Data Engineer 5

Capital One is hiring a Data Engineer 5 in McLean, VA (onsite) to design and deliver cloud-first data solutions and lead end-to-end large-scale initiatives.

Responsibilities

• Collaborate with and across Agile teams to design, develop, test, implement, and support technical solutions
• Guide a team of developers, data analysts, and data scientists with experience across machine learning, distributed microservices, lakehouse architecture, and full-stack systems
• Build with Python and Spark using open-source relational and NoSQL databases, plus cloud data warehousing platforms including Databricks and Snowflake
• Stay current with data trends; experiment with and learn new technologies; participate in internal and external technology communities; mentor the data engineering community
• Partner with product managers and software engineers to deliver robust cloud-first data solutions used by millions of Americans
• Independently design, build, and deliver cloud data solutions and applications with little or no support from supervisors or managers
• Architect and apply consistent data engineering design patterns to improve code quality, maintainability, and reusability across platforms and pipelines
• Design and build data pipelines and platforms for scalability, resilience, and operational efficiency under growing data volume and business demands
• Act as a force multiplier by balancing hands-on technical work, innovation, and mentoring to raise peer and junior engineer capabilities
• Lead end-to-end, large-scale transformative data initiatives by driving key architectural decisions and evaluating platform options (for example, Snowflake vs Databricks) against technical and business requirements
• Serve as an ambassador for data engineering by communicating technical concepts and data outcomes clearly to internal and external stakeholders

Requirements

• Bachelor’s Degree or higher in Computer Science or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
• 6+ years of experience in application development (internship experience does not apply)
• 4+ years of experience in distributed data
• 4+ years of experience with SQL
• 4+ years programming with at least one of: Python, Java, Scala
• 4+ years designing and developing data pipelines
• 2+ years in data modeling and designing end-to-end data solutions using both relational and non-relational databases

Technologies

• Python, Spark
• Databricks, Snowflake
• SQL, NoSQL, open-source relational databases
• Distributed microservices, lakehouse architecture
• Machine learning
• EMR, Glue, Airflow, Dagster
• Monte Carlo, Splunk
• AWS, Microsoft Azure, Google Cloud
• MongoDB, Cassandra, DynamoDB, Redshift
• Scala, Java

Preferred Qualifications

• Master’s Degree in Computer Science or related field
• 8+ years of experience in data engineering
• 4+ years of data modeling experience
• 9+ years of experience in application development with demonstrated proficiency in Python, SQL, Scala, or Java
• 5+ years hands-on experience designing, deploying, and operating data workloads in at least one public cloud environment (AWS, Microsoft Azure, or Google Cloud)
• 5+ years of experience building or supporting distributed data or compute workloads using tools such as EMR, Spark, Glue, or Databricks
• 5+ years of experience designing, implementing, and operating real-time or streaming data pipelines
• 3+ years of experience in data observability (e.g., Monte Carlo, Splunk) or data orchestration (e.g., Airflow, Dagster)
• 5+ years of experience with unstructured or semistructured data using NoSQL databases (e.g., MongoDB, Cassandra, DynamoDB)
• 5+ years of experience designing and supporting data warehousing solutions (e.g., Snowflake, Redshift)
• 3+ years of experience working in an Agile development environment
• 3+ years of experience developing user-centric reusable data products

Compensation

• USD 229,900 - 262,400 per year

Benefits

• Eligible for performance-based incentive compensation, potentially including cash bonuses and/or long-term incentives (LTI)
• Comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being