Data Engineer ; Senior Manager, IC-Risk Tech

This role sits within Capital One's Risk Tech organization, where data engineering teams build and deploy AI-powered risk management solutions  will lead end-to-end, large-scale engineering initiatives, combining full-stack data platform development with scalable pipeline design, platform delivery, and technical mentorship. The position is based in McLean, VA with onsite expectations.

• Partner across Agile teams to design, build, test, implement, and support full-stack technical solutions.
• Shape technical outcomes by influencing a team of developers, data analysts, and data scientists with strong experience across machine learning, distributed microservices, lakehouse architecture, and full-stack systems.
• Use Python and Spark alongside open-source relational and No
  
  SQL databases and cloud data warehousing platforms such as Databricks and Snowflake
  .
• Collaborate with product managers and software engineers to deliver cloud-first data solutions that support experiences for millions of Americans.
• Independently design, build, and deliver cloud data solutions and applications with little or no support from supervisors or managers.
• Architect and enforce common data engineering design patterns to improve code quality
  , maintainability
  , and reusability across platforms and pipelines.
• Build data pipelines and platforms with a focus on scalability
  , resilience
  , and operational efficiency
  , maintaining performance as data volume and business demand grow.
• Serve as a data engineering ambassador, explaining technical concepts and data outcomes clearly to internal and external stakeholders to drive alignment.
• Lead and execute large-scale, transformative data initiatives end to end, including critical architectural decisions such as evaluating Snowflake versus Databricks based on technical and business requirements.
• Act as a force multiplier by balancing hands-on innovation with mentoring and elevating the skills of peers and junior engineers.

• Languages: Python, SQL, Java, Scala
• Data & platforms: Databricks, Snowflake, EMR, Glue, Airflow, Dagster
• Observability & tooling: Monte Carlo, Splunk
• Cloud environments: AWS, Microsoft Azure, Google Cloud
• No
  
  SQL & databases: MongoDB, Cassandra, DynamoDB, Redshift

• Bachelor’s degree or higher in Computer Science or a related quantitative field:
  Statistics, Economics, Operations Research, Analytics, Mathematics, or Engineering
• 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 with at least one of:
  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 both relational and non-relational database systems

• Master’s degree in Computer Science or a 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 of 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 tools (e.g., Airflow, Dagster)
• 5+ years of experience working with unstructured or semi-structured data using No
  
  SQL 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

• McLean, VA (onsite): $229,900 - $262,400 per year
• Richmond, VA: $209,000 - $238,500 per year

• Performance-based incentive compensation, which may include cash bonus(es) and/or long-term incentives (LTI)
• Comprehensive, competitive, and inclusive set of health, financial, and other benefits supporting overall well-being