Lead Data Quality Engineer

Experience Range - 8 to 10 years

We are seeking an experienced and meticulous Lead Data Quality Assurance Engineer to join our growing data team. The ideal candidate will be a hands-on leader responsible for ensuring the accuracy, integrity, and reliability of the enterprise data. This role requires a deep expertise in data quality methodologies and extensive experience with data quality tools, particularly Informatica Data Quality (IDQ). The successful candidate will also have a working knowledge of Ataccama Data Quality and will be responsible for leading a team of data QA analysts, designing and implementing data quality frameworks, and driving a culture of data excellence across the organisation.

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  Lead and Mentor: Guide and mentor a team of Data QA analysts, providing technical leadership, assigning tasks, and fostering their professional development.



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  Strategy and Framework: Design, develop, and implement a comprehensive data quality assurance strategy and framework to ensure data accuracy and consistency across all data systems.



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  Tool Expertise: Serve as the subject matter expert for Informatica Data Quality (IDQ), developing and implementing complex data quality rules and scorecards. Utilize Ataccama for secondary data quality initiatives and analysis.



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  Data Profiling and Analysis: Perform in-depth data profiling and analysis to identify data quality issues, anomalies, and inconsistencies.



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  Testing and Validation: Develop and execute detailed test plans, test cases, and test scripts to validate data transformations and data quality rules.



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  Root Cause Analysis: Investigate data quality problems to determine the root cause and work with data stewards and engineering teams to implement corrective and preventive actions.



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  Monitoring and Reporting: Establish and monitor key data quality metrics and create dashboards and reports to communicate the state of data quality to stakeholders.



• Collaboration: Work closely with data engineers, data scientists, business analysts, and other stakeholders to understand data requirements and ensure data quality standards are met throughout the data lifecycle.
  

  Experience with IceDQ would be a significant advantage