Senior Staff Data Engineer — Remote-Eligible Cloud Platforms

Capital One; Trenton (NJ); À distance; USD 286 000 - 327 000; Plein temps

Résumé du poste

Capital One is seeking a Senior Staff Data Engineer (Remote-Eligible) to lead and influence the data engineering practice. You will own end‑to‑end data solutions, drive platform strategy, and mentor engineers while collaborating with data scientists, product managers, and analytics leaders.

You will specialize in AWS, Lakehouse architectures, and real‑time data processing with Spark, Flink, Kafka, Snowflake, and Databricks.

Qualifications

• Bachelor’s degree in Computer Science or a related quantitative field.
• At least 9 years of experience in data engineering.
• At least 5 years of experience in data architecture.
• At least 3 years of experience building applications in AWS.
• At least 7 years of experience programming in Python, Java, or Scala.
• At least 6 years of experience designing and developing data pipelines.
• At least 4 years of experience in data modeling and designing end‑to‑end data solutions using relational and NoSQL databases.

Responsabilités

• Lead the end‑to‑end design and coding of critical data projects.
• Communicate with stakeholders across the enterprise and influence architectural decisions.
• Mentor engineers and recruit to grow Capital One Tech talent.
• Drive strategy for data platforms, pipelines, and data sharing capabilities.
• Develop and socialize data governance policies and architecture blueprints.

Connaissances

Python

Java

Scala

Data engineering

Formation

Bachelor's degree in CS or related

Master's degree (preferred)

Outils

AWS

Kafka

Flink

Spark

Snowflake

Databricks

SQL

Description du poste

Capital One is seeking a Senior Staff Data Engineer (Remote-Eligible) to lead and influence the data engineering practice. You will own end‑to‑end data solutions, drive platform strategy, and mentor engineers while collaborating with data scientists, product managers, and analytics leaders.

You will specialize in AWS, Lakehouse architectures, and real‑time data processing with Spark, Flink, Kafka, Snowflake, and Databricks.