全职 Data Resiliency Engineer (Datalake) 工作, 薪水, PayNet (Payments Network Malaysia) Federal Territory 公司招聘中 - Ricebowl

Why PayNet / Why Now

• Contribute to national critical infrastructure operating at increasing scale and complexity
• Do work with impact beyond a single organisation as PayNet’s role in the ecosystem expands
• Join an organisation focused on resilience, reliability, and stability as core operating standards
• Make decisions and contributions that matter at national scale

TL;DR

• Own data platform resiliency for systems that cannot be wrong or unavailable
• Make judgment calls during incidents where data accuracy and trust matter
• Operate with production ownership across monitoring, recovery, and readiness
• Build confidence in data used by regulators, banks, and PayNet stakeholders

Why This Role Matters

• Data failures undermine trust in national payment reporting
• Poor incident handling increases operational and regulatory risk
• Strong resiliency enables faster, safer platform evolution
• This role requires judgment over process during real incidents

What You Will Actually Do

• Own detection, diagnosis, and resolution of data platform incidents
• Shape monitoring and alerting to surface issues before impact
• Decide on recovery actions that balance data correctness and service continuity
• Drive improvements in data quality, observability, and resilience
• Influence pre‑production readiness and disaster recovery design
• Partner engineers to reduce recurring failure modes

Examples of This Role in Practice

• Detect upstream data corruption and decide whether to halt downstream reporting
• Lead recovery of delayed payment reports under regulatory timelines
• Redesign data quality checks after identifying silent data drift
• Refine alerts to eliminate noise while catching true incidents
• Challenge designs that trade resiliency for short‑term delivery speed

What Will Help You Succeed

• Required: Hands-on experience operating production data platforms, including incident response, root-cause analysis, and post-incident remediation
• Required: Strong understanding of modern data lake and data pipeline architectures, including batch and streaming ingestion patterns
• Helpful: Proficiency in Python and SQL for data investigation, validation, and troubleshooting
• Helpful: Experience with data observability and monitoring tools such as Datadog or similar platforms
• Helpful: Exposure to distributed data processing or platform operations, including PySpark-based pipelines or data workloads running on Kubernetes