Weekend Data Loss Prevention Analyst Jobs (NOW HIRING)

As a Senior Loss Prevention Analyst, you'll be a trusted analytical partner responsible for turning complex operational and financial data into meaningful business insights. You'll evaluate trends, identify areas of risk, investigate process gaps, and develop reporting solutions that support strategic decision-making across the organization.

Success in this role requires a blend of technical expertise, analytical curiosity, and strong business acumen. You'll work across multiple functions to solve problems, improve reporting capabilities, support loss prevention initiatives, and influence decisions through clear, compelling data storytelling. Your work will directly contribute to strengthening controls, enhancing operational execution, and helping AutoZone make smarter, data-driven decisions.

What We're Looking For: 

• 5+ years of experience in analytics, business analysis, financial analysis, operations analysis, or a related field. 
• Strong proficiency in SQL and Python. 
• Experience working within Google Cloud Platform (GCP) and BigQuery environments. 
• Experience developing reports, dashboards, and analytics using business intelligence tools such as Looker. 
• Strong analytical, critical thinking, and problem-solving capabilities. 
• Demonstrated experience identifying and resolving data quality, reporting, or process issues. 
• Experience with forecasting, variance analysis, financial analysis, or business case development. 

You'll Go the Extra Mile If You Have:

• Bachelor's degree in Business, Finance, Information Systems, Data Analytics, Computer Science, or a related field. 
• Experience working in large-scale retail, distribution, or enterprise operations environments. 
• Knowledge of loss prevention, asset protection, inventory management, or supply chain analytics. 
• Experience supporting operational risk management, inventory shrink analysis, or fraud detection initiatives. 
• A track record of driving process improvements that deliver measurable business impact. 
• Passion for leveraging data to solve complex business challenges and improve organizational performance.

What You'll Do:

• Develop, maintain, and enhance reporting, dashboards, and analytical solutions that support Loss Prevention and enterprise business objectives.
• Create scalable reporting and data models using enterprise platforms such as GCP, BigQuery, and Looker.
• Deliver accurate, timely insights that enable informed decision-making.
• Analyze operational, financial, inventory, and loss prevention data to identify trends, risks, anomalies, and opportunities.
• Translate analytical findings into clear, actionable recommendations that improve business outcomes.
• Support proactive risk detection and mitigation efforts across the enterprise.
• Write, optimize, troubleshoot, and maintain SQL and Python-based analytical solutions.
• Support the modernization and enhancement of reporting and analytics capabilities within enterprise data environments.
• Improve automation and reporting efficiency through scalable technical solutions.
• Investigate reporting discrepancies, data quality concerns, and process breakdowns.
• Conduct root cause analysis to identify underlying issues and implement sustainable corrective actions.
• Drive continuous improvement in data integrity, reporting accuracy, and operational performance.
• Collaborate with Loss Prevention, Inventory Accounting, Supply Chain, Store Operations, Merchandising, and IT teams.
• Gather business requirements and translate needs into effective analytical solutions.
• Build strong partnerships that drive alignment and business results.
• Support forecasting, variance analysis, business case development, and strategic initiatives.
• Evaluate business performance and provide recommendations that support loss prevention and operational goals.
• Deliver insights that influence enterprise-level decisions.
• Present complex analyses in a clear and concise manner tailored to business and executive audiences.
• Develop presentations and decision-support materials that effectively communicate key findings and recommendations.
• Use data storytelling techniques to influence leadership decisions.
• Share knowledge and best practices with peers and junior analysts.
• Contribute to a culture of collaboration, innovation, and continuous improvement.
• Serve as a subject matter resource for analytics and reporting initiatives.