Data Engineer

  • Intelletec
  • Jun 17, 2020
Permanent

Job Description

My client is a leading startup in the Autonomous Driving space. Based in the South Bay, they have raised more than $27m from some of the world's largest VCs and auto manufacturers.

They are democratizing the use of advanced driver assistance systems (ADAS) to reduce accidents, save lives, and make driving more enjoyable. They have created the most accurate and highest-performing AI solutions available today that meet car manufacturer requirements, at a much lower price than existing solutions.

They are seeking a Data Engineer to help our visionary team through building a robust pipeline and handling terabytes of data.

You will be designing and implementing full-stack solutions for computing, storing, and presenting evaluation metrics using data gathered from vehicles. This will be a unique opportunity to both improve and implement a scalable pipeline that will be the backbone of the company.

Responsibilities
Design, improve, and manage datasets
Support technical design and implementation of demand planning automation projects that use these datasets
Monitor and enforce data quality standards
Hands-on coding, able to implement what was designed
Familiar with architecting and designing tools, and self-motivated for documentation
Optimize pipeline performance to reduce data lag and refresh time

Requirements
3+ years of experience working in data architecture or a related field
3+ years of Python Experience
Web framework experience
Experience building and optimizing data sets
Deep understanding of data architecture best practices such as normalizing data structures, minimizing logic complexity, defining intuitive and consistent semantic layers, and eliminating redundancy
Self-motivated and strong work ethic for continuous development with a strong sense of ownership

Preferred Skills
Master's degree in Computer Science or Data Science or work equivalent experience
Experience with ROS
Web development experience
Hands-on experience in dev-ops best practices and implementations
Demonstrated industry efficiency in the fields of database, data warehousing or data sciences