Remote Supply Chain Data Engineer & Analytics Lead

Bausch + Lomb Corporation; United States; On-site; USD 140,000 - 190,000; Full time

Job summary

Bausch + Lomb Corporation is seeking a Principal Supply Chain Data Engineer & Analytics Lead to drive data modeling, analytics capabilities, and insights across logistics, planning, and inventory processes. The role emphasizes remote work with full-time commitment.

Responsibilities include building scalable data pipelines, delivering dashboards, and translating complex data into actionable business insights to improve decision-making and operational performance.

Qualifications

• Bachelor's degree in a related discipline.
• 5+ years of experience in supply chain analytics, data engineering, or related fields.
• Proven ability to work with complex operational data and convert it into business insights.
• Experience with ERP systems and supply chain processes including demand and supply planning.
• Advanced skills in SQL, Python, or similar data analytics tools.

Responsibilities

• Develop and maintain supply chain data models and pipelines for consistent reporting and analytics.
• Extract, transform, and integrate data from various enterprise systems to create reliable analytical data sets.
• Design and deliver dashboards and visualization tools that translate complex data into actionable business insights.

Skills

Analytical thinking

Communication of insights

Problem-solving

Education

Bachelor's degree in supply chain management, Engineering, Data Science, or a related discipline

Tools

SQL

Python

ERP systems

BI dashboards

Job description

Bausch + Lomb Corporation is seeking a Principal Supply Chain Data Engineer & Analytics Lead to drive data modeling, analytics capabilities, and insights across logistics, planning, and inventory processes. The role emphasizes remote work with full-time commitment.

Responsibilities include building scalable data pipelines, delivering dashboards, and translating complex data into actionable business insights to improve decision-making and operational performance.