Ampstek
Java Developer
Java Developer in Hong Kong building and supporting distributed batch pipelines that compute PnL, Market Risk, Credit Risk and regulatory feeds. Day-to-day work spans Java/Perl/Python, Apache Spark, tuning slow queries and memory, automating batch recovery, and deploying cloud-native solutions on Azure.
- Compensation
- Not disclosed
- Location
- Hong Kong, Hong Kong
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Software engineering
- Posted
Skills
Must have
- Core languages: Java, Perl and Python with understanding of multi-threading, concurrency, and memory optimization
- Hands-on experience with big data / distributed computing technologies like Apache Spark
- Proficiency in enterprise scheduling tools such as Autosys and Stonebranch
- Strong SQL/NoSQL capabilities, particularly with high throughput databases (e.g., Sybase, Oracle)
- Solid experience with Git for deploying cloud-native data solutions on Azure
Nice to have
- Strong proficiency in Unix commands
Full description
- Core Languages: Java, Perl and Python with an understanding of multi-threading, concurrency, and memory optimization. Strong proficiency in Unix commands is highly preferred.
- Big Data & Distributed Computing: Hands-on experience with technologies like Apache Spark.
- Orchestration & Workflow: Proficiency in enterprise scheduling tools such as, Autosys and Stonebranch.
- Database & Storage: Strong SQL/NoSQL capabilities, particularly with high throughput databases (e.g., Sybase, Oracle)
- CI/CD & Cloud: Solid experience with Git for deploying cloud-native data solutions on Azure
Core Responsibilities
- Batch Pipeline Engineering: Support and implement robust distributed batch processing pipelines to compute PnL, Market Risk, Credit Risk and other market specific regulatory feeds.
- Performance Tuning: Optimize slow-running database queries, memory management, and compute grid utilization to shrink the overnight batch window.
- Resiliency & Automation: Automate batch failure recovery, monitoring alerts, and self healing mechanisms to minimize manual operational intervention.
- Stakeholder Collaboration: Partner closely with Quants, Risk Managers, Product Controllers and Production Support teams for support issues as well as onboard new Batch feeds as requested by the Business and Operation teams.