A Data Analytics and Platform team within the Institutional Securities Technology division is seeking a collaborative, hands-on developer to build best-in-class solutions for Data APIs, Data Governance, Data Quality, Security & Privacy, and Architecture. This role offers a unique opportunity to work in a state-of-the-art modern data stack using open-source technologies aligned with our cloud strategy. We are looking for a proficient developer who is excited about innovation, with a proven track record of delivery.

Roles and responsibilities include:

• You will work closely with quants and traders across multiple trading desks to design, develop, deploy and support the innovative data science environment
• Design widely used and flexible APIs to our core data and functionality
• Analyze and optimize performance of large data workloads using compute clusters
• Stay up to date with emerging trends and tools in the data & analytics domain
• Provide support and design advice to users of the cross-asset platform
• Work closely with strategists and other stakeholders to help move their financial assets, valuation models and data pipelines to the platform
• Efficient Communication across regions and functions
• At least 3 years relevant experience would generally be expected to find the skills required for this role

What were looking for: we have a number of roles available for a range of technologists at different experience levels to join the team who will need

• Excellent problem solving and code development skills
• Ability and interest to research, learn and implement new Data and Analytics technologies and paradigms
• Enterprise level software development practices
• Strong oral and written communication skills
• Strong team working ability in local and global teams
• Passion for continuous improvement both personally and as a team

Skills that will help you in the role:

• Established experience with Python and Python ecosystem.
• Experience with performance optimization, concurrent programming and microservices design.
• Good knowledge of data processing libraries and stacks (Polars, Pandas, Numpy, Dask, Spark), SQL/NoSQL (Mongo)?Knowledge of ETL and streaming pub/sub platforms (Kafka)?Knowledge of SDLC management tools (Git, Jenkins, Github, docker, Kubernetes, Autosys), apps observability libraries (OpenTelemetry) and monitoring tools (Grafana, Loki, Tempo, Prometheus).
• Exposure to grid distributed applications (optional)
• Exposure to KDB or other timeseries database technologies (optional)
• Exposure to Cloud technologies (optional)
• Exposure to Risk Management systems and a wide range of Financial Instruments (optional)