Data Engineer

Stellantis Financial Services; Auburn Hills (MI); On-site; USD 110,000 - 150,000; Full time

Job summary

Stellantis Financial Services in Auburn Hills, MI is seeking a hands-on Data Engineer to design and maintain reliable data pipelines and analytics-ready data products for marketing analytics and customer insights. The role collaborates with data scientists and analysts in a cloud-first environment, applying data quality practices and scalable modeling to deliver trusted data for decisioning.

Candidates should have strong SQL, ETL skills, and experience with Azure/Snowflake/BigQuery, Python, and

Qualifications

• Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or related field.
• 8+ years of experience in data engineering.
• Strong SQL development skills.
• Experience developing and supporting ETL/ELT pipelines.
• Experience with cloud-based data platforms and modern data architectures (Azure, Snowflake, BigQuery, AWS).
• Proficiency in Python, Spark, or similar data processing technologies.
• Experience with structured and semi-structured data.
• Understanding of data modeling concepts and data warehousing principles.
• Strong analytical and problem-solving skills.
• Ability to collaborate with both technical and business stakeholders.

Responsibilities

• Design, build, and maintain secure, scalable data pipelines for reporting, analytics, and machine learning initiatives.
• Develop and optimize ETL/ELT processes to ingest, transform, and deliver data from multiple sources.
• Support data integration across customer, marketing, digital, and enterprise data domains.
• Implement data quality checks, validation processes, and monitoring solutions.
• Troubleshoot and resolve data pipeline and data quality issues in production environments.
• Build and maintain scalable data models to support analytics and reporting.
• Develop curated datasets for consistent business metrics and KPI reporting.
• Collaborate with analysts and data scientists to support advanced analytics and audience development.
• Assist in implementing data structures that improve usability and reusability.

Skills

SQL development

ETL/ELT pipelines

Python

Spark

Data modeling

Cloud data platforms

Education

Bachelor's degree in Computer Science or related field

Tools

Azure

Snowflake

BigQuery

AWS

Job description

Role intent:

A hands-on individual contributor role focused on building and maintaining reliable data pipelines, curated datasets, and governed analytics-ready data products. This version intentionally removes senior-level expectations around owning architecture, setting standards, or leading platform strategy.

Position Summary:

The Customer Data Platform team is seeking a Data Engineer to help build and maintain trusted data products that power marketing performance, customer analytics, and advanced decisioning.

This role will work closely with data scientists, analysts, and business stakeholders to develop scalable data pipelines, support analytics initiatives, and ensure the availability of high-quality data across the organization.

The ideal candidate is a hands-on data engineering professional with strong technical skills, a passion for data quality, and experience developing data solutions in a modern cloud environment.

Key Responsibilities:

Data Engineering & Pipeline Development:

• Design, build, and maintain secure, scalable data pipelines that support reporting, analytics, and machine learning initiatives
• Develop and optimize ETL/ELT processes to ingest, transform, and deliver data from multiple sources
• Support data integration efforts across customer, marketing, digital, and enterprise data domains
• Implement data quality checks, validation processes, and monitoring solutions to ensure trusted data products
• Troubleshoot and resolve data pipeline and data quality issues in production environments

Data Modeling & Analytics Enablement:

• Build and maintain scalable data models that support analytics and reporting requirements
• Develop curated datasets that enable consistent business metrics and KPI reporting
• Collaborate with analysts and data scientists to support advanced analytics, audience development, customer insights, and performance measurement
• Assist in implementing data structures that improve usability, consistency, and reusability across teams

Collaboration & Delivery:

• Partner with business stakeholders to understand requirements and translate them into technical solutions
• Work closely with cross-functional teams to deliver reliable and scalable data products
• Participate in code reviews, testing, deployment activities, and continuous improvement initiatives
• Contribute to documentation, knowledge sharing, and engineering best practices

Data Governance & Reliability:

• Follow established standards for data governance, security, privacy, and compliance
• Monitor pipeline performance and recommend opportunities for optimization
• Support efforts to improve data quality, reliability, and operational efficiency
• Maintain documentation for data pipelines, data models, and technical processes

Basic Qualifications:

• Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or related field
• 8 years of experience in data engineering
• Strong SQL development skills
• Experience developing and supporting ETL/ELT pipelines
• Experience working with cloud-based data platforms and modern data architectures (e.g., Azure, Snowflake, BigQuery, AWS)
• Proficiency in Python, Spark, or similar data processing technologies
• Experience working with structured and semi-structured data
• Understanding of data modeling concepts, including dimensional modeling and data warehousing principles
• Strong analytical and problem-solving skills
• Ability to collaborate effectively with both technical and business stakeholders

Preferred Qualifications:

• Experience supporting marketing, customer, digital, or analytics use cases
• Experience with orchestration and workflow automation tools
• Familiarity with customer data platforms, customer analytics, or audience management solutions
• Experience with machine learning data preparation and feature engineering
• Understanding of data governance and data quality best practices