Principal Data Engineer (Fraud Projects)

PayNet (Payments Network Malaysia); Kuala Lumpur; On-site; MYR 180,000 - 240,000; Full time

Job summary

PayNet (Payments Network Malaysia) seeks a senior data engineer/scientist to own and evolve data pipelines and analytics for fraud prevention. You will productionise ML models and drive cross-institution collaboration with banks, regulators and e-wallet partners.

This role sits at the intersection of data engineering, ML and financial crime response. The ideal candidate has over five years of experience in data engineering or data science, strong Python/SQL skills, and hands-on big-data/cloud

Qualifications

• More than five years of relevant experience in data engineering or data science.
• Strong programming in Python and SQL; exposure to languages such as C#, VBA or equivalents.
• Hands-on experience with big-data technologies and cloud platforms.

Responsibilities

• Build, maintain and enhance data pipelines and infrastructure for analytics and data science.
• Productionise machine learning and statistical models for 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, turning requirements into robust outcomes.

Skills

Data engineering

Data science

Python

SQL

Big data

Cloud platforms

Kubernetes

Education

Bachelor's degree in a related field

Tools

Hadoop

Spark

AWS/Azure/GCP

Kubernetes

Job description

• 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

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