Principal Supply Chain Data Engineer & Analytics Lead

Bausch+Lomb Companies Inc.•USA - Remote, OR

9d•$145,000 - $165,000•Remote

About The Position

The Principal Supply Chain Data Engineer and Analytics Lead is a critical role responsible for building the data foundation, analytics capabilities, and insight-generation engine required to support Bausch + Lomb's supply chain transformation. This role will partner closely with to extract, connect, structure, analyze, and visualize complex supply chain data across enterprise systems. This role is designed for a highly analytical, business-oriented data professional who can operate across both technical and commercial dimensions. The individual will help convert large volumes of fragmented operational data into actionable insights, predictive analytics, and decision-support tools that improve service, cost, quality, inventory, productivity, and end-to-end supply chain performance. The successful candidate will bring strong data engineering and supply chain analytics capabilities, preferably with consulting experience or experience working in transformation-oriented environments. This role will support quantitative and commercial decision-making by developing reliable data pipelines, dashboards, analytical models, predictive tools, and AI-enabled insights that help identify opportunities, quantify value, and drive execution across the supply chain network. Success in this role will be measured by the ability to improve data availability, accelerate insight generation, strengthen forecasting and operational decision support, improve analytics quality, and enable measurable business outcomes across logistics, planning, warehousing, inventory, and customer fulfillment processes.

Requirements

Bachelor’s degree in supply chain management, Engineering, Data Science, Computer Science, Information Systems, Business Analytics, Operations Research, Finance, or a related discipline.
5+ years of progressive experience in supply chain analytics, data engineering, business intelligence, operations analytics, consulting, or a related field.
Demonstrated ability to work with complex operational data and convert large data sets into business insights, decision-support tools, and executable recommendations.
Experience extracting, transforming, validating, and analyzing data from ERP systems and other supply chain platforms.
Strong understanding of end-to-end supply chain processes, including demand planning, supply planning, MRP, DRP, warehousing, distribution, transportation, inventory, and customer fulfillment.
Strong analytical, quantitative, and problem-solving skills with the ability to conduct commercial and operational decision-making analysis.
Ability to partner effectively with various functions
Strong communication skills with the ability to explain complex data, models, assumptions, and insights to technical and non-technical audiences.
Ability to manage multiple priorities, work through ambiguity, and deliver high-quality analytics in a fast-paced transformation environment.

Nice To Haves

Advanced capability with SQL, Python, R, Alteryx, Power Query, or similar data extraction, transformation, and analytics tools.
Strong visualization and dashboard development experience using Power BI, Tableau, Qlik, or comparable business intelligence platforms.
Knowledge of ERP systems and supply chain data structures, including master data, transactions, inventory, orders, production, procurement, warehousing, and logistics data.
Experience building predictive models, forecasting tools, optimization models, simulation analyses, machine learning use cases, or AI-enabled decision-support solutions.
Ability to connect operational performance metrics with financial and commercial implications, including cost, margin, service, inventory, productivity, and working capital impacts.
Strong data quality, data governance, data validation, and data storytelling capabilities.
Experience documenting business requirements, data logic, metric definitions, assumptions, and model outputs for repeatable use across stakeholders.
Familiarity with Lean Six Sigma, structured problem solving, process mapping, or consulting-style analytics frameworks preferred.
Experience with a consulting firm, internal consulting group, transformation office, supply chain excellence team, or advanced analytics organization.
Experience in medical device, pharmaceutical, healthcare, consumer health, manufacturing, or other regulated supply chain environments.
Experience supporting logistics transformation, warehouse optimization, inventory analytics, planning improvement, network analysis, or service-cost-quality improvement initiatives.
Working knowledge of SAP, Oracle, Kinaxis, Blue Yonder, Manhattan, HighJump/Korber, SAP EWM, Dematic, or other ERP, planning, WMS, WCS, or supply chain execution platforms.
Certification or advanced training in data analytics, data engineering, supply chain management, Lean Six Sigma, APICS CPIM/CSCP, project management, or AI/ML preferred.

Responsibilities

Develop and maintain supply chain data models, pipelines, and data structures that enable consistent reporting, analytics, and decision support across logistics, planning, inventory, warehousing, and fulfillment processes.
Extract, transform, and integrate data from ERP, MRP, DRP, WMS, WCS, TMS, planning, finance, and other enterprise systems to create reliable analytical data sets.
Design and deliver dashboards, scorecards, visualization tools, and management reporting that translate complex data into clear business insights and recommended actions.
Analyze large, complex, and often fragmented data sets to identify patterns, root causes, risks, opportunities, and improvement levers across the end-to-end supply chain.
Develop predictive analytics, simulation tools, optimization models, and AI-enabled use cases that support service, cost, quality, inventory, and capacity decision-making.
Support opportunity funnel development by quantifying savings opportunities, operational impacts, customer service implications, and commercial trade-offs.
Create analytical tools that support decisions related to network performance, warehouse productivity, transportation cost, inventory positioning, demand and supply variability, and service performance.
Translate technical analysis into executive-ready insights, business cases, and recommendations that support transformation governance and leadership decision-making.
Collaborate with IT and data architecture teams to improve data accessibility, governance, automation, scalability, and long-term sustainability of analytics solutions.
Contribute to continuous improvement by identifying opportunities to simplify, standardize, automate, and scale supply chain analytics and reporting processes.