Business Intelligence Analyst

Perry Ellis International•Miami, FL

2h•Onsite

About The Position

Perry Ellis International, Inc. represents a diverse portfolio of lifestyle apparel brands. The Company, through its wholly owned subsidiaries owns and licenses nationally and with a brand portfolio of over 40 Brands domestically and internationally recognized brands including: Perry Ellis®, Original Penguin® by Munsingwear®, Laundry by Shelli Segal®, Rafaella®, Cubavera®, Ben Hogan®, Savane®, Grand Slam®, John Henry®, Manhattan®, Axist® and Farah®. The Company enhances its roster of brands by licensing trademarks from third parties, including: Nike® for swimwear, and Callaway®, PGA TOUR®, and Jack Nicklaus® for golf apparel. We are seeking a dynamic Business Intelligence Analyst to join our team for five days onsite in Miami. In this role, you will support the entire organization by serving as the primary subject matter expert for our core reporting platform, Looker, across retail, wholesale, and accounting functions. Daily responsibilities include designing dashboards, generating reports, troubleshooting data models, and delivering actionable data-driven recommendations to key stakeholders. Additionally, you will build custom analytical solutions using Python as part of our ongoing initiative to modernize our BI stack from SAP BusinessObjects to Looker. As we roll out our long-term AI strategy, this role will continuously integrate advanced AI capabilities, making it essential to embrace emerging AI tools, adapt quickly, and actively build AI Agents and workflow automations to streamline operations.

Requirements

Bachelor's degree in MIS, Information Systems/Technology, Computer Science, or a related field.
2–3 years of hands-on experience as a BI Analyst or Data Analyst solving end-to-end business problems with data.
Advanced daily SQL proficiency, including writing, optimizing, and debugging complex queries, stored procedures, and analytics functions.
Practical experience writing queries and working within Snowflake.
Hands-on experience with data visualization tools, with a strong preference for Looker (experience with Tableau or Power BI is acceptable).
Functional experience using Python for data cleaning, data manipulation, and custom script development.
Hands-on experience leveraging AI tools (e.g., LLMs, AI code assistants in VS Code, or building basic AI Agents) to speed up coding and automate workflows.
Solid understanding of relational databases, data modeling, semantic layers, and data warehousing principles.

Nice To Haves

Industry experience in Fashion, Apparel, Retail, E-commerce, Wholesale, or Supply Chain functions.
Hands-on experience or familiarity with legacy reporting tools like SAP BusinessObjects (WebIntelligence) or Oracle databases.
Deep understanding of retail and supply chain domain metrics (e.g., sell-through rates, inventory turn, lead times, procurement, wholesale vs. DTC performance).
Exposure to ERP systems (e.g., Shopify, NetSuite, Jesta, Oracle Retail) or foundational Data Science concepts.

Responsibilities

Design, develop, and maintain reports, dashboards, and LookML models using Looker to meet business specifications across retail, wholesale, and accounting functions.
Modernize legacy BusinessObjects reports into modern, governed Looker semantic layers and interactive dashboards.
Write and debug daily SQL queries in Snowflake to validate Looker's auto-generated SQL, troubleshoot existing data models, and resolve data discrepancies or performance bottlenecks.
Build custom analytical solutions and scripts using Python for data cleaning, analytics, and operational metadata management.
Build AI Agents and leverage AI code assist tools (e.g., in VS Code and Looker) to automate data workflows, transport metadata, and optimize development processes instead of using traditional rule-based schedulers.
Rigorously validate raw data integrity and audit AI-generated query outputs to ensure absolute metric precision.
Collaborate directly with business analysts, executive leadership, and departmental stakeholders to translate data into actionable business strategy.
Schedule, monitor, and deliver executive-level reporting and ad-hoc analytics as required.
Proactively identify opportunities to optimize data models, refine semantic definitions, and improve overall BI environment efficiency.
Document data models, LookML definitions, query logic, and metadata specifications.