Senior Data Engineer Lead, Risk Tech (Onsite, McLean)

DataJobs

McLean (VA)

On-site

USD 230,000 - 262,000

Full time

7 days ago

Be an early applicant

Application generator

Turn this role into an interview — a resume and cover letter built around what this employer wants.

Get past ATS filters

Benefits offered by this job

Performance-based incentive

Comprehensive benefits

Job summary

DataJobs is recruiting for a senior data engineering lead within Capital One's Risk Tech organization in McLean, VA. You will lead end-to-end, large-scale engineering initiatives, building AI-powered risk management solutions and cloud data platforms.

You will collaborate with product managers, software engineers, data scientists to deliver scalable, cloud-first data solutions and mentor junior engineers. This role is onsite in McLean, VA, with opportunities to influence architecture and data

Qualifications

Bachelor’s degree or higher in Computer Science or a related quantitative field.
At least 6 years of application development experience.
At least 4 years of distributed data experience.
At least 4 years of SQL experience.
At least 4 years of programming with Python, Java, or Scala.
At least 4 years designing and developing data pipelines.
At least 2 years data modeling with relational and non-relational databases.

Responsibilities

Partner across Agile teams to design, build, test, implement, and support full-stack technical solutions.
Influence a team of developers, data analysts, and data scientists with ML, microservices, lakehouse, and full-stack systems.
Use Python and Spark with relational and NoSQL databases and cloud data warehousing such as Databricks and Snowflake.
Collaborate with product managers and software engineers to deliver cloud-first data solutions for millions of Americans.
Independently design, build, and deliver cloud data solutions with little supervision.
Architect and enforce common data engineering design patterns to improve code quality and maintainability.
Build data pipelines and platforms focusing on scalability, resilience, and operational efficiency.
Explain data outcomes to stakeholders to drive alignment.
Lead large-scale data initiatives end-to-end, including architectural decisions like Snowflake vs Databricks.
Mentor peers and elevate junior engineers.