Data Engineer

Autobrains, a leading AI Company in the Automotive Industry, is expanding! Following recent major design wins with leading global OEMs, we're scaling up our operations and innovation efforts. This is your chance to be part of a groundbreaking company at the forefront of automotive safety and intelligence.

We are looking for passionate individuals who want to make a real impact, working on state-of-the-art AI solutions that save lives and transform the driving experience. Join us in shaping the next generation of automotive technology.

Requirements:

BSc in EE or Computer Science, Industrial Engineer (or similar field) (MSc – advantage)
Proficiency in Python programming language.
Experience in heading up exceptionally large-scale business process operations.
Familiar with Data transfer methodologies design and operation
Strong project management skills: experience in working with cross-organizational teams- R&D, IT, Product, and project managers to achieve various project goals in an effective and timed manner.
Ability to handle multiple competing priorities in a fast-paced environment
Experience in big data, database techniques, tagging, and labeling - advantage.
Experience in Computer Vision products – advantage.
Experience in data architecture, especially from the automotive industry – big advantage.

Responsibilities:

The data engineer is responsible for defining the data ecosystem used by the R&D team at Autobrains.
Overseeing the company’s data pipeline system and processes including organizing, storing, labeling, and analyzing the data received, to ensure timely delivery of high-quality data required for the company's development projects.
Establish and implement an effective process for data management.
Monitoring data tasks and improving processes.
Develop Python scripts to automate repetitive data tasks, ensuring high efficiency in data handling.
Working closely with R&D and product management teams, algorithms, functional testing specialists, and data tagging professionals to ensure successful kickoff and execution of required projects including manpower planning, the correct definition of requirements, training materials preparation, and progress follow-up.
Provide quality data for training deep learning networks.
Collect and analyze data for the effectiveness of the projects, using scripts, python queries, and building dashboards