Parts and Service Data Engineer / Data Scientist

Stellantis•Auburn Hills, MI

About The Position

Mopar is seeking a technically skilled and innovative Data Engineer / Data Scientist to drive advanced analytics and data solutions. This role is central to designing, building, and optimizing data pipelines, developing predictive models, and delivering actionable insights that empower HQ stakeholders. The ideal candidate will blend strong engineering skills with analytical expertise to support data-driven decision-making and operational excellence across the region.

Requirements

Bachelor’s degree in Computer Science, Data Engineering, Data Science, Information Systems, or a related field
5+ years of experience in data engineering, analytics, or data science (automotive industry experience preferred)
Proficiency in programming languages such as Python and SQL
Hands-on experience with ETL/ELT tools, data modeling, and cloud platforms (Snowflake, etc.)
Strong analytical thinking, problem-solving skills, and attention to detail
Excellent communication and presentation abilities, with a proven ability to explain complex technical concepts to diverse audiences
Ability to manage multiple priorities and deliver results in a fast-paced environment

Nice To Haves

Master's degree
Demonstrated experience designing and deploying business dashboards and data visualizations for large-scale automotive or after-sales operations
Advanced proficiency with business intelligence tools (e.g., Power BI, Tableau, Qlik) and experience integrating visualizations with cloud data platforms (e.g., Snowflake)
Familiarity with Mopar systems and performance metrics
Knowledge of machine learning, deep learning, and advanced analytics techniques
Certifications in cloud data engineering or analytics platforms

Responsibilities

Design, implement, and maintain robust data pipelines (ETL/ELT) to collect, process, and transform large-scale structured and unstructured datasets from diverse automotive sources.
Ensure data quality, integrity, and accessibility by developing automated validation and monitoring tools.
Optimize data workflows for performance, scalability, and reliability, supporting both batch and real-time analytics needs.
Collaborate with IT and analytics teams to integrate data from business systems into centralized data products.
Analyze complex datasets to uncover trends, patterns, and actionable insights that inform business strategies and operational improvements.
Build, train, and deploy predictive models and machine learning algorithms for applications such as performance forecasting, anomaly detection, and customer segmentation.
Conduct A/B testing, causal inference, and statistical analysis to evaluate business initiatives and drive continuous improvement.
Apply best practices in data visualization to ensure clarity, accuracy, and accessibility of insights, including interactive dashboards, automated reporting, and mobile-friendly solutions.
Serve as a technical liaison between HQ analytics and business teams, translating business needs into scalable data solutions.
Educate and mentor team members on data best practices, analytics tools, and emerging technologies.