Nike Data Analyst Case Interview (Engineering): EMEA Footwear Launch Sell-Through Diagnosis

What to expect: a collaborative, panel-style case mirroring Nike’s consumer-first, cross‑functional culture. Many Nike analytics interviews include a SQL screen plus a data challenge or case presentation; this template reflects that reality. ([interviewquery.com](https://www.interviewquery.com/interview-guides/nike-data-analyst?utm_source=chatgpt.com), [glassdoor.com](https://www.glassdoor.com/Interview/NIKE-Senior-Data-Analyst-Interview-Questions-EI_IE1699.0%2C4_KO5%2C24.htm?utm_source=chatgpt.com), [datalemur.com](https://datalemur.com/blog/nike-sql-interview-questions?utm_source=chatgpt.com)) Case prompt: you’re the analyst for EMEA; sell‑through for a new Running silhouette underperforms on Nike.com and the Nike App during week 2. Using three tables (sessions, orders, inventory_positions) and a brief data dictionary, diagnose drivers and recommend actions across merchandising, allocation, and digital experience, highlighting regional nuances (EMEA vs North America), Member vs guest behavior, and launch seasonality. Format (60 minutes): 10‑min warm‑up on your approach and Nike values; 15‑min dataset brief and clarifying questions; 20‑min structured walkthrough of your analysis (whiteboard/sample SQL; simple charts); 15‑min Q&A with a cross‑functional panel (Digital Product, Marketplace/Merchandising, Ops Analytics). Optional variant some teams use: a take‑home dataset sent 2–7 days prior culminating in a 3–5 slide readout. ([glassdoor.com](https://www.glassdoor.com/Interview/NIKE-Senior-Data-Analyst-Interview-Questions-EI_IE1699.0%2C4_KO5%2C24.htm?utm_source=chatgpt.com)) Focus areas to hit: conversion funnel (view→add‑to‑cart→checkout→purchase), size availability and weeks of supply, sell‑through/ASP/markdown, delivery promise/OTIF signals, geo & channel mix (DTC vs wholesale context), and experiment ideas (e.g., PDP content, notification timing, Member targeting). Tools: write/describe SQL and simple visualizations; Tableau/Power BI accepted for take‑home variants; Nike commonly probes for SQL plus visualization experience (e.g., Tableau). ([interviewquery.com](https://www.interviewquery.com/interview-guides/nike-data-analyst?utm_source=chatgpt.com), [glassdoor.com](https://www.glassdoor.com/Interview/NIKE-Data-Analyst-Interview-Questions-EI_IE1699.0%2C4_KO5%2C17.htm?utm_source=chatgpt.com)) What interviewers evaluate: problem framing under ambiguity, data hygiene and assumptions, stakeholder storytelling, and team fit. ([glassdoor.com](https://www.glassdoor.com/Interview/NIKE-Senior-Data-Analyst-Interview-Questions-EI_IE1699.0%2C4_KO5%2C24.htm?utm_source=chatgpt.com))