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PayNet (Payments Network Malaysia) Linkedin · Posted 6d ago

Data Platform Engineer

Federal Territory of Kuala Lumpur

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Indexed description

Why PayNet / Why Now

  • Build the data platform that underpins analytics, products, and decision‑making across national payment systems
  • Shape how data is ingested, processed, and operated as PayNet scales volume and complexity
  • Influence platform standards early, before one‑off solutions become systemic debt
  • Work on infrastructure where reliability and cost efficiency directly affect enterprise outcomes
  • Step into a role with clear ownership, your platform decisions compound across teams

TL;DR

  • Build and own the core data platform that other engineers rely on daily
  • Decide how data ingestion, pipelines, and tooling scale across teams and use cases
  • Optimise for reliability, cost, and developer experience, not one‑off solutions
  • Work hands‑on with Python, Kubernetes (container orchestration platform), and cloud‑native data infrastructure
  • Be accountable for platform outcomes, not just code delivery

Why This Role Matters

  • Enables data engineers to ship pipelines faster with fewer operational failures
  • Reduces duplicated effort through standardised ingestion and pipeline frameworks
  • Improves platform reliability that downstream analytics and products depend on
  • Shapes how data services are built, deployed, and operated across PayNet
  • Directly impacts cost efficiency and scalability of the data lake

What You Will Actually Do

  • Own and evolve reusable ingestion and CDC (Change Data Capture) frameworks used across teams
  • Build standard pipeline SDKs (Software Development Kits) that make onboarding new data sources predictable
  • Decide platform patterns that balance flexibility, simplicity, and scale
  • Engineer monitoring, alerting, and debugging tools that prevent silent failures
  • Run platform deployments using GitOps (Git‑based Operations) with strong operational discipline

Examples of This Role in Practice

  • Designing a CDC framework that becomes the default for all new data sources
  • Eliminating repeated pipeline failures by standardising retries and observability
  • Challenging a complex design in favour of a simpler, more robust platform API (Application Programming Interface)
  • Improving developer velocity by replacing bespoke scripts with shared tooling
  • Catching platform instability early through proactive monitoring improvements

What Will Help You Succeed

  • Strong Python engineering skills building libraries, SDKs, or internal frameworks
  • Sound judgment in API design and managing long‑term platform complexity
  • Hands‑on experience operating Kubernetes workloads with a GitOps mindset
  • Understanding trade‑offs of running stateful data workloads at scale
  • Ability to prioritise reliability, cost, and usability over theoretical perfection
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