Senior Data Engineer, Ingestion Framework - Data & AI Platform

Rivian and Volkswagen Group Technologies•Vancouver, BC

2d•CA$96,000 - CA$128,000

About The Position

As a Senior Data Engineer in the Data & AI Platform team, you will play a pivotal role in enabling our big data platform to operate seamlessly at a petabyte scale. The successful candidate will be an architect and custodian of a custom-built, Go-powered vehicle data processing framework, ensuring its smooth integration with our Databricks platform.

Requirements

Bachelor's or Master's degree in Computer Science, Data Science, or a related field.
5+ years of hands-on experience in data engineering or a similar role, with a proven track record of building and maintaining large-scale data processing systems.
Deep proficiency in Go programming language, with a strong understanding of its concurrency model and best practices for building high-performance applications.
Solid understanding of big data ecosystems, such as Spark and Databricks. Experience with data warehousing and data lake concepts is a plus.
Exceptional analytical and problem-solving skills, with the ability to break down complex problems into manageable components and develop effective solutions.
Excellent communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams.

Nice To Haves

Experience with Kubernetes container orchestration, deployment strategies, and cluster management.
Familiarity interacting with open source or Databricks proprietary Delta tables. Understanding of Delta protocol is an extra bonus.

Responsibilities

Design, develop, and enhance a cutting-edge, petabyte-scale vehicle data processing framework using Go. This framework is the backbone of our data operations; therefore, the ability to write clean, efficient, and maintainable code is essential.
Collaborate closely with the big data platform team to ensure seamless integration of the framework with our Databricks environment. This involves understanding the intricacies of both systems and optimizing their interaction for maximum efficiency.
Take ownership of the framework's deployment lifecycle. Implement and manage robust CI/CD pipelines on Kubernetes to ensure smooth, automated, and reliable deployments.
Continuously monitor the framework's performance, identify bottlenecks, and implement optimizations to ensure it operates at peak efficiency even as data volumes grow.
Proactively identify and resolve issues that may arise with the framework. The ability to diagnose problems quickly and implement effective solutions will be critical to maintaining system uptime.
Collaborate with data producers and consumers to deliver dependable and scalable solutions.
Keep abreast of the latest advancements in big data technologies, data engineering practices, and cloud computing. Explore new tools and techniques to improve our data infrastructure.