomes to stakeholders, and mentors other engineers.

Requirements

• Bachelor's degree or higher in Computer Science or a related quantitative field.
• At least 6 years of experience in application development; internship experience does not apply.
• At least 4 years of experience in distributed data.
• At least 4 years of experience with SQL.
• 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 experience in data engineering.
• 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 across Agile teams to design, develop, test, implement, and support technical solutions.
• Influence and work with developers, data analysts, and data scientists across machine learning, distributed microservices, lakehouse architecture, and full-stack systems.
• Use programming languages such as Python and Spark, relational and NoSQL databases, and cloud-based 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 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 to support code quality, maintainability, and reusability across data platforms and pipelines.
• Communicate technical concepts and data outcomes clearly to internal and external stakeholders.
• Design and build scalable, resilient, and operationally efficient data pipelines and platforms.
• Contribute hands-on technical work and innovation while mentoring and developing peers and junior engineers.
• Lead large-scale data initiatives from end to end, make architectural decisions, and evaluate platform choices such as Snowflake versus Databricks.

Benefits

• Comprehensive health, financial, and other benefits supporting total well-being.
• Eligible 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