Data Platform Engineer at PayNet (Payments Network Malaysia) – Kuala Lumpur | Jobyssey

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

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

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

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

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

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

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

Data Platform Engineer

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