Principal Data Engineer (Fraud Projects)

Why PayNet / Why Now

• Shape data capabilities that strengthen how Malaysia’s payment ecosystem responds to fraud and scams
• Build solutions spanning payment tracing, profiling, scoring, fraud-network analysis and emerging modus operandi
• Work at the intersection of data engineering, machine learning and industry-wide financial crime response
• Partner with banks, e-wallets, regulators and internal specialists on proofs of concept and ecosystem initiatives
• Explore new-generation technologies that uplift fraud, risk, compliance and security capabilities

TL;DR

• Own secure, reliable and usable data pipelines and infrastructure for analytics and data science
• Productionise statistical and machine learning models that improve fraud prevention and investigation outcomes
• Drive financial crime analytics, automation, monitoring and reporting across Risk & Compliance
• Lead technical decisions with broad direction, clear accountability and independent delivery
• Contribute at Senior or Principal level, bringing more than five years of relevant data science or engineering experience

Why This Role Matters

• Turn complex payment data into capabilities the ecosystem can use to combat fraud and scams
• Bridge experimentation and production so analytical models deliver dependable operational value
• Improve collective fraud response by connecting data, systems and cross-industry stakeholders
• Raise the division’s ability to monitor, analyse and automate fraud, risk, compliance and CISO processes
• Shape greenfield and cross-functional projects that strengthen PayNet’s services and security

What You Will Actually Do

• Build, maintain and continuously enhance data pipelines and infrastructure that keep data accessible, secure and usable
• Productionise machine learning and statistical models for payment tracing, profiling, scoring and fraud-network insights
• Develop and test fraud solutions and microservices, including transaction scoring and centralised financial crime capabilities
• Drive analytical and automation initiatives across fraud, risk, compliance and CISO monitoring and reporting
• Lead technical delivery across concurrent projects, deciding how to move from ambiguous requirements to robust outcomes
• Engage financial institutions, e-wallets, regulators, vendors and internal teams to shape practical ecosystem solutions

Examples of This Role in Practice

• A fraud model performs well in experimentation; you decide how to engineer, deploy and monitor it for dependable production use
• Payment data sits across multiple sources; you shape a secure pipeline that makes it usable for tracing and network analysis
• Banks and e-wallets join an industry proof of concept; you translate shared needs into a testable data solution
• A new fraud pattern emerges; you build analysis that helps specialists discover accounts, identities and transactions of interest
• Monitoring relies on manual work; you drive automation that improves the quality and repeatability of risk reporting

What Will Help You Succeed

• More than five years of relevant experience in data engineering, data science or both, supported by a related degree
• Strong programming capability in Python and SQL, with working exposure to languages such as C#, VBA or equivalents
• Hands-on experience with big-data technologies such as Hadoop or Spark, cloud platforms such as AWS, Azure or GCP, and container orchestration using Kubernetes
• Applied knowledge of machine learning, analytical scripting, databases, automation and data visualisation
• Sound judgment, conceptual thinking and the confidence to take accountable technical decisions under broad direction
• Clear communication and relationship skills across business users, financial institutions, regulators, vendors and technical teams

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