Remote Senior Staff Data Engineer — Platform & Pipelines

Capital One; United States; Remote; USD 315,000 - 392,000; Full time

Job summary

Capital One is seeking a Sr. Staff Data Engineer to lead and implement large-scale data pipelines and data sharing capabilities.

You will craft technical strategy, drive architectural decisions, and mentor engineers while delivering scalable, resilient data platforms. You will work across teams leveraging AWS, Lakehouse architectures, Kafka, Flink, Spark, Snowflake and Databricks, with hands-on coding in Python, SQL, Java or Scala.

Qualifications

• Bachelor’s Degree in a relevant field.
• 9+ years of data engineering experience.
• 5+ years of data architecture experience.
• 3+ years of experience building applications in AWS.
• 10+ years of data engineering preferred.
• 3+ years of data modeling experience.
• 2+ years of ontology standards for defining a domain.
• 2+ years of experience using Python, SQL or Scala.
• 1+ year of experience deploying machine learning models.
• 3+ years of experience implementing big data processing solutions on AWS.
• Experience leveraging interactive AI tooling to accelerate productivity.

Responsibilities

• Build awareness and promote modern technologies across the enterprise.
• Balance expertise with inclusive collaboration to grow skills.
• Encourage engineering excellence and reuse of solutions.
• Communicate with stakeholders at all levels of the organization.
• Act as trusted advisor for a technology domain and shape use cases.
• Mentor internal talent and recruit external talent to bolster Capital One’s tech.

Skills

Data engineering

Data architecture

Leadership

Mentoring

Education

Bachelor’s Degree

Master’s Degree

Tools

AWS

Kafka

Flink

Spark

Snowflake

Databricks

Python

SQL

Java/Scala

Job description

Capital One is seeking a Sr. Staff Data Engineer to lead and implement large-scale data pipelines and data sharing capabilities.

You will craft technical strategy, drive architectural decisions, and mentor engineers while delivering scalable, resilient data platforms. You will work across teams leveraging AWS, Lakehouse architectures, Kafka, Flink, Spark, Snowflake and Databricks, with hands-on coding in Python, SQL, Java or Scala.