Senior Data Analytics Engineer ( Risk Analytics)

Salary: RM 8,000 – RM 10,000 per month

Benefits of this role:

• 13th Months Salary (upon Company Declaration)

• Project Incentive (upon Company Declaration based on project performance)

• Transport, Meal, Birthday Allowances (upon Confirmation)

• Sports Activities (Badminton & Basketball), Company Outing

• Training and certification sponsored by company (Selected employees).

• Rewards and recognition: Long Service Award / Best Performer Award / Role Model Awards etc

• Medical Insurance (upon Confirmation)

• Optical, Dental, Body Check Up Claim (upon Confirmation)

Job Description

Join our Data & Analytics team to detect abnormal behaviour, build risk detection strategies, and deliver reliable data solutions for risk management and business decisions. You’ll work with risk, product, operations and engineering teams to turn complex risk scenarios into practical, data-driven solutions.

Key Responsibilities

Risk Analytics & Strategy

• Analyze large-scale transaction and user behavioral data to identify suspicious activities, abnormal patterns, and potential business risks.

• Design, develop, and continuously optimize risk detection rules, monitoring strategies, and analytical frameworks.

• Identify fraud, abnormal transactions, promotional abuse, arbitrage or exploitation patterns, and other suspicious user behaviors.

• Apply statistical analysis, anomaly detection, and behavioral analysis to improve risk detection capabilities.

• Evaluate and optimize risk strategies based on detection accuracy, false-positive rates, precision/recall, and business impact.

• Investigate risk incidents, perform root-cause analysis, and recommend preventive or corrective actions.

Data Engineering & Advanced Analytics

• Design and maintain scalable ETL/ELT pipelines, datasets, and data models supporting risk analytics and monitoring.

• Write and optimize complex SQL queries and Python scripts for large-scale data processing, investigation, and automation.

• Integrate and analyze data from multiple sources, including transactional databases, event logs, APIs, and third-party systems.

• Perform exploratory data analysis (EDA), statistical analysis, and behavioral pattern analysis.

• Ensure data quality, consistency, reliability, and processing efficiency.

Ownership & Collaboration

• Partner with Risk, Operations, Product, and Engineering teams to translate business risk scenarios into technical and analytical solutions.

• Independently lead risk analytics initiatives from problem identification → data investigation → strategy development → implementation → performance evaluation.

• Provide actionable recommendations based on analytical findings.

• Communicate complex analytical and risk-related insights clearly to both technical and non-technical stakeholders.

• Contribute to team best practices and mentor junior team members when required.

Risk Monitoring & Automation

• Develop automated risk monitoring solutions, dashboards, alerts, and recurring analytical workflows.

• Define and optimize statistical thresholds and anomaly detection methodologies.

• Monitor key risk indicators and proactively identify emerging risk patterns.

• Automate recurring risk analysis, validation, and reporting processes.

• Continuously improve monitoring coverage, alert accuracy, and operational efficiency.

Qualifications Required

• Bachelor’s degree in Computer Science, Data Science, Statistics, or related field.

• 5+ years of relevant experience in Data Analytics, Data Engineering, Risk Analytics, or related roles.

• At least 2 years of hands-on experience in Risk Analytics, Fraud Detection, Transaction Monitoring, or Risk Strategy Development.

• Strong proficiency in SQL and Python (mandatory), including complex data analysis, automation, and query/performance optimization.

• Proven experience identifying suspicious behavioral patterns and developing or optimizing risk detection rules or strategies.

• Strong understanding of statistical analysis, anomaly detection, and data-driven decision-making.

• Practical experience with ETL/ELT, data warehousing, and large-scale datasets.

• Experience with relational or analytical databases such as PostgreSQL, MySQL, ClickHouse, or equivalent technologies.

• Ability to independently investigate complex problems, identify root causes, and propose practical solutions.

• Strong analytical thinking, problem-solving, communication, and stakeholder management skills.
  

Preferred / Nice to Have

• Experience in high-volume digital platforms, payments, financial transactions, gaming, e-commerce, fintech, or other transaction-intensive environments.

• Experience detecting behavioral fraud, promotional abuse, account abuse, arbitrage, or other sophisticated risk patterns.

• Familiarity with real-time or near-real-time data processing technologies such as Kafka, Flink, or equivalent systems.

• Experience developing statistical risk models, anomaly detection algorithms, or automated alerting systems.

• Experience with BI and visualization tools such as Tableau, Power BI, or similar platforms.

• Experience with cloud platforms such as Alibaba Cloud, AWS, GCP, or Azure.

• Exposure to machine learning techniques applied to fraud detection, anomaly detection, or behavioral risk analysis.