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.
Key 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
Basic Qualifications- 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.
Preferred Qualifications - 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.
- This is a fast-paced environment providing rapid delivery for our business partners. You will be working in a highly collaborative environment that values speed and quality, with a strong desire to drive change and value.
