Senior Cloud Data Engineer — ML & Pipelines

Capital One; Riverwoods (IL); On-site; USD 179,000 - 205,000; Full time

Job summary

Capital One is seeking a data engineer to collaborate with Agile teams in designing, developing, testing, and supporting cloud-first data solutions, with emphasis on full-stack capabilities. The role involves mentoring colleagues and driving data strategy across ML, distributed microservices, and lakehouse architectures.

You will work with Python, Spark, and SQL across Databricks and Snowflake, building scalable pipelines and ensuring data security and compliance.

Qualifications

• Bachelor’s degree required in CS or related quantitative field.
• 4+ years in application development.
• 2+ years in distributed data.
• 2+ years in SQL.
• 2+ years in one programming language (Python/Java/Scala).
• 2+ years in data pipeline design and development.
• 1+ year in data modeling and end-to-end data solutions with relational and non-relational DBs.

Responsibilities

• Collaborate with Agile teams to design, develop, test, and support full-stack data solutions.
• Influence a team of developers, data analysts, and data scientists in ML and microservices.
• Build data pipelines and cloud-based data platforms focusing on scalability and performance.
• Implement data security standards including encryption and access control.
• Communicate data outcomes clearly to stakeholders and drive alignment.
• Mentor other data community members and stay updated on trends.
• Architect data patterns to ensure maintainability and reusability across pipelines.

Skills

Python

SQL

Java/Scala

Education

Bachelor's Degree in Computer Science or related quantitative field

Tools

Spark

Databricks

Snowflake

NoSQL Databases

EMR

Glue

Job description

Capital One is seeking a data engineer to collaborate with Agile teams in designing, developing, testing, and supporting cloud-first data solutions, with emphasis on full-stack capabilities. The role involves mentoring colleagues and driving data strategy across ML, distributed microservices, and lakehouse architectures.

You will work with Python, Spark, and SQL across Databricks and Snowflake, building scalable pipelines and ensuring data security and compliance.