Senior Data Analyst

Overview

Joining Razer will place you on a global mission to revolutionize the way the world games. Razer is a place to do great work , offering you the opportunity to make an impact globally while working across a global team located across 5 continents. Razer is also a great place to work, providing you the unique, gamer-centric #LifeAtRazer experience that will put you in an accelerated growth, both personally and professionally.

Responsibilities

Razer Gold aims to become the world’s leading payment service provider for games and digital content. As our business grows to meet the needs of gamers globally, we are seeking a highly skilled and detail-oriented Senior Data Analyst – Risk and Compliance to perform data-driven risk assessments and compliance monitoring initiatives. This role involves analyzing complex datasets to identify patterns, anomalies, and emerging risks that impact regulatory and internal compliance. The ideal candidate will have deep analytical expertise, experience in regulatory environments, and the ability to communicate insights clearly to stakeholders at all levels.

• Design, develop, and maintain reports and dashboards related to fraud risk, suspicious activity, transaction monitoring, and regulatory compliance.
• Build and enhance Power BI dashboards to visualize key AML and fraud KPIs (e.g., alert volumes, false positives, escalation rates).
• Analyze large datasets (transactions, customer behavior, device data) to detect anomalies, trends, and fraud patterns.
• Support the development of predictive risk models (e.g., fraud scoring, anomaly detection) using Python, pandas, scikit-learn, etc.
• Write and optimize complex SQL queries for extracting and transforming data from multiple fraud / AML-related systems.
• Collaborate with compliance officers, fraud investigators, data scientists, and product teams to gather requirements and build tailored data solutions.
• Conduct data validation, cleansing, and reconciliation to ensure accuracy of fraud / AML reporting and alert generation.
• Work closely with engineering teams to define and document data models
• Identify areas of improvement in alerting logic, rules tuning, or case management workflows through deep-dive analysis
• Partner with data engineering teams to improve data availability, reliability, and performance for analytics use cases

Nice to Have

Requirements

Pre-Requisites

Are you game?

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