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)
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