Principal Supply Chain Data Engineer & Analytics Lead
New
J
JobgetherSupply Chain
Based in the United StatesFull-TimePrincipal
Salary$145,000–$165,000
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLMachine LearningMicrosoft Power BITableauData engineeringR
Requirements
- Bachelor’s degree in Supply Chain Management, Engineering, Data Science, Computer Science, or related field.
- 5+ years of progressive experience in supply chain analytics, data engineering, or business intelligence.
- Demonstrated ability to transform complex operational datasets into actionable insights and decision-support tools.
- Experience extracting, transforming, and validating data from ERP platforms and supply chain systems.
- Understanding of end-to-end supply chain processes (demand/supply planning, warehousing, distribution, transportation).
- Advanced proficiency with SQL, Python, R, Alteryx, or Power Query.
- Experience developing dashboards/visualizations using Power BI, Tableau, Qlik, or similar platforms.
- Knowledge of ERP/supply chain data structures, including master data, transactions, and logistics information.
- Experience developing predictive models, optimization models, or machine learning applications.
- Strong communication skills with the ability to explain complex data to technical and non-technical stakeholders.
- Strong organizational skills to manage competing priorities in a fast-paced environment.
Responsibilities
- Develop and maintain scalable supply chain data models, pipelines, and structures for reporting and analytics.
- Extract, transform, validate, and integrate data from ERP, MRP, DRP, WMS, WCS, and TMS systems.
- Design and deliver dashboards, scorecards, and visualization solutions to translate complex data into actionable insights.
- Develop predictive analytics, optimization models, machine learning applications, and AI-enabled decision-support tools.
- Quantify potential savings, operational impacts, and commercial trade-offs to support transformation initiatives.
- Partner with IT and data architecture teams to improve data accessibility, governance, and scalability.
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