Senior Data Engineer - Risk Tech & AI Pipelines

Capital One; McLean (VA); On-site; USD 197,000 - 225,000; Full time

Job summary

Capital One is hiring a Data Engineer 4 (Risk Tech) in McLean, VA. You will build and deploy data pipelines and cloud-first solutions, partnering with risk and product teams to enable AI-powered risk management and customer insights.

You’ll work across agile squads, shaping data architecture, and mentoring peers in ML and data engineering best practices. This role emphasizes scalability, security, and collaboration with stakeholders.

Qualifications

• Bachelor's degree or higher in a quantitative field (CS, Statistics, Economics, Analytics, Math, Eng).
• At least 4 years of application development experience.
• At least 2 years of distributed data experience.
• At least 2 years of SQL experience.
• At least 2 years of one programming language (Python, Java, or Scala).
• At least 2 years of data pipeline design and development.
• At least 1 year of end-to-end data modeling with relational and non-relational databases.

Responsibilities

• Collaborate with Agile teams to design, develop, test, and support data solutions.
• Influence teams with ML, microservices, lakehouse architecture, and full-stack systems.
• Utilize Python, Spark, SQL and cloud data warehousing like Databricks and Snowflake.
• Mentor data community members and stay current with new data technologies.
• Design scalable data pipelines and secure data access across platforms.

Skills

Python

SQL

Spark

Machine Learning

Cloud platforms

Education

Bachelor's degree in Computer Science or related quantitative field

Tools

Databricks

Snowflake

EMR

Airflow

Dagster

MongoDB

Cassandra

DynamoDB

Job description

Capital One is hiring a Data Engineer 4 (Risk Tech) in McLean, VA. You will build and deploy data pipelines and cloud-first solutions, partnering with risk and product teams to enable AI-powered risk management and customer insights.

You’ll work across agile squads, shaping data architecture, and mentoring peers in ML and data engineering best practices. This role emphasizes scalability, security, and collaboration with stakeholders.