Senior Product Data Analyst - Supply Chain

Job Description Summary:

Digital data products are central to how the NAOU Supply Chain plans, executes, and continuously improves end‑to‑end performance across sourcing, manufacturing, transportation, and customer fulfillment. Our Supply Chain Capabilities COE 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 Senior 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 Engineers, Product Designers, and business stakeholders to translate complex supply chain data into trusted insights and scalable data products.

About the Role

Senior Product 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 analytics—turning complex, multi‑source operational data into clear insight, reliable reporting, and decision‑ready products.

Responsibilities

Supply 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 explain why it happened and what actions should 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 and maintain 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 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 engineers, and data scientists to align insight across domains

Tools & Technologies

This role will actively use and support data products built on:

• Microsoft Azure Synapse Analytics and Microsoft Fabric

• PySpark Notebooks for data exploration, transformation, and analysis

• Microsoft Azure Pipelines for analytics workflows and data product deployment

• SQL‑based analytics across large‑scale enterprise datasets

• Visualization and reporting tools such as Power BI