We are building an AI-enabled supply chain that senses, predicts, prescribes, and acts. The Data Engineer plays a critical role in enabling this vision by designing, building, and operating enterprise-grade data pipelines and analytical data products that power advanced analytics, optimization, automation, and agentic AI solutions across the Supply Chain organization.
This role focuses on production-ready data engineering-ensuring data is reliable, governed, scalable, and fit for decisioning. The Data Engineer partners closely with Data Science, AI Engineering, Automation, and Platform teams to deliver high-quality data assets embedded into operational workflows.
Responsibilities include but not limited to:
Design, build, and maintain scalable batch and near-real-time data pipelines supporting supply chain analytics and AI use cases
Ingest, transform, and curate data from enterprise and operational systems (ERP, planning, logistics, manufacturing, execution platforms)
Develop and maintain analytical data models and feature-ready datasets to support data science, optimization, and agentic AI workflows
Implement data quality validation, monitoring, and alerting to ensure trust and reliability of downstream analytics
Optimize data pipelines and storage for performance, cost, and scalability
Partner with data scientists and AI engineers to support model training, scoring, and deployment needs
Establish and follow best practices for data modeling, naming conventions, version control, and documentation
Ensure data solutions comply with enterprise standards for security, privacy, lineage, and governance
Support production operations, including incident investigation and root cause analysis related to data issues
Basic Qualifications:
Bachelor's in Computer Science, Information Systems, or a related field required
8+ years of professional experience in data engineering, analytics engineering, or data platform development
Strong proficiency in Python and SQL for data transformation and pipeline development
Experience designing and maintaining production-grade data pipelines and analytical data models
Hands-on experience with modern data platforms such as Databricks, Spark, Snowflake, or equivalent
Solid understanding of data quality, validation, and monitoring concepts
Experience working with structured and semi-structured data at scale
Proven ability to own production data pipelines end-to-end (design deployment monitoring incident response)
Demonstrated ability to operate independently, drive technical decisions, and deliver solutions in ambiguous environments with minimal oversight
Ability to collaborate effectively with analytics, AI, and software engineering teams
Preferred Qualifications:
Master's Degree
Experience supporting machine learning or advanced analytics pipelines, including feature engineering and model scoring data
Experience with orchestration tools, CI/CD, and version control for data pipelines
Familiarity with streaming or event-driven data architectures
Experience working with supply chain, operations, manufacturing, or ERP data
Knowledge of data governance, metadata management, and lineage tools
Experience supporting BI or downstream analytics tools (e.g., Power BI) and enterprise data platforms (e.g. Palantir Foundry)