Qaulity Data Analyst (Customer Onsite)

Expeditors•Ellenwood, GA

12h•Onsite

About The Position

As the newly implemented Quality Management System (QMS) reaches General Availability globally, we are seeking a highly autonomous Quality Data Analyst to act as the primary engine for system stabilization and data extraction globally. Early-stage systems are notoriously complex and initially unstable; we need a tenacious data specialist who can navigate sparse documentation, extract fragmented data, and build a predictive data pipeline from the ground up. You will be the sole bridge connecting real-time warehouse floor errors directly to our engineering risk documentation, specifically Process Failure Mode and Effects Analysis (PFMEA). By standardizing the data to tell a cohesive story, bypassing the latency of traditional RCCA loops, and defining strict thresholds for risk updates, you will turn operational noise into actionable process improvements and prevent administrative chaos.

Requirements

Self-Sufficient Pioneer: You thrive in early-stage, ambiguous environments where documentation may be lacking. You can independently figure out how to navigate backend tables, extract data, and partner with Insights teams to fix routing issues.
Strategic Synthesizer: You understand that data is only valuable if it drives process improvement. You instinctively connect quality trends back to the "North Star" of the PFMEA, teaching others how data applies to process.
Resilient Problem Solver: You aren't discouraged by the rough, upfront effort required to launch a global system. You see cleaning unstructured data and building reliable dashboards as a satisfying puzzle rather than a chore.
Tactful Influencer: You possess the interpersonal skills to influence cross-functional operational teams, effectively pitching mitigation plans and driving execution in environments you do not directly manage.
3 to 5 years of experience in data analytics, quality assurance, or process engineering, ideally supporting a global supply chain or warehouse operations.
Deep understanding of Quality Management Systems (QMS), Root Cause and Corrective Action (RCCA) loops, and strict risk documentation methodologies.
Proven ability to build operational dashboards, align disparate data streams, and apply predictive analytics to identify quality trends.
Ability to synthesize large datasets to tell a cohesive, actionable story for leadership.
Demonstrated experience navigating enterprise software launches (from UAT through General Availability), extracting data via backend tables, and collaborating with developer or insights teams to establish robust data flows.
3 to 5 years of experience in logistics data analysis, operations support, or quality assurance within a warehouse/supply chain environment.
Solid understanding of supply chain flows, procurement dependencies, 3PL dynamics, and warehouse operations.
Strong experience with Google Sheets (including VLOOKUPs and Pivot Tables) and Microsoft Excel.
Demonstrated ability to incorporate AI tools (e.g., Gemini) and automation workflows to streamline data synthesis, clean unstructured data, and reduce manual reporting efforts.
Proven track record of learning and mastering complex, proprietary operational databases and ticketing/bug systems.

Nice To Haves

Six Sigma Black Belt or extensive PFMEA experience is highly preferred.

Responsibilities

Investigative System Stabilization: Navigate the newly launched, GA-stage QMS tool to extract, condition, and manage quality data, working through initial system instability and backend API learning curves to create reliable data pipelines.
Data Standardization & KPI Alignment: Audit and unify core data streams, working directly with Insights teams to adjust backend tables. You will ensure the correct data is pulled, made visible to users, and aligned with business KPIs so the data tells a cohesive operational story.
PFMEA Creation & Implementation: Lead the initial development of Process Failure Mode and Effects Analysis (PFMEA) documentation, establishing the foundational risk matrices from scratch and teaching cross-functional teams how to utilize them as their operational "North Star."
Dynamic PFMEA Threshold Management: Once the foundational PFMEAs are built, define and govern strict rules for dynamic updates, ensuring that temporary spikes in shipping errors do not cause these tightly controlled risk matrices to constantly oscillate.
Proactive Predictive Analytics: Design early warning systems that feed primarily off raw Issue Management data rather than completed Root Cause and Corrective Action (RCCA) loops, bypassing the standard 5-to-30 day lag time for investigations to surface immediate trends.
Operational Feedback Loops: Translate complex quality data into clear mitigation plans. While you do not own the warehouse floor, you will collaborate with cross-functional Operations teams to drive accountability and execute process alignments based on your data findings.