- Wilayah Persekutuan Kuala Lumpur, Malaysia Malaysia
PayNet (Payments Network Malaysia) Hiring! Full Time Data Engineer- Python Developer - Data Lake in - Ricebowl
Data Engineer- Python Developer - Data Lake
PayNet (Payments Network Malaysia)
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Job Description
Responsibilities
Why PayNet / Why Now
- PayNet is operating at a scale where data reliability directly affects financial integrity and regulatory confidence
- Transaction volumes and data complexity are increasing faster than legacy pipelines can safely support
- Teams need production‑grade data foundations to move from insight to action without bottlenecks
- This role exists to shape how PayNet engineers data as a long‑term capability, not a one‑off solution
- The decisions made here set standards that multiple teams will build on for years
- Own the design and reliability of critical data pipelines
- Build scalable Python-based ETL (Extract, Transform, Load) workflows used by multiple teams
- Decide how data quality, performance, and resilience are enforced
- Operate across cloud platforms to support enterprise-grade data needs
- Data decisions across PayNet depend on reliable, well-engineered pipelines
- This role determines how fast teams can move from data to insight
- You influence standards for data quality, scalability, and engineering rigor
- Your work directly impacts business outcomes and regulatory confidence
- Design and build production-grade data pipelines using Python
- Own ETL workflows end-to-end, from ingestion to consumption
- Decide and implement data quality checks that protect data integrity
- Optimize pipelines for performance, cost, and long-term scalability
- Deploy and operate data solutions on cloud platforms such as AWS (Amazon Web Services), GCP (Google Cloud Platform), or Azure (Microsoft Azure)
- Redesigning a slow-running pipeline to scale with increased transaction volume
- Partnering with Data Scientists to productionise experimental data workflows
- Identifying and fixing data quality issues before they impact reporting
- Choosing the right orchestration or storage approach for a new dataset
- Documenting data flows so teams can onboard and move faster
- Strong Python fundamentals and experience building real-world data pipelines
- Hands-on experience with data libraries and frameworks such as Pandas, NumPy (Numerical Python), and Airflow (Apache Airflow)
- Confidence working with SQL (Structured Query Language) and relational databases
- Practical experience with cloud platforms such as AWS, GCP, or Azure
- Exposure to Big Data technologies such as Hadoop (Apache Hadoop) or Spark (Apache Spark) is a plus
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