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Design, implement, and optimise data pipelines to support end-to-end capital-markets AML detection scenarios.
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Implement detection logic, rule frameworks, and scoring components to support financial-crime use cases.
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Ensure high-quality engineering across code, data pipelines, and operational processes.
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Contribute to a greenfield architecture with a strong sense of ownership, innovation, and delivery excellence.
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Strong programming skills in Python and Java.
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Advanced experience with Apache Spark (PySpark), SQL for large-scale distributed data processing.
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Hands-on experience in distributed compute platforms and on-premise cluster environments.
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Proficiency with CI/CD tools such as Bamboo/Jenkins, and Bitbucket.
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Experience working in Agile teams, with the ability to work independently in fast-paced, evolving environments.
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Strong analytical and problem-solving skills with the ability to adapt quickly.
Nice to Have
- Understanding of Capital Markets products including FX, Equities, Derivatives, and Bonds etc.
- Previous experience in Financial Crime, AML, or surveillance use cases.
- Experience implementing rule-based detection systems, scorecards, or alerting frameworks.
- Experience delivering against NFRs such as performance, scalability, observability, data quality, resiliency, and security.
- Experience with automation, including automated pipeline deployment, data validation, testing frameworks, and workflow orchestration.
- Experience in integrating the solutions with the third party systems (like case management systems).
- A strong “can-do” attitude and the ability to collaborate effectively in a cross-functional team.
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