Senior Data Engineer, Cloud Data Pipelines

Capital One; Plano (TX); On-site; USD 209,000 - 239,000; Full time

Job summary

Capital One is seeking a Data Engineer to design and deliver scalable cloud-native data solutions. You will collaborate with cross-functional teams and mentor peers while driving hard technical decisions and platform choices across data platforms.

The role emphasizes building robust pipelines, modeling data, and leveraging Python/SQL in a modern data stack to empower financial services on a large scale.

Qualifications

• Bachelor's Degree or higher in Computer Science or related quantitative field.
• At least 6 years of experience in application development (internship not counted).
• At least 4 years of experience in distributed data.
• At least 4 years of experience with SQL.
• At least 4 years of experience programming in 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 end-to-end data solutions.

Responsibilities

• Collaborate with Agile teams to design, develop, test, and support technical solutions.
• Influence a team of developers, data analysts and data scientists on data projects.
• Utilize Python, Spark and cloud data warehousing platforms like Databricks and Snowflake.
• Mentor other members of the data community and stay current with data trends.
• Deliver cloud-first data solutions enabling millions of Americans to achieve financial empowerment.
• Design and deliver scalable, maintainable data pipelines and platforms.

Skills

Python

SQL

Spark

Data pipelines

Cloud data platforms

Education

Bachelor's degree or higher in CS or related field

Master's degree

Tools

Databricks

Snowflake

Airflow

EMR

Job description

Capital One is seeking a Data Engineer to design and deliver scalable cloud-native data solutions. You will collaborate with cross-functional teams and mentor peers while driving hard technical decisions and platform choices across data platforms.

The role emphasizes building robust pipelines, modeling data, and leveraging Python/SQL in a modern data stack to empower financial services on a large scale.