Data Platform Engineer

• 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