Lead Risk Data Analyst

Ektello; Plano (TX); On-site; USD 140,000 - 170,000; Full time

Job summary

Ektello is seeking a Lead Risk Data & Business Intelligence Analyst for a 6+ month onsite contract in Plano, TX. You will drive end-to-end analytics, fraud detection, and executive reporting across risk domains.

You will mentor analysts, support ML initiatives, and own data pipelines and governance in a fast-paced environment. The role emphasizes collaboration with cross-functional teams and agile delivery.

Qualifications

• Bachelor’s degree in a technical field and 5–8+ years of analytics/risk experience.
• Experience leading enterprise analytics initiatives independently.
• Strong SQL, BI, and data integration skills across cloud platforms.

Responsibilities

• Lead end-to-end analytics delivery from requirements to production monitoring.
• Analyze transactions to detect fraud, reseller abuse, and risk trends.
• Design and maintain risk dashboards and executive reports.
• Provide SME guidance and mentor junior analysts.
• Support ML/AI model work with feature engineering and validation.
• Design and maintain ETL/ELT pipelines and data architecture.
• Lead data quality efforts and support platform migrations.
• Collaborate across Data Eng, Finance, Product, Ops, and IT in Agile cadence.
• Manage analytics roadmaps, backlogs, and stakeholder communications.

Skills

SQL & Data Warehousing

Tableau

Python or R

E-Commerce & Risk Data

Data Engineering & SDLC

Education

Bachelor’s degree in Data Science/CS/Statistics/IS/Business Analytics

Master’s degree preferred

Tools

Jira

Confluence

GitHub

Job description

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

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.

Qualifications & Skills

Technical & Analytical Requirements

• 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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