Data Engineer - Supply Chain

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

Requirements:

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)