Associate Data Engineer

Grade Level (for internal use):

Job Title:Associate Data Engineer

The Team:
The Automotive Insights - Supply Chain and Technology and IMR department at S&P Global is dedicated to delivering critical intelligence and comprehensive analysis of the automotive industry's supply chain and technology. Our team provides actionable insights and data-driven solutions that empower clients to navigate the complexities of the automotive ecosystem, from manufacturing and logistics to technological innovations and market dynamics. We collaborate closely with industry stakeholders to ensure our research supports strategic decision-making and drives growth within the automotive sector. Join us to be at the forefront of transforming the automotive landscape with cutting-edge insights and expertise.

Responsibilities and Impact:

• Develop and maintain automated data pipelines to extract, transform, and load data from diverse online sources, ensuring high data quality.
• Build, optimize, and document web scraping tools using Python and related libraries to support ongoing research and analytics.
• Implement DevOps practices for deploying, monitoring, and maintaining machine learning workflows in production environments.
• Collaborate with data scientists and analysts to deliver reliable, well-structured data for analytics and modeling.
• Perform data quality checks, troubleshoot pipeline issues, and ensure alignment with internal taxonomies and standards.
• Stay current with advancements in data engineering, DevOps, and web scraping technologies, contributing to team knowledge and best practices.

What Were Looking For:

Basic Required Qualifications:

• Bachelors degree in computer science, Engineering, or a related field.
• 1 to 3 years of hands-on experience in data engineering, including web scraping and ETL pipeline development using Python.
• Proficiency with Python programming and libraries such as Pandas, BeautifulSoup, Selenium, or Scrapy.
• Exposure to implementing and maintaining DevOps workflows, including model deployment and monitoring.
• Familiarity with containerization technologies (e.g., Docker) and CI/CD pipelines for data and ML workflows.
• Familiarity with the cloud platforms (preferably AWS).

Key Soft Skills:

• Strong analytical and problem-solving skills, with attention to detail.
• Excellent communication and collaboration abilities for effective teamwork.
• Ability to work independently and manage multiple priorities.
• Curiosity and a proactive approach to learning and applying new technologies.