Engage in data-related tasks within a defined scope, often involving known challenges in data acquisition, cleaning, processing, or basic modeling.
Contribute to specific data project deliverables, supporting senior team members in areas such as data pipeline development, data quality checks, model testing, or dashboard creation.
Follow established standards for data governance, data quality, metadata management, and analytical best practices in daily work.
Actively develop technical skills in data engineering, analytics, cloud platforms, and foundational data science methodologies, especially as they apply to powertrain data.
Implement components of data-driven solutions, such as scripts for data transformation, segments of automated data pipelines, or basic analytical dashboards, according to project plans and technical specifications.
Develop and apply proficiency in data engineering principles, data querying, programming for data analysis, and data visualization tools with an understanding of powertrain data sources and their relevance.
Support the development, testing, and maintenance of data pipelines and analytical models.
Analyze powertrain datasets to identify trends, anomalies, and patterns as directed.
Assist in the rollout of new data reports or tools and contribute to technical documentation related to data processes, pipelines, and analytical findings.
Requirements
Education: Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a similar related field.
Experience: 1-5+ years of relevant experience in data engineering, data analysis, or data science roles, with a focus on delivering data-driven insights and solutions.
Demonstrated hands-on experience with data processing, analysis, and visualization.
Experience within the automotive industry, particularly with powertrain data, is highly advantageous.
Hard Skills: Proficiency in Python for data analysis and automation. Familiarity with other programming languages (e.g., MATLAB/Simulink) is a plus.
Strong SQL skills for data extraction, manipulation, and analysis.
Understanding of ETL/ELT concepts, data modeling, and data warehousing.
Proficiency with tools like Tableau, PowerBI, or Qlik.
Awareness and practical experience with cloud platforms (e.g., Azure, AWS, GCP) and their data services.
Proven ability to analyze large datasets to inform engineering decisions.
Familiarity with version control systems (e.g., Git).
Tech Stack
AWS
Azure
Cloud
ETL
Google Cloud Platform
Python
SQL
Tableau
Benefits
Education: Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a similar related field.
Experience: 1-5+ years of relevant experience in data engineering, data analysis, or data science roles, with a focus on delivering data-driven insights and solutions.
Demonstrated hands-on experience with data processing, analysis, and visualization.
Experience within the automotive industry, particularly with powertrain data, is highly advantageous.
Programming & Scripting: Proficiency in Python for data analysis and automation. Familiarity with other programming languages (e.g., MATLAB/Simulink) is a plus.
Data Querying: Strong SQL skills for data extraction, manipulation, and analysis.
Data Engineering Fundamentals: Understanding of ETL/ELT concepts, data modeling, and data warehousing.
Data Visualization Tools: Proficiency with tools like Tableau, PowerBI, or Qlik.
Cloud Data Services: Awareness and practical experience with cloud platforms (e.g., Azure, AWS, GCP) and their data services.
Data Analysis: Proven ability to analyze large datasets to inform engineering decisions.
Version Control: Familiarity with version control systems (e.g., Git).
Communication: High-level verbal and written communication skills, with the ability to clearly document work and present technical findings.
Problem Solving: A diligent, passionate, and accountable approach to troubleshooting complex technical challenges.
Initiative & Adaptability: High self-learning skills with the ability to quickly master new technologies and processes with minimal supervision.
Teamwork & Collaboration: Excellent teamwork and interpersonal skills, with the ability to collaborate effectively in a global team environment.
Attention to Detail: Detail-oriented approach to ensure data quality and accuracy.