Digital data products are central to how the North America Operating Unit (NAOU) Supply Chain plans, executes, and continuously improves end-to-end performance across sourcing, manufacturing, transportation, and customer fulfillment. Our Supply Chain Digital Enablement Team builds enterprise-scale data products that create visibility, decision intelligence, and measurable business outcomes for planning, operations, and leadership across NAOU.
We are hiring a
Lead Product Data Analyst to join our Supply Chain Digital Enablement Team. This role is embedded within empowered, cross-functional product teams and partners closely with Product Managers, Data Architects, Data Engineers, Product Designers, and business stakeholders to translate complex supply chain data into trusted insights and scalable data products.
This is a Director-level role.
About the RoleProduct Data Analysts are core contributors to the design, development, and adoption of supply chain data products. In this role, you will operate with a high degree of autonomy in complex, ambiguous problem spaces, helping teams understand operational performance, evaluate opportunities, and measure outcomes across the end-to-end supply chain.
You will lead sophisticated analyses, shape data models and metrics, and influence prioritization and trade-offs by grounding decisions in data. Beyond executing analysis, you will help elevate analytical standards across the organization by modeling strong analytical practice, strengthening measurement discipline, and enabling self-service insight for product teams and stakeholders.
This role is a strong fit for someone who thrives at the intersection of supply chain operations, data engineering, and advanced analytics-turning complex, multi-source operational data into clear insight, reliable reporting, and decision-ready products.
What You Will DoSupply Chain Analytics & Insight- Develop a deep understanding of end-to-end supply chain processes and data, including procurement, manufacturing, inventory, transportation, and customer fulfillment.
- Analyze large, complex datasets to identify performance drivers, risks, trade-offs, and improvement opportunities across planning and execution.
- Translate operational and optimization data into actionable insights that inform product strategy, roadmap decisions, and business priorities.
- Go beyond describing what happened to explainwhyit happened andwhat actionsshould follow.
- Proactively surface insights that help teams focus on the highest-value opportunities.
- Informally mentor analysts and partners through collaboration, review, and shared best practices.
Data Product Development & Measurement- Partner with Product Managers and stakeholders to define product objectives, success metrics, KPIs, and outcome-based measures.
- Design, maintain, and document analytical models, metrics, and business logic that support scalable data products and decision intelligence.
- Support movement from static reporting toward reusable, trusted data assets and analytical products.
- Advise on the appropriate level of analytical rigor based on decision risk, business value, and operational impact.
Data Quality, Modeling & Integration- Work closely with data architect and data engineering teams to define, validate, and evolve data models in Microsoft Azure.
- Ensure analytical outputs are accurate, reliable, and aligned to supply chain process realities.
- Act as a steward of data quality, metric definitions, and analytical assumptions across products.
- Document data logic, metrics, and methodologies to support transparency, reuse, and auditability.
Insight Communication & Influence- Create dashboards, analytical views, and narratives that make complex supply chain data accessible and actionable.
- Communicate insights clearly to technical and non-technical audiences, including supply chain leaders.
- Contribute to a data-informed culture by raising analytical fluency across supply chain product teams.
- Collaborate with other analysts, data architects, data engineers, and data scientists to align insight across domains.
Tools & TechnologiesThis role will actively use and support data products built on:
- Microsoft Azure Synapse Analytics and Microsoft Fabric.
- PySpark Notebooksfor data exploration, transformation, and analysis.
- Microsoft Azure Pipelinesfor analytics workflows and data product deployment.
- SQL-based analytics across large-scale enterprise datasets.
- Visualization and reporting tools such as Power BI.
What You Will NeedKey Qualifications- Bachelor's degree in analytics, statistics, economics, computer science, industrial engineering, supply chain, or a related quantitative field, or equivalent practical experience.
- 7+ years of experience in data analysis, analytics, or business intelligence supporting complex operational or supply chain domains.
- Advanced SQL querying and mastery of Python or another scripting language, including PySpark.
- Extensive experience working with supply chain data from systems such asSAP S/4HANA(e.g., procurement, inventory, planning, manufacturing, order-to-cash),Oracle Transportation Management (OTM), Manufacturing Execution Systems (MES),and other supply chain systems.
- Statistical methods and modeling techniques, with working knowledge of machine learning models.
- Data quality assessment, validation, and root cause investigation.
- Proven ability to influence decisions through data and clear communication.
- Strong collaboration skills and experience working within cross-functional product or platform teams.
- Ability to work independently and to foster a data-driven culture of growth.
Preferred Qualifications- Master's degree in a related quantitative or supply chain field.
- Experience supporting enterprise-scale supply chain data platforms or analytics products.
- Experience applying forecasting and optimization modeling to supply chain planning, execution, or operational decision-making.
- Knowledge of Azure and Microsoft Fabric storage and compute concepts that support scalable analytics products.
Skills- Analytical Rigor: Breaks down complex operational questions into clear, structured analyses using SQL, scripting, statistical methods, and root cause investigation.
- Supply Chain Acumen:Understands end-to-end supply chain processes and metrics.
- Product & Systems Thinking:Connects data insight to scalable data products and business outcomes.
- Data Storytelling:Communicates insights with clarity through visuals and narratives.
- Collaboration & Influence:Builds trust across supply chain, product, and technology partners.
