URBN Lead Data Analyst

Urban Outfitters•Philadelphia, PA

About The Position

URBN is seeking a Lead Data Analyst to provide hands-on technical leadership and serve as a trusted analytics partner across its brands and shared-service teams. The role initially focuses on Inventory Science, partnering with Supply Chain, Merchandising/Planning, Data Scientists, and ML Engineers to improve inventory and fulfillment decisions. Over time, the role may broaden to support analytics initiatives across URBN’s brands and Technology teams, including marketing, customer analytics, search, and personalization. The position combines advanced SQL and analytical expertise with curiosity about business operations. The successful candidate will investigate data across systems, trace findings to root causes, connect analysis to business outcomes, tackle ambiguous problems, identify opportunities, and help strengthen colleagues’ analytical skills. It is a technical leadership role with potential to grow into people leadership as the team expands. The initial Inventory Science focus includes supporting inventory and fulfillment optimization across URBN’s brands, such as purchase order splitting, retail store allocation, and order sourcing. The analyst will quantify opportunities and costs, define performance measures, evaluate optimization solutions, and translate operational and merchandising constraints into analytical requirements.

Requirements

At least 5 years of experience in analytics or quantitative roles, with demonstrated ownership of complex analyses that influenced business decisions.
Advanced SQL skills, including complex queries, performance optimization, and data validation.
Proficiency in Python for analysis, including pandas and standard analytical libraries.
Experience developing analytical datasets and data models in a modern data warehouse environment such as Snowflake, BigQuery, or Redshift.
Strong analytical problem-solving skills, including statistical analysis, identifying and mitigating potential biases, and independently navigating ambiguous business questions.
Experience working across source systems and collaborating with Data Engineering to investigate data quality issues.
Ability to provide technical guidance, mentor peers, and promote analytical best practices.
Strong communication skills and ability to translate complex findings for technical and business stakeholders.
Experience using AI-powered analytical or coding tools with appropriate validation and judgment.
Bachelor’s degree in a quantitative field or equivalent practical experience.

Nice To Haves

Experience with BI tools such as Qlik or Looker.
Experience with supply chain, inventory, fulfillment, marketing, or customer analytics.
Experience with experimental design, A/B testing, or causal inference.
Experience with version control and collaborative code review.
Familiarity with AI integrations and workflows, including MCP/connectors and agentic tools.
Experience in a multi-brand or omnichannel retail environment.

Responsibilities

Investigate data across source systems, validate accuracy, resolve discrepancies, and trace data issues to their root causes.
Build analytical datasets and data models for advanced analysis, experimentation, and data science initiatives.
Partner with Data Engineering to define analytical data requirements and address upstream data quality issues.
Develop measurement frameworks and design experiments, holdouts, and other approaches to evaluate business initiatives and data science solutions.
Develop dashboards and self-service reporting in Qlik and Looker, and document metrics, methodologies, and data lineage.
Lead complex analytical initiatives by translating ambiguous business questions into actionable insights and recommendations.
Partner with business and Technology stakeholders to identify opportunities, quantify potential impact, and define success metrics.
Collaborate with Data Scientists, ML Engineers, and business partners to frame problems, establish analytical requirements, and communicate findings.
Present complex analyses as clear data stories, including business implications and actionable recommendations.
Guide analytical approaches as a technical lead and ensure results are high-quality and defensible.
Establish and promote best practices for SQL, code quality, analytical rigor, validation, and documentation.
Mentor analysts through technical guidance, code reviews, and collaborative problem-solving.
Support inventory and fulfillment optimization initiatives, including purchase order splitting, retail store allocation, and order sourcing.
Size and prioritize opportunities, quantifying current-state costs and potential improvements in dollars, units, service levels, and labor.
Define and own performance metrics such as sell-through, fill rate, split shipment rate, and cost-to-serve, and evaluate the impact of optimization solutions.
Translate operational and merchandising constraints into analytical requirements and investigate unexpected outcomes to support model development and improvement.