About the Role
Join the Supply Chain AI Hub as a Senior Data Engineer helping turn AI ambition into reliable data foundations and delivery-ready assets. This role helps engage business, engineering and ICT stakeholders around practical data needs and constraints, scale AI delivery through stronger data models, pipelines, integration pathways, quality routines and traceability, and pioneer more robust data-engineering practices that make solutions easier to trust, operate and industrialize.
Your Missions:
Data Modelling, Pipelines & Reuse:
- Design, improve or govern selected data models, transformation logic and pipeline components that support AI and analytics use cases
- Promote maintainable structures, reusable components and clear lineage across transformations where relevant
- Support delivery teams with practical data-engineering discipline rather than one-off technical builds
Platform, Integration & Traceability:
- Clarify selected source-to-platform pathways, integration dependencies and technical constraints affecting delivery
- Help maintain visibility on traceability, handoffs and access conditions across Supply Chain
- Work with ICT and engineering stakeholders to keep the build path practical and scalable
Data Quality, Certification & Governance Support:
- Contribute to selected quality checks, certification routines, governance expectations or compliance-related traceability needs depending on the scope assigned
- Help surface structural data issues, documentation gaps or control weaknesses that affect deployment readiness
- Support a trusted delivery environment by making data assets more visible, understandable and supportable
Your Profile:
- Strong data-engineering experience in modern enterprise environments, with depth in some combination of data modelling, pipelines, integration, quality, lineage or governance-related topics
- Able to operate across business needs, technical constraints and delivery realities
- Strong SQL and practical understanding of data structures, transformations, traceability and controlled delivery environments
- Comfortable working with multiple stakeholders across architecture, data, engineering and governance topics
- Structured, pragmatic and able to take ownership of a defined subset of a broader senior data-engineering scope
Skills You'll Grow:
- Broader exposure across the different building blocks that make AI-ready data operational at scale
- Experience working at the intersection of data engineering, integration, quality and delivery governance
- Opportunity to deepen expertise in a specific component while contributing to a wider AI data foundation agenda
Why Join / Impact:
- Work on data-engineering challenges directly tied to real AI deployment in Supply Chain
- Join a role broad enough to offer variety, while still allowing focused ownership on a defined perimeter
- Help strengthen the data foundations that make scalable AI delivery possible
Preferred Qualifications
Basic Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, Engineering, Mathematics, or related field
- 8 years of experience in data engineering or data platforms
- Previous Supply Chain experience
- Hands-on experience with modern data platforms such as Databricks, Spark, Snowflake, or equivalent
- Experience with data pipelines, integration, semantic, lineage, architecture and platform environments
- Enterprise-scale data transformation and delivery experience
- Ability to collaborate effectively with analytics, AI, and software engineering teams
Skills Required
- Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, Engineering, Mathematics, or related field
- 8 years of experience in data engineering or data platforms
- Previous Supply Chain experience
- Hands-on experience with modern data platforms such as Databricks, Spark, Snowflake, or equivalent
- Strong SQL and practical understanding of data structures, transformations, traceability and controlled delivery environments
- Experience with data pipelines, integration, semantic models, lineage, architecture and platform environments
- Enterprise-scale data transformation and delivery experience
- Ability to collaborate effectively with analytics, AI, and software engineering teams
Stellantis Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Stellantis and has not been reviewed or approved by Stellantis.
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Pay Growth & Progression — Contract-driven increases lifted hourly wages roughly 25% over 4.5 years and restored cost-of-living adjustments, pushing top rates near $42 per hour by the end of the agreement. Union hourly positions appear to have benefited most since the 2023 deal.
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Affordable Benefits — UAW-represented hourly workers pay no premiums and about 3% of total healthcare costs while receiving comprehensive medical, dental, vision, and wellness coverage. This creates materially lower out-of-pocket costs for represented hourly roles.
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Retirement Support — Post-2007 hourly hires receive a 10% employer 401(k) contribution and legacy workers saw defined-benefit improvements with retiree bonuses. Salaried roles also cite a 401(k) with employer match and contribution up to a maximum of 8%.
Stellantis Insights
What We Do
Our storied and iconic brands embody the passion of their visionary founders and today’s customers in their innovative products and services: they include Abarth, Alfa Romeo, Chrysler, Citroën, Dodge, DS Automobiles, Fiat, Jeep®, Lancia, Maserati, Opel, Peugeot, Ram, Vauxhall and mobility brands Free2move and Leasys. Powered by our diversity, we lead the way the world moves – aspiring to become the greatest sustainable mobility tech company, not the biggest, while creating added value for all stakeholders as well as the communities in which we operate.
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