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The Pricing & Promotion Strategy Analyst is the analytical engine that enables the Pricing & Promotional Strategy Partner team to execute Meijer's Elevate strategy at the category level. This role owns the data assembly, integration, and analytical outputs that Strategy Partners depend on to lead category deep dives, develop Golden Rules, co-own promotional calendars, and influence merchant decisions across Fresh, Grocery, HBC, and Hardlines/Softlines.
The role maintains the Price Architecture framework, tracking AUR performance against tier and profile targets, and supports promotional effectiveness analysis. Success requires data proficiency, the ability to work with high-volume multi-source retail data, and a consistent commitment to data accuracy and timeliness-so Strategy Partners can action on data insight.

What You'll Be Doing:

• Own the deep dive data framework: assemble, merge, and clean item-level pricing history, promotional events, competitive observations, basket penetration, and vendor funding by SKU for assigned category waves.
• Maintain structured data that Strategy Partners use to enter each deep dive, ensuring inputs are current, validated, and item-level before analysis begins.
• Identify and resolve upstream quality issues-mismatched SKU mappings, missing competitive observations, duplicate promotional records-before they surface in merchant-facing outputs.
• Maintain data pipelines across multiple parallel category waves at different stages of the deep dive process.
• Synthesize item-level pricing, promotional history, competitive positioning, basket penetration, and vendor funding into a complete category picture before any recommendation is made.
• Own and maintain the Price Architecture Framework (Product Tier Profile Lever) for assigned categories as the single source of truth feeding pricing and promo systems, keeping assignments current through annual review and flagging material shifts to Strategy Partners.
• Support KVI list expansion and ongoing tier/lever decisions with data analysis on basket penetration, competitive overlap, and price elasticity.
• Partner with Strategy Partners to design, run, and interpret tests validating Elevate assumptions on Archetype IV categories, reporting recommendations to merchant teams.
• Partner with Pricing Execution to monitor actual AUR against Balance Matrix Profile/Tier targets, advise on adjustments, and feed results into the annual Balance Matrix review.
• Deliver timely, validated analysis and scenario models for Strategy Partners' merchant meetings, pricing events, and AUR gap escalations, quantifying financial impact of promo, base price, or vendor funding changes.
• Build and maintain reusable analytical templates (category workbooks, AUR dashboards, promo summaries) to speed up each new category wave.

What You'll Bring With You:

• Bachelor's degree in Business, Economics, Finance, Statistics, Mathematics, or a related quantitative field.
• 2-4 years of experience in pricing analytics, promotional analytics, retail data analysis, or a related role; experience within a retail pricing, category management, or merchandising analytics function preferred.
• Proficiency in SQL for querying large transactional datasets; advanced Excel; experience with BI or visualization tools such as Power BI or Tableau.
• Ability to work with high-volume, multi-source retail data-identifying quality issues, building reproducible data pipelines, and delivering clean outputs on recurring schedules.
• Working knowledge of retail pricing concepts such as AUR, promotional lift, elasticity, and margin, and how data connects to pricing and promotional decisions.
• Ability to structure and communicate analytical outputs clearly for Strategy Partner and merchant audiences.
• Proficient in statistical analysis software (e.g., SAS, SPSS, Stata, R, Python) required.
• Experience working directly alongside pricing strategists, category managers, or merchant/buying teams in a retail environment.
• Exposure to ML/AI-enabled promotional effectiveness modeling or price elasticity modeling; experience preparing input datasets for data science teams.
• Experience with competitive intelligence data sources (e.g., Nielsen, IRI, competitive web data, or equivalent) and integrating external competitive pricing feeds into internal tracking.