Senior QA Analyst - AML & FinCrime

Support the development, implementation, maintenance and enhancement of Financial Crime Risk detection systems and data environment.

Contribute to the development, review and update of process and system documentation.

Contribute to the design of technical data mapping from source to target including transformation rules.

Contribute to data governance processes and procedures, including business rule definition and documentation.

Produce regular data analytics deliverables in relation to Financial Crime Risk, including exploratory data analytics, and end-of-month management information reporting.

Assist with financial crime projects and implementation of operational changes following regulatory change.

Collaborate with cross-functional teams to investigate financial crime prevention methods.

Assist with related Financial Crime Operations and/or other business projects with a Financial Crime Risk impact, as required.

Liaise with both operational and technology functions across the BOQ Group where required to complete inquiries.

Adhere to regulatory obligations and internal policies/procedures.

Keep abreast of new technologies in relation to data analytics and emerging AML/CTF trends.

We are seeking a passionate individual with a keen sense of curiosity and a proactive approach to problem solving. As the successful candidate for this role, we would like to see a foundation in data analytics and an interest in financial crime. The following attributes will help you succeed in this role:

8+ years of experience overall and 4+ years of experience in AML and Fincrime testing

Bachelor's or Master's degree in Data Science, Finance, Computer Science, or a related field.

Proficiency in programming languages and data analytics tools (SQL, R, Python, Power BI), proficiency in SQL skills is preferred.

Excellent communication skills, both verbal and written.

Ability to work independently or in a team environment. The following attributes are highly desirable:

Additional training/qualifications in Data Science and Financial Crime Risk e.g. Association of Certified Anti-Money Laundering Specialists (ACAMS).

Experience working with Financial Crime systems

detection/transaction monitoring, screening and case management.

Experience in overseeing and implementing AML/CTF or Sanctions systems and controls such as transaction monitoring and sanctions screening processes.

Experience in data infrastructure design and development.

Proficiency in data analytics tools e.g. python, R.

Experience in Databricks.

Knowledge of machine learning techniques.

Australian Banking experience is preferred.

Lending, Test Automation

Manual Testing

BCM Industry

21/04/2025

Req. VR-113511