Data Engineer – Financial Crime Data Pipelines & Analytics

Westpac Bank; Takutai; On-site; NZD 100,000 - 140,000; Full time

Benefits offered by this job

Wellbeing leave

Banking benefits

Internal mobility

Recognition program

Volunteer days

Job summary

Westpac is seeking a Data Engineer to ensure the quality, integrity, availability, and reliability of data that underpins financial crime platforms such as NetReveal. You will design, develop, support and improve data pipelines, ETL processes, and data assets to enable effective monitoring, risk assessment, and regulatory reporting.

You will collaborate with AML Analytics, business stakeholders, engineers and vendors to deliver robust data solutions in a complex enterprise environment, including

Qualifications

• Experience with data modelling and ETL/ELT development.
• Knowledge of batch processing and data quality management.
• Experience with data reconciliation and root cause analysis.
• Exposure to incident management and production support.
• Experience working in financial services or complex enterprise data environments.

Responsibilities

• Design, develop, support and improve data pipelines, ETL processes, integrations and data assets.
• Ensure quality, integrity, availability and reliability of data for financial crime platforms.
• Collaborate with AML Analytics, Engineers, Analysts and Vendors to enable data-driven decisions.

Skills

Strong SQL

Data modelling

ETL/ELT development

Batch processing

Data quality management

Data reconciliation

Incident management

Financial services experience

Tools

Oracle PL/SQL

Python

Kafka

Snowflake

Control-M

CI/CD practices

Job description

Westpac is seeking a Data Engineer to ensure the quality, integrity, availability, and reliability of data that underpins financial crime platforms such as NetReveal. You will design, develop, support and improve data pipelines, ETL processes, and data assets to enable effective monitoring, risk assessment, and regulatory reporting.

You will collaborate with AML Analytics, business stakeholders, engineers and vendors to deliver robust data solutions in a complex enterprise environment, including