Lead the architecture and development of scalable data warehouses and batch and real-time data pipelines (e.g. Flink-based streaming and Hive/HDFS-based batch processing).
Own the end-to-end design and operation of a big data development platform, including multi-cluster resource management (e.g. YARN), access and permission controls, audit trails and unified authentication.
Build and maintain real-time user-facing data services, such as transaction, billing and profit-and-loss analytics, with strict accuracy, latency and availability targets.
Design reliable data ingestion and collection systems for external data sources, including primary/backup links, heartbeat monitoring, quota monitoring and fallback strategies.
Define data models, tagging systems and feature datasets that support user segmentation, analytics and personalisation.
Establish standards for data quality, observability, monitoring and governance, including unified monitoring dashboards and alerting.
Plan and run fault drills and joint performance and stress tests to find bottlenecks, reduce operational risk and prepare the platform for peak-traffic events.
Improve processing performance, resource efficiency and platform stability, and make sound scaling decisions.
Provide technical guidance, conduct design and code reviews, and mentor engineers.
Collaborate with business, product and engineering teams to translate requirements into dependable data solutions.

Bachelor's degree in Computer Science, Information and Computing Science, Mathematics, Statistics or a related field, or equivalent practical experience.
At least 8 years of professional software or data engineering experience, including at least 4 years building and operating big data platforms in production.
Hands-on experience in production with Flink for real-time processing, and with Hive, HDFS and YARN (or equivalent distributed storage, query and resource-management technologies).
Proven experience designing and running a multi-tenant data or big data development platform, covering resource and cluster management, permissions, auditing and authentication.
Strong proficiency in Java (including Spring / Spring Cloud microservices); working knowledge of Python, Scala or Go is an advantage.
Strong SQL skills, including query optimisation, with experience in relational and distributed databases (e.g. MySQL, TiDB).
Demonstrated experience building real-time data services for transactional or financial data, where accuracy and timeliness are critical.
Track record of improving system reliability through monitoring, fault drills, failover design and performance or stress testing.
Solid understanding of data modelling, ETL/ELT, batch and streaming architectures, and data quality practices.

Lead the architecture and development of scalable data warehouses and batch and real-time data pipelines (e.g. Flink-based streaming and Hive/HDFS-based batch processing).
Own the end-to-end design and operation of a big data development platform, including multi-cluster resource management (e.g. YARN), access and permission controls, audit trails and unified authentication.
Build and maintain real-time user-facing data services, such as transaction, billing and profit-and-loss analytics, with strict accuracy, latency and availability targets.
Design reliable data ingestion and collection systems for external data sources, including primary/backup links, heartbeat monitoring, quota monitoring and fallback strategies.
Define data models, tagging systems and feature datasets that support user segmentation, analytics and personalisation.
Establish standards for data quality, observability, monitoring and governance, including unified monitoring dashboards and alerting.
Plan and run fault drills and joint performance and stress tests to find bottlenecks, reduce operational risk and prepare the platform for peak-traffic events.
Improve processing performance, resource efficiency and platform stability, and make sound scaling decisions.
Provide technical guidance, conduct design and code reviews, and mentor engineers.
Collaborate with business, product and engineering teams to translate requirements into dependable data solutions.

Bachelor's degree in Computer Science, Information and Computing Science, Mathematics, Statistics or a related field, or equivalent practical experience.
At least 8 years of professional software or data engineering experience, including at least 4 years building and operating big data platforms in production.
Hands-on experience in production with Flink for real-time processing, and with Hive, HDFS and YARN (or equivalent distributed storage, query and resource-management technologies).
Proven experience designing and running a multi-tenant data or big data development platform, covering resource and cluster management, permissions, auditing and authentication.
Strong proficiency in Java (including Spring / Spring Cloud microservices); working knowledge of Python, Scala or Go is an advantage.
Strong SQL skills, including query optimisation, with experience in relational and distributed databases (e.g. MySQL, TiDB).
Demonstrated experience building real-time data services for transactional or financial data, where accuracy and timeliness are critical.
Track record of improving system reliability through monitoring, fault drills, failover design and performance or stress testing.
Solid understanding of data modelling, ETL/ELT, batch and streaming architectures, and data quality practices.

Principal Big Data Engineer (Contract Role)

Skills