Engineering, Data Engineer, Associate/VP, Hong Kong

As part of GS's continued focus on execution innovation, the machine learning modelling team is looking for a high caliber, self-driven candidate that has experience in creating complex data pipelines to power the next generation of trading platforms and execution algorithms.

The successful candidate will help building data pipelines, sourcing, cleaning and feeding data into our statistical models. In addition to that he/she will help deploy our models in a resilient and scalable fashion ensuring that latency requirements are met.

As part of the Electronic Trading (GSET) team, our team is looking at leveraging market and trading data to develop machine learning/statistical models to improve our execution algorithms performance. We are also building BOTs that will deliver insights to our clients on their execution flow.

As part of the firm global machine learning effort we are collaborating with other teams to set the standard of machine learning based systems. This will be a chance for the right candidate to have a great impact into how we build machine learning systems here at Goldman Sachs.


Basic Qualification:

  • Experience designing and implementing large scale data streaming applications
  • Good understanding of middleware technologies, particularly Kafka
  • Experience with SOA and RESTful services
  • Good understanding of data pre-processing concepts (data cleaning, transformation, reduction)
  • In-depth knowledge in relational database including database design
  • Bachelor degree or above with CS, engineering or related background
  • Excellent communication skills as he/she will be required to interact with different stakeholders ( Algo Strats, Coverage Team, R&D Tech ...)
  • 2-3+ years of experience in data engineering

Preferred Qualification:
  • Experience deploying machine learning models in production
  • Ability to deploy code in a Kubernetes / Docker environment
  • Interest in machine learning and statistical modelling



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