Automotive Vehicle QA Specialist

As an Automotive Vehicle QA Specialist at Partly, you will play a crucial role in our tech company, which provides software solutions in the automotive parts industry. Your primary responsibility will be to conduct comprehensive research, manage our vehicle data standard, and correct any errors or inconsistencies.

This role reports to the Data Transformation Lead and offers an exciting opportunity to contribute to a dynamic and innovative company in a rapidly evolving industry.

Responsibilities

• Research and analyze vehicle data from various sources, including manufacturer specifications, internal databases, and industry standards, to establish and maintain a comprehensive vehicle data standard.
• Identify and correct errors, inconsistencies, and inaccuracies in vehicle data, collaborating with cross-functional teams and external stakeholders to ensure data quality and integrity.
• Conduct thorough data validation and verification processes to ensure compliance with established standards, resolving any discrepancies or issues promptly.
• Collaborate with engineering and development teams to define and implement data quality assurance procedures and workflows, incorporating feedback from internal and external sources.
• Continuously monitor and evaluate the effectiveness of data management processes, recommending and implementing improvements to enhance efficiency, accuracy, and scalability.
• Stay up-to-date with industry trends, emerging technologies, and best practices related to vehicle data management and quality assurance, applying this knowledge to enhance our data practices.

What are we Looking for?

• Strong knowledge of automotive systems, components, and specifications, with a focus on vehicle data research and management.
• Familiarity with industry standards and regulations related to vehicle data, such as VIN decoding, vehicle taxonomy, and data exchange formats.
• Attention to detail and a systematic approach to data research and validation, ensuring accuracy and completeness.
• Strong problem-solving and analytical skills, with the ability to identify data inconsistencies, diagnose root causes, and propose effective solutions.
• Excellent communication and collaboration skills, enabling effective engagement with cross-functional teams and external stakeholders.
• Ability to prioritize tasks, manage multiple projects simultaneously, and meet deadlines in a fast-paced environment.