About the Role
This is an opportunity for a technically strong early-career professional to join a specialist Investment Operations team supporting ETF products, with a focus on data, automation, and scalable operational processes.
You will play a key role in both running and improving ETF operations—owning critical processes such as trade lifecycle management and reconciliations, while also building automation solutions that improve efficiency, reduce risk, and enhance data integrity. The role sits at the intersection of operations, technology, and markets, offering hands-on exposure to trading workflows, fund operations, and financial systems.
Working closely with operations, technology, and investment teams, you will design and implement data-driven solutions, leverage modern tools (including Python, SQL, APIs, and AI-assisted workflows), and contribute to a more scalable and technology-enabled operating model.
Role Responsibilities
- Own and manage key components of the ETF trade lifecycle, including trade capture, settlement, corporate actions, and reporting.
- Perform and enhance daily reconciliations (cash, holdings, transactions), including building automated solutions to reduce manual processes.
- Apply AI-enabled tools to streamline workflows, enhance data validation, and support decision-making processes.
- Work with internal and external stakeholders to resolve operational issues and improve workflows.
- Contribute to ETF creations/redemptions and fund flow processes, ensuring accuracy and timeliness.
- Build and maintain data validation, monitoring, and reporting frameworks.
- Partner with technology teams to support API integrations and system enhancements.
- Contribute to operational and technology projects, including new product launches and platform improvements.
Prerequisites and Key Skills
- Bachelor’s degree in a quantitative discipline (e.g. Finance, Economics, Computer Science, Engineering, Data Science).
- High-performing candidate with 1–3 years’ experience in investment operations, fund administration, financial technology, or data-driven financial services environments.
- Proficiency in AI tools, Python, SQL, or similar technologies, with hands-on experience in automation, data engineering, or advanced analytics.
- Demonstrated track record of improving processes through automation, scripting, or data-driven solutions, with a focus on scalability and efficiency.
- Exposure to AI and machine learning applications in finance—including process automation, predictive analytics, anomaly detection, natural language processing, or data pipeline optimisation is highly regarded.
- Strong analytical and problem-solving capability, with high attention to detail and a consistent focus on data integrity and accuracy.
- Solid understanding of financial markets, ETFs, trading strategies, and operational workflows, including market structure fundamentals.
- Experience with data manipulation, API integrations, automation frameworks, or financial modelling.
- Strong communication skills, with the ability to translate technical concepts into clear, actionable insights for both technical and non-technical stakeholders.
- Demonstrated initiative, intellectual curiosity, and a growth mindset, with a clear interest in leveraging AI and technology to enhance operational performance and scalability.
- Ability to operate effectively in a fast-paced environment while maintaining strong control standards.
