Senior Data Engineer - Python, AWS, Spark, Snowflake

COMFORT SYSTEMS; New York (NY); On-site; USD 215,000 - 246,000; Full time

Job summary

Capital One is seeking a Data Engineer 4 to join the Marketing and Messaging data team. You will design, build, and scale cloud-based data solutions, collaborating with product managers and engineers to deliver robust data platforms.

The role emphasizes data engineering best practices, security, and performance in a cloud-first environment. The team works across Agile squads, mentoring others and driving lakehouse architectures using Python, Spark, Databricks, Snowflake, and AWS.

Qualifications

• Bachelor's degree in Computer Science or related quantitative field.
• At least 4 years of application development experience.
• At least 2 years of distributed data experience.
• At least 2 years of SQL experience.
• At least 2 years of one programming language (Python/Java/Scala).
• At least 2 years of data pipeline design and development.

Responsibilities

• Collaborate with Agile teams to design, develop, test, and support technical solutions.
• Influence a team of developers, data analysts and data scientists on ML, microservices, lakehouse architecture.
• Use Python and Spark with Databricks, Snowflake, and other data platforms.
• Mentor other data community members and share knowledge.
• Deliver cloud-first data solutions for scalable experiences for users.
• Architect and enforce reusable data engineering patterns across pipelines.
• Communicate data outcomes clearly to stakeholders.
• Design scalable, resilient data pipelines with strong performance.

Skills

Python

Spark

SQL

Kafka

Education

Bachelor's Degree in Computer Science or related field

Tools

Databricks

Snowflake

AWS

Job description

Capital One is seeking a Data Engineer 4 to join the Marketing and Messaging data team. You will design, build, and scale cloud-based data solutions, collaborating with product managers and engineers to deliver robust data platforms.

The role emphasizes data engineering best practices, security, and performance in a cloud-first environment. The team works across Agile squads, mentoring others and driving lakehouse architectures using Python, Spark, Databricks, Snowflake, and AWS.