Data Platform Engineer

PayNet (Payments Network Malaysia); Kuala Lumpur; On-site; MYR 80,000 - 120,000; Full time

Job summary

PayNet (Payments Network Malaysia) is looking for an engineer to build and oversee a core data platform that supports analytics and national payment systems. In this role, you will make crucial decisions on data ingestion and tooling, ensuring reliability and optimising for cost.

Your responsibilities include evolving reusable frameworks, designing SDKs, and implementing monitoring tools. Strong Python and Kubernetes experience is essential for success.

Qualifications

• Strong Python engineering skills in building libraries and SDKs.
• Hands-on experience operating Kubernetes workloads with GitOps.
• Ability to prioritize reliability, cost, and usability.

Responsibilities

• Own and evolve ingestion and CDC frameworks across teams.
• Build standard pipeline SDKs for reliable data onboarding.
• Engineer monitoring and alerting tools for platform stability.

Skills

Strong Python engineering skills

Experience operating Kubernetes workloads

API design understanding

Ability to manage platform complexity

Tools

Kubernetes

GitOps

Job description

• 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

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