ICT Data Engineer

Stellantis•Auburn Hills, MI

17h

About The Position

We are seeking a strategic and hands-on Data Engineer to support Purchasing and Finance Analytics and Programs within our North America Data & AI team. Data engineering is the practice of making the appropriate data available to various data consumers (including data scientists, data and business analysts, citizen integrators, and line-of-business users). It is a discipline that involves collaboration across business and IT units. In addition to creating and maintaining an optimal pipeline architecture, typical duties and responsibilities for a Data Engineer position may include: The ideal candidate combines strong analytical skills with practical experience building scalable analytics, models, and data products in enterprise environments. You will be part of a talented team of data scientists, engineers, driving predictive analytics and early detection of emerging warranty trends using vast datasets across the enterprise.

Requirements

Bachelor’s or in Data Science, Statistics, Engineering, Computer Science, or related field.
Minimum 3 years' experience as Data Scientist, Advanced Analyst, or similar role
Strong proficiency in Python, SQL, PySpark and visualization tools (e.g., Power BI, Foundry Workshop).
Solid understanding of statistics, exploratory data analysis, and applied machine learning.
Experience working with large, complex datasets in enterprise environments
Ability to communicate analytical findings clearly to technical and non-technical audiences.
Proven experience delivering end-to-end analytics or data science solutions into production.
Experience with one or two data and cloud platforms (e.g., Palantir Foundry. Snowflake, Databricks AWS, Azure, GCP).
Strong communication and stakeholder engagement skills.

Nice To Haves

Familiarity with data modeling, semantic layers, and enterprise data platforms.
Industry experience in automotive and manufacturing
Exposure to MLOps concepts, model deployment, or monitoring
Hands-on experience with Palantir Foundry, Snowflake Intelligence
Master’s degree in Data Science, Statistics, Engineering, Computer Science, or related field.

Responsibilities

Assembling large, complex sets of data that meet non-functional and functional business requirements
Design, implement, and optimize end-to-end data pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data.
Develop robust ETL (Extract, Transform, Load) process to integrate data from various sources.
Identifying, designing and implementing internal process improvements including re-designing infrastructure for greater scalability, optimizing data delivery, and automating manual processes
Building required infrastructure for optimal extraction, transformation and loading of data from various data sources using AWS, Azure, DB2 and SQL technologies
Building scalable tables to provide actionable insight into key business performance metrics including operational efficiency and customer acquisition
Working with stakeholders including the Data Product teams to support their data infrastructure needs while assisting with data-related technical issues
Design and maintain data models, schemas, and database structures to support analytical and operational use cases.
Optimize data storage and retrieval mechanisms for performance and scalability.
Lead and coordinate cross-functional AI programs from concept to deployment, ensuring alignment with business goals and timelines.
Collaborate with other data scientists, engineers, and business stakeholders to define and prioritize program objectives.
Apply statistical analysis and machine learning techniques to solve business and operational problems.
Partner with business stakeholders to understand requirements and translate them into analytical solutions.
Translate business needs into actionable AI use cases and technical requirements
Build and deploy predictive models to forecast warranty claims, failure rates, and cost trends.
Ensure data quality, lineage, documentation, and compliance with governance requirements
Create dashboards and analytical outputs that drive insight adoption and operational impact
Collaborate with business data engineers, and platform teams on scalability, performance, and best practices