Requirements

Demonstrated ability to design, build, and operate scalable production-grade software systems for data processing, including expertise in Python, SQL, and modern data platforms.
Hands-on experience with modern software engineering practices and tooling, including version control, CI/CD, automated testing frameworks, containerisation, and AI-augmented software development workflows.
Experience working in cloud environments such as AWS or Microsoft Azure, including infrastructure provisioning and automation using Infrastructure as Code.
Strong understanding of data validation, testing strategies, code quality practices, and ability to independently drive technical solutions from design through delivery.
Experience supporting advanced analytics and AI/ML use cases, with a rigorous, data-driven approach to experimentation, evaluation, and technical decision-making.
Excellent collaboration, communication, and stakeholder management skills, with the ability to influence across engineering, product, data science, and business teams.
A proactive, curious, and resilient mindset, with demonstrated ability to navigate ambiguity, explore new technologies, and continuously improve systems and ways of working.
Experience leading, mentoring, and developing software engineers while maintaining hands-on technical credibility.

Nice to Haves

Experience working with financial market data, investment analytics, or private markets datasets.
Experience designing and building software and/or data platforms in financial services or other regulated environments.
Experience productionising statistical or quantitative models.
Experience supporting AI/ML lifecycle management, including model versioning, experiment tracking, evaluation, serving, and deployment pipelines.

Architect, build, and own scalable software solutions end-to-end, translating business objectives into technical designs through implementation, deployment, and production support.
Design and implement reproducible, testable data workflows enabling advanced analytics and AI/ML use cases, incorporating validation, monitoring, observability, experimentation, versioning, and production deployment capabilities.
Lead architectural decisions and influence technical prioritisation in close partnership with product and delivery teams to align engineering efforts with business impact.
Act as a technical authority within the wider team, elevating engineering standards and driving best practices in data management, governance, and cloud-native development.
Engage senior stakeholders, clearly communicating complex technical trade-offs and recommendations in business-relevant terms.
Collaborate cross-functionally with engineers, data scientists, analysts, and product leaders to deliver high-impact solutions for institutional investors and private markets clients.
Lead, coach, and develop software engineers, combining hands-on technical contribution with people management to foster a high performing, inclusive, and continuously improving engineering culture.

Retirement investment and financial planning tools to help build a sound financial future.
Access to education reimbursement programs.
Comprehensive resources to support physical health and emotional well-being.
Family support programs.
Flexible Time Off (FTO) to relax, recharge, and be there for family and loved ones.
Hybrid work model enabling collaboration and flexibility with 4 days in office and 1 day remote work per week.