About Marshall Wace:

Marshall Wace is a leading provider of alternative investment solutions with approximately $63 billion of assets under management (“AUM”) and over 500 employees worldwide including London, New York, Singapore and Hong Kong. One of our founding beliefs is that technology and data are at the core of the business allowing us to build and maintain cutting edge hardware and software solutions.

The technology team is lean and has a culture that encourages interaction across all areas of the business on a global scale. Our aim is to use the best tool for the job therefore there is the opportunity to be constantly learning and use modern technologies.

At Marshall Wace, all our teams strive to push boundaries and think innovatively, creating an environment that is fast paced, dynamic and successful. Click here to view our open- source projects for a glimpse into our culture, projects and types of technologies we work with.

Role Overview:

We are looking for an experienced software engineer with a history of developing horizontally scalable architectures to join the Quant Platform Engineering team in London.

The team delivers libraries and distributed platforms to support quantitative research and quantitative portfolio implementation. We have a direct impact on Marshall Wace's ability to research and implement new investment strategies, and we develop solutions that other technology teams can in turn rely upon to develop their own systems.

The role will suit an agile and rigorous self-starter who enjoys working across technologies to deliver practical solutions that can be leveraged by a wide range of users.

Responsibilities:

Collaboratively architect and develop cloud-native distributed applications
Developing high-performance libraries and distributed batch/DAG processing platforms across a spectrum of technologies, with an emphasis on scale, reusability, and simplicity
Actively communicating with stakeholders to collate requirements and plan rapid application deliveries
Write quality, performant code across various platforms, with thorough testing and state of the art monitoring
Identifying high-value opportunities for machine learning and other impactful technologies
Guarantee the quality of the data produced by our systems and investigate data issues at scale
Document solutions, train users and first-line application support teams
Own responsibility for platforms, diagnosing and fixing production issues
Remain up to date on the latest technologies and contribute new ideas to enhance our in-house systems

Technologies:

Expert or highly proficient in Python and/or Matlab and able to become proficient in the other
Expert or highly proficient in C++ and/or Java and able to become proficient in the other
Demonstrated experience of delivering all aspects of a large-scale distributed project from start to finish across a combination of:
- Big data & distributed compute (e.g. Spark/Dremio/Dask)
- DAG schedulers and parallel executors (e.g. Airflow/Kubernetes/Slurm)
- Data science and/or machine learning platforms
- SQL and NoSQL databases and object storage (e.g. AWS S3)
- Monitoring and observability (e.g. Prometheus/Opentelemetry)
- Test driven development and CI/CD

Additional beneficial skills and experience:

Experience in cross-language/interop development (e.g. mex, pybind)
Experience of async programming and/or stream processing (e.g. Flink)
Knowledge of infrastructure as code (e.g. GitOps, Helm)
Data science/finance/quant background

Quantitative Data Engineer