WHAT WILL YOU DO?

• Work with a large, global team of developers in developing risk reporting solutions for the equity derivatives businesses at Capital Markets.
• Work closely with both quants and traders in defining requirements, aligning deliverables, and continually refining code for quality.
• Contribute support and knowledge towards a transition to better devops tools, practices and metrics.
• Continuously identify potential issues, conflicts, and risks. Analyze, mitigate and escalate where appropriate
• Establish strong working relationships across business and functional units and platforms to build influence and impact with key business partners.
• Collaborate with groups to ensure to define and achieve deliverables and ensure initiatives are effectively integrated into company's procedures and processes.
• Contribute to ongoing administrative program activities as required.

• Work in Agile fashion using JIRA/scrum

• Collaborate with CI/CD Team to create Devops pipeline for C++ applications

WHAT DO YOU NEED TO SUCCEED?

Must have:
• Strong programming experience in modern C++

• Proficiency in Oracle and Python is a plus

• Undergraduate degree or higher.
• 10+ years' development experience. Valuable if in a Capital Markets or FinTech.
• Passion for technology and large-scale software design.
• Dedication to high-quality, clear, and well-tested code.
• Deadline-driven, detailed and results-oriented; able to meet consistently high quality standards while handling a variety of tasks and deadlines simultaneously. Strong interpersonal skills.

• Commercial experience - Communicating with multiple teams and stakeholders and understanding the greater impact of the systems and services you have built

• Strong communicator - being able to work across multiple role types in engineering teams is critical.

Nice-to-have:
• Experience in Risk Systems and / or Pricing Model Integration.
• Experience working in distributed systems including webservices, messaging software (e.g. Solace), etc..
• Experience in working with large amounts of data.