Data Reliability Engineer
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 evolutionThis 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