Data Asset Excellence Expert

1 week ago

Sydney, New South Wales, Australia beBeeDataQuality Full time $85,000 - $95,000
Role Overview

The position of Data Quality Specialist is pivotal in enhancing the accuracy, consistency, and reliability of customer data assets. This role focuses on address standardization and broader data cleansing and validation using Informatica Data Quality (IDQ) tools.

Key Responsibilities include:

  • Utilize Informatica Data Quality (IDQ) for profiling, cleansing, monitoring, and rule-based transformation activities
  • Implement address cleansing solutions and design reusable data quality components
  • Conduct data profiling and assessments to identify and resolve anomalies
  • Collaborate with data governance teams and business stakeholders to define and enforce data quality standards
  • Recommend improvements and automation opportunities for data quality processes
  • Monitor data quality metrics and produce dashboards and reports
  • Ensure alignment between business requirements and technical data quality solutions

Qualifications:

  • 5+ years of experience in enterprise data quality initiatives
  • Proven expertise in Informatica Data Quality (IDQ), including rule creation and monitoring
  • Strong SQL skills and experience working with large relational datasets
  • Preferred experience with Oracle Enterprise Data Quality (OEDQ), especially in address cleansing and standardization
  • Solid understanding of data governance, MDM, and data quality frameworks
  • Strong analytical and communication skills, with the ability to translate business needs into technical solutions
  • Experience in industries where address and data quality are critical (e.g. finance, logistics, insurance, healthcare)
  • Familiarity with agile environments and tools such as JIRA and Confluence
  • Knowledge of data quality dashboards and reporting tools such as Power BI

This is a 12-month contract position, and the successful candidate will work closely with stakeholders to improve data quality and integrity across large datasets.

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