1.1.1 Overview:
We are seeking a Data Quality Engineer to help ensure the quality, reliability, and performance Health Data products to provide ready-to-use, easily consumable, trusted data set to support the AI and analytics use cases required to drive Manulife’s Health business ambition. This role is critical in enabling business units to innovate faster, optimize operations, and deliver localized analytics and AI capabilities. The successful candidate will participate in regional health strategic initiatives such as Provider Management Solutions, Health KPIs and Claim Driver analyses/automation, and other Regional/Local Health Analytics/AI use cases that require a common health data model.
As part of ADO, you will have the opportunity to:
Contribute to regional health data initiatives impacting regional and local health businesses
Collaborate with data scientists, AI teams, and business leaders in a dynamic, forward-thinking environment.
Drive innovation and shape the future of health data products within the organization, to enable cutting-edge analytics/AI use cases including Generative AI (GenAI) , Large Language Models (LLMs) , Agentic AI , and advanced solutions leveraging OpenAI GPT models and AliCloud Qwen models .
1.1.2 Key Responsibilities:
1.1.2.1 Quality Engineering
· Support the QA strategy and execution plan to ensure the accuracy, completeness, consistency, timeliness, and reliability of health data products relating to provider networks, claims efficiency, and care quality within our health insurance businesses and provide insights to support contract/negotiation strategy, network optimization, medical cost management, and fraud, waste, abuse identifications
Collaborate with cross-functional teams including data engineers, data analysts, product owners, and business stakeholders to resolve quality issues.
Conduct peer reviews of code and deliverables to promote continuous learning.
Control, update, and distribute new and revised quality standards and technical changes.
Ensure compliance with internal and external quality standards and company SDLC standards.
Stay current with frontend and backend technologies and trends.
Continuously improve skills to address diverse application challenges, including cloud-based development.
1.1.2.2 Testing Execution & Automation · Develop, execute, and maintain manual and automated test cases for data pipelines, APIs, reports, dashboards, and analytical datasets.
· Validate source-to-target mappings, data transformations, business rules, calculations, and reconciliations to ensure data accuracy and consistency.
· Build and enhance automated testing frameworks and scripts to support CI/CD and continuous testing practices.
· Prepare and manage test data, analyze test results, investigate defects, and partner with delivery teams to resolve issues.
· Perform integration, regression, end-to-end, performance, and data quality testing to ensure reliable delivery of data products and analytics solutions.
1.1.2.3 Quality Governance & Process Improvement · Define, monitor, and report key quality metrics, including data accuracy, completeness, defect trends, and test coverage.
· Support quality audits, governance reviews, and compliance activities to ensure adherence to established standards and controls.
· Develop quality dashboards and reporting to provide visibility into data quality performance and testing effectiveness.
· Drive adoption of quality-by-design principles, testing best practices, and automation across the data and AI delivery lifecycle.
· Identify opportunities to improve testing efficiency, data quality, and delivery outcomes through continuous process improvement.
1.1.3 Qualifications & Skills:
Education: Bachelor’s or Master’s degree preferably in Statistics, Mathematics, Computer Science, Business Analysis or Data Analytics related courses
Experience: 3-5 years in…