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Lead Risk Data & Business Intelligence Analyst
Role Overview
Location: Plano, TX (Onsite) Employment Type: 6 + month contract to (hire + multi-extension capability) Rate: 73.00 /hr W2
The Lead Risk Data & Business Intelligence Analyst drives end-to-end data analytics, fraud mitigation, and risk management initiatives across our global e-commerce ecosystem. In this role, you will analyze large-scale transactional datasets, build robust Business Intelligence (BI) and reporting solutions, optimize AI/ML fraud detection models, and design automated data pipelines. Acting as a Subject Matter Expert (SME), you will partner with cross-functional leadership—including Data Engineering, Finance, Operations, Product, and Risk Operations—to translate complex business challenges into scalable, technical analytical products.
Key Responsibilities
Analytics Leadership & Strategic Risk Monitoring
End-to-End Analytics Delivery: Lead complex data analytics and risk initiatives from business requirement gathering through solution design, ETL development, validation, and production monitoring.
Transaction & Fraud Analysis: Query and analyze large-scale e-commerce, payment, and customer data to detect fraudulent activity, reseller abuse, promotional exploitation, chargeback trends, and emerging operational risks.
Dashboards & Executive Reporting: Design, deploy, and maintain automated risk dashboards, KPI trackers, and alert systems to deliver clear, actionable insights via tables, charts, and executive summaries.
Subject Matter Expertise & Mentorship: Serve as an analytical SME supporting high-priority fraud investigations, setting data governance standards, and mentoring junior analysts and team members.
Technical & AI/ML Fraud Strategy Optimization
AI/ML Model Support: Enhance fraud prevention strategies by contributing to feature engineering, model validation, performance tuning, and third-party vendor solution integration.
ETL & Data Architecture: Design, test, automate, and maintain enterprise-level ETL/ELT pipelines; modify database management systems to ensure data quality and uninterrupted reporting continuity.
Data Quality & Platform Migration: Lead data validation, Quality Assurance (QA) testing, root-cause error analysis, and platform migration initiatives across cloud data warehouses.
Project & Stakeholder Management
Cross-Functional Alignment: Collaborate with global partners across Data Engineering, Finance, Product, Operations, and IT to align technical deliverables with strategic business goals.
Product & SDLC Lifecycle: Manage analytics project roadmaps, backlog prioritization, timeline dependencies, and stakeholder communication utilizing Agile methodologies.
SQL & Data Warehousing: Advanced SQL proficiency for multi-source data integration across relational databases and cloud data platforms (e.g., Snowflake, Databricks, AWS, GCP, Azure).
Business Intelligence: Advanced expertise in Tableau or equivalent BI platforms to build scalable, automated reporting assets.
Programming & Modeling: Proficiency in Python or R for statistical analysis, anomaly detection, predictive modeling, data automation, and script development.
E-Commerce & Risk Datasets: Direct experience working with enterprise e-commerce, transaction, payment gateway, fraud, and chargeback datasets.
Data Engineering & SDLC: Experience with ETL/ELT pipelines, data governance, QA testing, and software development tools (Jira, Confluence, GitHub).
Business & Leadership Requirements
Problem Solving & Communication: Strong capability to navigate ambiguous business scenarios and communicate complex technical concepts clearly to non-technical executives.
Methodology & Standards: Familiarity with information service standards and frameworks (e.g., SDLC, ITIL, QA/Testing, Six Sigma, Agile).
Education & Experience
Education: Bachelor’s degree in Data Science, Computer Science, Statistics, Information Systems, Business Analytics, or a related technical field (Master’s degree preferred).
Experience: 5–8+ years of progressive experience in data analytics, business intelligence, data science, or risk analytics, with a demonstrated history of independently leading enterprise initiatives.
Preferred Certifications: Certified Fraud Examiner (CFE), Six Sigma, ITIL, CBPP, AWS/GCP/Azure Cloud, or relevant Data Science / Agile certifications.
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