Drimva

Capital One

Data Engineer 5

$209K–$262K

McLean, VA +1 · Full-time · Posted Oct 6, 2026

Location
McLean, VA
Richmond, VA
Experience
6+ years application development; 4+ years each in distributed data, SQL, a listed programming language, and data pipeline design; 2+ years in data modeling and end-to-end solutions.
Education
Bachelor's Degree or higher in Computer Science or a related quantitative field; Master’s Degree preferred.
Visa sponsorship
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.

About the job

Lead the design and delivery of scalable cloud data platforms, pipelines, and AI-powered risk management solutions, while guiding engineering practices and mentoring peers.

What you'll do

  • Design, build, operate, and support scalable, resilient data pipelines and cloud data solutions.
  • Lead architectural decisions and large-scale data initiatives; establish reusable engineering patterns and communicate outcomes to stakeholders.
  • Collaborate across Agile teams and mentor engineers, analysts, and data scientists.

What they ask for

  • Bachelor's degree or higher in Computer Science or a related quantitative field.
  • At least 6 years of application development experience (internships do not count).
  • At least 4 years of experience in distributed data, SQL, a listed programming language (Python, Java, or Scala), and designing and developing data pipelines.
  • At least 2 years designing data models and end-to-end data solutions with relational and non-relational databases.

Nice to have

  • Master’s degree in Computer Science or a related field.
  • Experience in data engineering, public cloud, distributed workloads, streaming pipelines, data observability or orchestration, NoSQL, data warehousing, Agile, or reusable data products.

Pay

$229,900 - $262,400 USD base salary (McLean, VA); $209,000 - $238,500 USD base salary (Richmond, VA)

Tools

Python, Spark, SQL, Java, Scala, Databricks, Snowflake, AWS

AI focus

Build and deploy proprietary risk management solutions powered by AI; work with machine learning and AI-enhanced products.

Model-written summary · the original posting is authoritative

First seen Oct 28, 2026 · Last verified Nov 5, 2026 (UTC)