Senior Data Engineer - Risk Tech & AI Solutions

Capital One; Richmond (VA); On-site; USD 179,000 - 205,000; Full time

Benefits offered by this job

Health benefits

Performance-based incentives

Job summary

Capital One is seeking a Data Engineer 4 in Risk Tech to drive data solutions at scale in a cloud-first environment. You’ll collaborate with cross-functional teams to design full-stack data platforms, mentor peers, and implement secure, scalable pipelines using Spark, Python and SQL across Databricks and Snowflake.

The role emphasizes hands-on delivery, innovation, and aligning data engineering with risk management using state-of-the-art AI tooling.

Qualifications

• Bachelor’s degree or higher in CS or a related quantitative field.
• At least 4 years in application development and 2+ years in distributed data.
• Strong skills in SQL and at least one of Python, Java, or Scala.
• Experience designing data pipelines and end-to-end data solutions.
• Experience with relational and non-relational databases and cloud data warehousing.

Responsibilities

• Collaborate with Agile teams to design, develop, test, and support technical solutions.
• Mentor developers, data analysts and data scientists in ML, microservices, and lakehouse architectures.
• Build data pipelines and cloud data solutions with Databricks and Snowflake.
• Ensure data security, encryption, and access controls across platforms.
• Deliver scalable, resilient data solutions and communicate outcomes to stakeholders.

Skills

Python

SQL

Java

Scala

Data pipelines

Education

Bachelor's Degree or higher in Computer Science or related quantitative field

Tools

Databricks

Snowflake

EMR

Spark

Glue

Airflow

Job description

Capital One is seeking a Data Engineer 4 in Risk Tech to drive data solutions at scale in a cloud-first environment. You’ll collaborate with cross-functional teams to design full-stack data platforms, mentor peers, and implement secure, scalable pipelines using Spark, Python and SQL across Databricks and Snowflake.

The role emphasizes hands-on delivery, innovation, and aligning data engineering with risk management using state-of-the-art AI tooling.