Data Analytics Engineer – Automation

Role Overview

• 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.