Data Analyst 3 - Supply Chain Planning

Nordstrom•Seattle, WA

1d•$102,500 - $170,500

About The Position

The Data Analyst 3 is a senior individual contributor on the Supply Chain Operations Planning analytics team, anchored in core planning analytics with a significant and growing focus on applied AI. On the applied AI side, you’ll be a key contributor to a live, LLM-powered platform for supply chain intelligence — building and maintaining the knowledge base, tool library, and quality controls that make the agent trustworthy, accurate, and genuinely useful to planners and leaders. This is hands-on applied-AI work that teams already rely on, not a research experiment and not a maintenance seat. On the analytics side, this role owns dashboards, data pipelines, variance analysis, and the underlying data models that power weekly and monthly planning cycles — and that foundational analytics work is inseparable from the AI platform: the domain expertise, SQL rigor, and data quality standards you build here are exactly what keeps the AI’s answers grounded in real operations. You’ll operate at both ends of the spectrum: building and maintaining the planning analytics that planners and leaders depend on every week, and helping build out the LLM-powered knowledge platform that turns validated business rules into trustworthy, self-serve answers. Strong SQL, Python, and data-modeling fundamentals are essential, along with genuine comfort using AI-assisted development tools as part of a daily workflow and practical experience working with LLM-based systems.

Requirements

Degree in Finance, Statistics, Mathematics, Analytics, Computer Science, or related field required
4+ years of data analytics experience; demonstrated ability to deliver business-critical analytics independently and raise analytical standards across a team
Hands-on experience with AI-assisted development and productivity tools (e.g., Claude Code, Claude Desktop) to accelerate analysis, automate data workflows, and produce higher-quality output faster
Practical experience working with LLM-based systems — prompt design, tool/function definitions, retrieval patterns (RAG), evaluation frameworks, or curating structured knowledge for AI consumption
Advanced SQL skills with hands-on BigQuery experience; strong understanding of data warehousing, dimensional modeling, and query construction
Demonstrated ability to build and maintain data pipelines and lightweight automation in Python, including validation, monitoring, and workflow scripts
Proficiency with at least one leading visualization tool (Looker preferred) and best practices for operational dashboarding
Strong operational business understanding, including the downstream effects of forecast accuracy, fulfillment decisions, and planning cadence on cost and service
Supply chain expertise in retail or consumer goods preferred, with familiarity in outbound fulfillment, inbound operations, inventory, returns, or capacity planning
Strong written and verbal communication skills; comfortable presenting findings to business stakeholders and operational leadership

Responsibilities

Build, maintain, and expand the AI knowledge base — authoring structured domain content (validated SQL, business rules, glossary definitions, workflow summaries) and curating the agent’s tool library so it answers planning questions accurately and confidently
Run and extend the evaluation and quality controls for the AI platform — writing test cases, running evals, and diagnosing hallucinations and retrieval gaps, then closing them with better content or tooling
Develop and maintain the Python tooling and automation that keeps the knowledge base current — freshness checks, extraction scripts, scheduled jobs that sync new data into the agent’s context
Propose and prototype new agent capabilities with the team — new planning domains, retrieval patterns, and tool definitions — and help validate and ship them
Own and maintain core planning analytics deliverables — BigQuery pipelines, Looker and Tableau dashboards, and the data models behind weekly and monthly Ops Planning cycles (WBR, MBR, forecast accuracy, variance reporting)
Build the reusable data models, query libraries, and SQL patterns that power self-serve analytics and provide the validated, authoritative source of truth the AI platform depends on
Execute and support ad hoc analysis to resolve planning risks and answer executive questions, from diagnosing forecast misses to sizing the impact of operational decisions
Identify and develop new measures and KPIs across outbound fulfillment, inbound receiving, inventory, returns, and capacity that improve planning visibility and decision quality
Support weekly business rhythms and the creation of key planning deliverables; partner with planners and business stakeholders to understand evolving analytical needs
Champion data quality and data governance within the Ops Planning team, coordinating with Data Engineering, Data Science, and Technology as needed